Categories   >   Data Integration Tools
Last Reviewed: November 22nd, 2024

Best Data Integration Tools Of 2024

What are Data Integration Tools?

Data integration tools are like bridge builders for your data ecosystem. Imagine a company with customer data scattered across salesforce, marketing automation, and web analytics platforms. These tools connect the dots, allowing seamless data flow between various systems. This solves the problem of data silos, fragmented information hindering a holistic view of the customer journey. Their importance is undeniable. By unifying data, businesses gain a 360-degree view of customers, operations, and market trends. This empowers data-driven decisions across departments. Key functionalities include data extraction, transformation, and loading (ETL) processes, along with real-time data synchronization. Emerging features leverage APIs for even smoother data exchange and cloud-based solutions for scalability. Data integration benefits a wide range of industries, from retail personalizing marketing campaigns to healthcare optimizing patient care coordination. Limitations include ensuring data quality across different sources and managing complex data structures. Nevertheless, data integration tools are the backbone of a unified data landscape, unlocking the power of combined information for smarter decision-making.

What Are The Key Benefits of Data Integration Tools?

  • Unified Data View
  • Improved Data Quality
  • Break Down Silos
  • 360° Customer View
  • Better Decision Making
  • Streamlined Workflows
  • Enhanced Analytics
  • Simplified Reporting
  • Increased Efficiency
Read more

Overall

Based on the latest available data collected by SelectHub for 35 solutions, we determined the following solutions are the best Data Integration Tools overall:

IFTTT
IFTTT
Start Price
$20,000
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Key Features

  • Enhanced Accessibility: Seamlessly access hundreds of features and triggers. Choose from over 600 apps and devices and avail of new service updates every week. Create rudimentary apps by using the open API. Access a suite of marketing and brand tools to position and promote applets and services. 
  • Automation: Increase work efficiency on personal and professional fronts. Enjoy the smart home experience, and easily connect services and create new tasks even without coding knowledge. Bridge the gap between available smart home devices and experience automation. 
  • Optimized Integration: Synchronize various products of a brand with different apps. Enable integration with any service in the software ecosystem with the click of a button. Create an applet and partner with different brands. Connect products with third-party apps on Windows, Google, Android and iOS. 
  • Support: Seek technical support to receive helpful answers from skilled support and account management teams. 
  • Applets: Pick from a repository of applets and services. Customize applets or create one from scratch. Use multi-step queries and filter codes to specify how and when applets run with a pro subscription. 
  • Compatible: Use on any device, including laptops, phones, tablets and more. 
  • Personalization: Gain useful insights regarding product usages and how people connect to them to facilitate personalized customer experiences. 
  • Consistency: Post content or information simultaneously on various platforms, saving time and effort. 
Domo
Domo
Start Price
$15
Per User, Monthly
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Why We Picked Domo

Domo has everything data teams could wish for — self-service ETL, advanced analytics and data science. Its rich set of connectors makes users happy as they praise its robust integration APIs. Its scripting language is similar to Power BI, and knowing SQL will shorten your team's learning curve. The vendor offers daily refreshes, currently capping them at 48.

On the flip side, the interface seemed a bit clunky to me. Dashboards don’t display in the edit mode by default, which was a tad annoying. The Getting Started documentation is dated and doesn’t match the new interface. I could find my way around with help from user forums.

While the vendor earns praise for releasing frequent updates, quite a few users say some much-needed features lack depth. According to our research, Domo offers only 64% of the required functionality out of the box, which is much less than what Power BI and Tableau provide. It also underperforms in data querying, scoring only 53 in our analysis.

Some reviews mention bugs and that performance can lag when handling anything complex than simple visualizations. The slowness could be due to the multitenant SaaS model that provides shared computing. As for the mobile app, it didn’t work in the offline mode for me. I should mention here that I had opted for the trial version. A proof-of-concept will help you check if the issue persists in the paid edition.

Domo’s pay-as-you-go model is great for estimating usage but be prepared to pay more for workload spikes. According to our research, about 89% of users who reviewed the price found Domo’s consumption model expensive. Small organizations working with a lean team might find it challenging to handle billing.

Here’s what’s great about subscribing to Domo. You can create as many reports and dashboards as required — there’s no limit or additional cost. Plus, Domo allows adding an unlimited number of users. Domo accepts external data models from OpenAI, Amazon Bedrock, Hugging Face, Databricks and Jupyter Workspaces.

Despite a competitive market, Domo is an excellent product for organizations seeking data visualization and strong integration. Its flexible pricing model and recent AI updates make it a strong challenger to leading data platforms.

Pros & Cons

  • Source Connectivity: About 86% of users citing data integration said they could connect to their preferred sources easily.
  • Ease of Use: Around 82% of users discussing the interface said options and tabs were straightforward and intuitive.
  • Data Visualization: About 74% of people who reviewed Domo for graphics appreciated the ease of creating and sharing data stories.
  • Functionality: Around 73% of users who mentioned features said Domo performed as expected.
  • Support Services: About 71% of reviews discussing assistance praised the support team for being helpful and responsive.
  • Speed: About 78% of users discussing speed said the platform lagged sometimes.
  • Cost: Around 89% of users discussing price termed the platform as expensive.

Key Features

  • Domo App Studio: Design custom apps for needs Domo might not address out of the box. Build your own functionality without coding knowledge. Create branded visualizations with your trademark colors, logos and fonts. ESPN enhances the fan experience by capturing and analyzing customer data using a Domo Studio app.
  • Analyzer: Save time spent cleaning data manually. Use a special editor to set up checks for data inputs. Keep tabs on which charts and dataflows use a specific dataset with the lineage option. You can choose the best chart to present your data and annotate it. Use the Beast Mode for complex math.
  • DomoGPT: Get answers to data queries using AI Chat (currently in beta). Convert text to SQL or calculations and understand your data using text summaries. Use Domo.AI in a safe, governed space.
  • Personalized Data Permissions: Create custom data views for your users and hide sensitive data. Your regional managers get exclusive views specific to their roles, while senior management can switch between full and filtered views.
  • Domo Mobile: View cards and text summaries on the mobile app. Cards fit within the small screen, giving a great user experience. Domo Buzz allows sharing files to WhatsApp, Gmail, QuickShare and Google Drive. You can even save a screenshot to your phone gallery.
  • Alerts: Stay informed about KPIs that matter to you. Set new alerts and share them with selected users or subscribe to existing ones. Choose where you want to receive the notifications — email, mobile app or SMS.
Mathematica
Mathematica
Start Price
$1,800
Annually
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Why We Picked Mathematica

Let's crunch some numbers and see what users have to say about Mathematica!

Mathematica has garnered a reputation as a powerful computational tool, particularly in academic and research settings. Users frequently praise its symbolic computation capabilities, allowing them to manipulate and solve complex mathematical expressions and equations with ease. This strength sets Mathematica apart from competitors like MATLAB, which primarily focuses on numerical computation. Mathematica's notebook interface also receives positive feedback for its ability to combine code, visualizations, and text in a single document, facilitating reproducible research and clear communication of findings. However, Mathematica's steep learning curve and high price point are often cited as drawbacks. Users transitioning from other programming languages may find Mathematica's syntax and functional programming paradigm challenging to grasp initially. Additionally, the cost of a Mathematica license can be prohibitive for individual users or small businesses.

Overall, Mathematica is best suited for researchers, scientists, and engineers who require a comprehensive tool for symbolic and numerical computation, data analysis, and visualization. Its extensive functionality and ability to handle complex mathematical problems make it an invaluable asset in these fields. However, individuals or organizations with limited budgets or those seeking a more user-friendly option may want to explore alternative software solutions. Keep in mind that software is constantly evolving, so it's always a good idea to check for the latest updates and user reviews to make an informed decision.

Pros & Cons

  • Symbolic Computation: Mathematica excels at handling and manipulating symbolic expressions, making it ideal for tasks that involve algebra, calculus, and other forms of mathematical analysis. This can be particularly useful for financial modeling, risk analysis, and other business intelligence applications that require complex calculations.
  • Visualization Capabilities: Mathematica offers a wide range of visualization tools that can be used to create high-quality charts, graphs, and other visual representations of data. These visualizations can be interactive, allowing users to explore data from different perspectives and gain deeper insights. This is essential for effectively communicating complex data to stakeholders in a business setting.
  • Automation and Scripting: Mathematica allows users to automate tasks and create scripts, which can save time and improve efficiency. This can be particularly useful for repetitive tasks, such as data cleaning and analysis. Automating these tasks can free up time for business intelligence professionals to focus on more strategic initiatives.
  • Machine Learning and AI: Mathematica includes a wide range of machine learning and artificial intelligence (AI) tools that can be used for tasks such as predictive modeling, classification, and anomaly detection. These capabilities are becoming increasingly important for business intelligence, as they can help organizations to identify trends, make better decisions, and gain a competitive advantage.
  • Price: Mathematica comes with a hefty price tag, especially for commercial use, which can be a significant barrier for individuals or small businesses.
  • Learning Curve: The software has a steep learning curve due to its vast functionality and unique syntax, requiring a significant time investment to master.
  • Closed Ecosystem: Mathematica operates within a closed ecosystem, making it challenging to integrate with other data analysis tools or programming languages commonly used in business intelligence.
  • Limited Collaboration: Collaboration features are not as robust as those found in other business intelligence platforms, hindering teamwork and knowledge sharing.
  • Visualization Capabilities: While Mathematica offers visualization tools, they may not be as intuitive or user-friendly as dedicated data visualization software, potentially limiting the ability to create compelling and insightful dashboards.

Key Features

  • Wolfram Language: Wolfram’s proprietary computational language allows developers to code with a language that allows both computers and humans to communicate with each other through almost 6,000 built-in functions. Built on a philosophy of knowledge-based programming, it aims to help users automate as much as possible and maximize coherence of design while being universally deployable in any environment.
  • Connect to Everything: Through symbolic expressions, interactions and external connections, the Wolfram Language conveniently connects to a broad spectrum of platforms, languages, databases, protocols, APIs, applications, file formats and devices.
  • Notebook Interface: With structured documents that store text, runnable code, dynamic graphics and more, Wolfram Notebooks provide an environment for technical workflows that supports interactive computation. They empower user literacy in a high-level programming interface through interactive coding, natural language queries and expansive documentation that make the platform accessible to users without coding experience.
  • AlgorithmBase: Not just through industrial-strength algorithms but also meta-algorithms and super functions, which automatically select the optimal algorithms to use in a given situation, users can define their goals or concepts and let the system take over to automatically achieve them, enabling discoveries and experimentation with algorithms. With its robust library of scalable and accurate algorithms, the AlgorithmBase serves as a trustworthy resource for programmers to use to ensure high-quality computations.
  • Data Visualization: Through algorithms, Mathematica can create visually compelling representations of data in the form of 2D and 3D plots, graphs, histograms, word clouds, geographic visualizations and more.
  • Machine Learning: Through highly automated functions that work on many types of data, the platform can carry out a wide range of tasks, including classifying data in categories, predicting values, learning from examples and performing automated time series analysis. 
  • Mathematica Online: Powered by the Wolfram Cloud, users can harness the computational system from directly within their web browsers, with no installation required. Everything automatically saves and stays in the cloud, and users can control who can access their documents through instant sharing, URL links and permissions controls. Seamlessly integrated with the desktop version, it allows users to upload or download notebooks and access the cloud from a computer.
  • Wolfram Knowledgebase: Mathematica and the Wolfram Language has access to the world’s largest and broadest trusted source of computable knowledge, curated by experts and derived from primary sources, including not just the data but also the methods that compute results.
  • Mobile App: The Wolfram Cloud free app for iOS and Android mobile devices allows users to edit, run and deploy programs and access Wolfram notebooks and instant apps through its home-screen-like experience.
Stata
Stata
Start Price
$48
Annual, Quote-based
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Key Features

  • Easy Import and Export of Data: Users can import and export data from a myriad of formats, including XLS, CSV, spreadsheets, SQL sources, ASCII files, text, etc. Stata can also import files from SAS or SPSS, ensuring that it has compatibility with other popular statistical software.
  • Comprehensive Statistical Analysis: Stata can provide users all the statistics functions they need to perform data science, including but not limited to linear models, panel/longitudinal data, time series analysis, survival analysis, Bayesian analysis, selection models, choice models, extended regression models, generalized linear models, finite mixture models, spatial autoregressive models, nonlinear regression and more.
  • Predictive Analysis: Stata helps users anticipate the future. It has lasso tools that allow users to predict outcomes, characterize groups and patterns and perform inferential statistics on data.
  • Data Visualization: Users can create customizable bar charts, box plots, histograms, spike plots, pie charts, scatterplots, dot charts and more. With a graph editor, users can customize how their visualizations look, by adding, moving, modifying or removing elements, with the option to record changes and apply those edits to other graphs. Users can also write scripts to produce graphs en masse in a reproducible manner. Graphics can be exported to a variety of formats: EPS or TIFF for publication, PNG or SVG for online distribution, or PDF for viewing and sending. 
  • Task Automation: Either with built-in procedures or self-written scripts, users can automate common tasks such as creating certain kinds of variables, producing specific tables, or performing a sequence of analyses. Users can then extend the benefits to their colleagues or all Stata users by turning their automation scripts into Stata commands that can be used in the same way as official Stata commands.
  • Advanced Programming: In addition to the fast and complete Stata programming languages ado and Mata, users can also incorporate C, C++ and Java plug-ins via a native API. Stata also has comprehensive Python integration, so users can embed and execute coding directly within the program.
  • Automated Reporting: Users can automate reports, which can be created in Word, Excel, PDF and HTML files directly from the solution. The look of the reports can be customized using Markdown text-formatting language.
  • Integrated Versioning: With built-in backwards compatibility, Stata will always be able to run scripts and programs written years and years ago in modern versions of its platform, generating the same results.
  • Language Support: Stata’s interface, including all menus and all dialogs, is available in multiple languages and will automatically match the user’s computer language settings. Languages supported include English, Chinese, German, Hindi, Japanese, Spanish, French, Russian, Italian, Dutch, Polish, Turkish, Swedish and Korean. 
Redshift
Redshift
Start Price
$0
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Key Features

  • Massively Parallel Processing: Parallelize and distribute SQL (Structured Query Language) operations. Use local attached storage to maximize throughput between CPUs and drives.
  • End-To-End Encryption: Utilize SSL encryption to secure data in transit and AES-256 encryption for data at rest. Encrypt all data written to disk and backup files. Migrate data between encrypted and unencrypted clusters and make use of highly customizable and robust data security options. 
  • Cost-Effective: Enjoy on-demand pricing with no upfront costs, no commitments and no hidden charges. Pay only for the resources used. Use the pause and resume feature to suspend on-demand billing when the cluster isn’t going and pay only for backup storage.
  • Scalable: Change the number or type of nodes with a few clicks from the console or an API call. Share live data across clusters, improving the agility of the company. Use S3 as a highly available, secure and cost-effective data lake, storing unlimited data in open formats.
  • Advanced Query Accelerator: Enable the platform to run ten times faster by automatically boosting certain types of queries.
  • Currency Scaling: Support thousands of users and queries simultaneously, enabling superior performance. Process queries without delays and assign clusters to particular groups and workloads while controlling the number.
  • Data Sharing: Enable instant, granular and high-performance data access across clusters without moving or copying data. See information as it’s updated in the data warehouse via live access, at no additional cost. Provide a secure and organized collaboration within and across companies.
  • Concurrency Limits: Attain flexibility while maintaining concurrency limits similar to other data warehouses. Configure limits based on regions instead of applying a single limit to all users.
  • Fault Tolerance: Enhance the reliability of the data warehouse cluster — automatically re-replicate data and shift data to healthy nodes on the failure of drives, nodes and clusters.
  • Column-Oriented Databases: Allow quick access to large data amounts and accomplish massive data processing tasks in an instant.
  • Automatic Workload Management: Easily segregate a workload and assign a part of cluster resources to every workload.
Fivetran
Fivetran
Start Price
$55
Monthly
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Why We Picked Fivetran

Users praise Fivetran for its ease of use and effortless data integration. "Setting up connectors is straightforward," one reviewer comments, "like plugging in appliances." This plug-and-play simplicity sets it apart from competitors like Stitch, often lauded for its flexibility but criticized for a steeper learning curve. However, Fivetran's strength in pre-built connectors comes at a cost: limited customizability. While users love its "seamless data movement," another user points out it's "not ideal for complex transformations," requiring additional tools that negate its initial ease. This lack of advanced ETL capabilities puts it behind platforms like Informatica PowerCenter, but at a fraction of the cost. Ultimately, Fivetran shines for its user-friendly approach and reliable data pipelines, perfect for businesses prioritizing simplicity and scalability. But for complex data manipulation or real-time needs, users might find themselves yearning for the power and flexibility of other ETL solutions.

Pros & Cons

  • Effortless Data Integration: Connects to hundreds of data sources with pre-built connectors and minimal setup.
  • Automated Data Pipelines: Schedules and runs data transfers reliably, freeing up time for analysis.
  • Centralized Data Management: Provides a single source of truth for all your data, simplifying reporting and decision-making.
  • Scalable for Growth: Handles large data volumes with ease, adapting to your evolving needs.
  • Improved Data Visibility: Makes data readily available for everyone in your organization, fostering data-driven decision-making.
  • Limited Customizability: Relies on pre-built connectors, making complex data pipelines or transformations challenging.
  • Costly for Advanced Needs: Pricing scales with data volume and complexity, becoming expensive for intricate ETL processes.
  • Batch-Oriented Transfers: Focuses on scheduled data refreshes, not ideal for real-time needs or low-latency pipelines.
  • Basic Data Transformations: Offers limited built-in transformations, requiring additional tools for complex data manipulation.
  • Advanced Feature Learning Curve: Mastering custom connectors, scripting, or other advanced features requires technical expertise.

Key Features

  • Data Connectivity: The solution has data connectors for 100 sources, with the option for users to create their own data connector to APIs not yet natively supported. It directly pulls information from cloud applications. Users can also upload or email files into a cloud storage service and have that data loaded into their warehouse. The system reflects changes made to live files such as Google Sheets.
  • Extract Data: The solution connects natively to over 100 SaaS sources, automatically extracting information from those sources after an admin has granted the tool access through OAuth. The system normalizes, cleanses and standardizes data before loading.
  • Data Sync: Upon connection, the system performs a historical sync. From there, instead of arduously reloading full data dumps from APIs and databases, the solution optimizes loadings by incrementally updating data sources in batches, with data load time configurable as frequently as five minutes and as infrequent as every 24 hours.
  • Load into Cloud Data Warehouses: Fivetran supports modem cloud warehouses like BigQuery, Snowflake, Azure and Redshift.
  • Transform Data: The platform preps data, normalizing schemas from APIs, so that it can be analyzed instantly. Transformations always happen in the warehouse so that the raw data is always available alongside the transformed data; data will never be lost and the transformations can be edited and run again on the raw data.
  • Alerts: The system notifies users if there are delays or issues in any step of the process.
  • Dashboards: Users interact with the platform via dashboards that display information about ELT processes in a visual, easy-to-digest format.
  • System Logs: The solution maintains transparency with granular system logs of every sync that users can cross-reference in their own logging system.
  • Metadata Management: A suite of policies and procedures allows users to manage the data which describes other data such as file size, date of data creation, tags, titles, authors, etc.
SQL Server Integration Services
SQL Server Integration Services
Start Price
$300
Monthly
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Why We Picked SQL Server Integration Services

User reviews of SQL Server Integration Services paint a contrasting picture. Proponents praise its intuitive visual workflow, robust data transformation capabilities, and seamless integration with the Microsoft ecosystem. This makes it ideal for organizations already invested in Microsoft tools and requiring efficient data movement within Windows environments. The built-in security features and scalability for handling large datasets are further pluses, offering peace of mind and ensuring smooth performance for growing data volumes. However, critics point to its heavy reliance on Microsoft technologies and limited open-source compatibility as major drawbacks. This can restrict customization and community support compared to more open platforms like Talend or Apache Airflow. The steep learning curve and Windows-only limitation can also be hurdles, requiring dedicated training and potentially hindering platform flexibility. Additionally, the closed-source nature can make troubleshooting complex issues challenging. Finally, pricing tied to SQL Server editions may not be cost-effective for organizations needing only basic data integration functionalities or using other database solutions. Ultimately, SQL Server Integration Services shines for its robust data handling, intuitiveness, and Microsoft integration within Windows environments. However, its limited open-source compatibility, steep learning curve, and reliance on SQL Server licensing make it less ideal for organizations seeking greater flexibility, affordability, or platform independence. Carefully weighing your specific needs and resources against its strengths and limitations is crucial before choosing SSIS for your data integration needs.

Pros & Cons

  • Visual Workflow: Drag-and-drop interface simplifies complex data flows, making integration tasks intuitive and manageable, even for users without extensive coding experience.
  • Robust Data Transformations: Cleanses, transforms, and validates data to ensure accuracy and consistency before integration, improving data quality and trust in downstream analytics.
  • Microsoft Integration: Seamlessly integrates with other Microsoft tools and platforms like SQL Server and Azure, streamlining data workflows within existing infrastructure and reducing the need for additional software.
  • Scalability and Performance: Handles large datasets efficiently with parallel processing and optimization techniques, minimizing processing time and ensuring smooth data integration for growing data volumes.
  • Built-in Security: Supports encryption, data masking, and role-based access controls for secure data handling and compliance with industry regulations, providing peace of mind and reducing security risks.
  • Limited Open Source: Relies heavily on Microsoft technologies and lacks extensive open-source integrations, potentially restricting customization and community support compared to more open platforms.
  • Steep Learning Curve: While the visual interface is helpful, mastering complex data flows and transformations can require significant training and experience, especially for users unfamiliar with the platform.
  • Windows Only: Limited to Windows environments, excluding non-Microsoft operating systems like Linux or macOS, hindering platform flexibility and potentially requiring additional infrastructure investment.
  • Closed-Source Ecosystem: Limited transparency into internal algorithms and processes can make troubleshooting and debugging complex issues challenging, requiring specialized knowledge or relying on Microsoft support.
  • Cost Tied to SQL Server: Pricing depends on the chosen SQL Server edition, potentially increasing costs for organizations already invested in other database solutions or needing only basic data integration functionalities.

Key Features

  • Big Data Support: Connects to new, in-demand big data sources like databases, systems, files and unstructured content. Access mainframe sources and captured data changes in real time. Expand cloud support for big data with Microsoft Azure, Impala, Cassandra, OData and Apache Hive.
  • Import/Export Wizard: Move data from a variety of source types to disparate destination types, including text files and other SQL Server instances. Create packages that move data across systems seamlessly, without transformations. 
  • Build Integration Packages: Create and maintain integration packages through the SSIS Designer. Deploy the package and view the execution status at run time. Add functionality to packages through dialog boxes and windows. Configure the development environment through SQL Server Data Tools (SSDT). 
  • Built-in Data Transformations: Provides aggregation, pivot, unpivot, cache transform, fuzzy lookup, data conversion, data mining query and partition processing. Leverage a wide range of transform capabilities like fuzzy logic, data profiling, data and text mining and direct insert to SSAS. 
  • Secure Business Data: Provides threat and vulnerability mitigation, and access control. Sign packages with digital certificates that ensure customers open and run packages only from trusted sources. 
  • Precedence Constraints: Control task runs by defining precedence constraints. Connect tasks to control the workflow and configure to work based on an SSIS expression or the status of the preceding job. 
AWS Glue
AWS Glue
Start Price
$0.44
Per M-DPU-Hour
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Why We Picked AWS Glue

User reviews of AWS Glue paint a picture of a powerful and user-friendly ETL tool for the cloud, but one with limitations. Praise often centers around its intuitive visual interface, making complex data pipelines accessible even to non-programmers. Pre-built connectors and automated schema discovery further simplify setup, saving users time and effort. Glue's serverless nature and tight integration with the broader AWS ecosystem are also major draws, offering seamless scalability and data flow within a familiar environment. However, some users find Glue's strength in simplicity a double-edged sword. For complex transformations beyond basic filtering and aggregation, custom scripting in Python or Scala is required, limiting flexibility for those unfamiliar with these languages. On-premise data integration is another pain point, with Glue primarily catering to cloud-based sources. This leaves users seeking hybrid deployments or integration with legacy systems feeling somewhat stranded. Cost also arises as a concern. Glue's pay-per-use model can lead to unexpected bills for large data volumes or intricate pipelines, unlike some competitors offering fixed monthly subscriptions. Additionally, Glue's deep integration with AWS can create lock-in anxieties for users worried about switching cloud providers in the future. Overall, user reviews suggest Glue shines in cloud-based ETL for users comfortable with its visual interface and scripting limitations. Its scalability, ease of use, and AWS integration are undeniable strengths. However, for complex transformations, on-premise data needs, or cost-conscious users, alternative tools may offer a better fit.

Pros & Cons

  • Cost-Effective & Serverless: Pay only for resources used, eliminates server provisioning and maintenance
  • Simplified ETL workflows: Drag-and-drop UI & auto-generated code for easy job creation, even for non-programmers
  • Data Catalog: Unified metadata repository for seamless discovery & access across various data sources
  • Flexible Data Integration: Connects to diverse data sources & destinations (S3, Redshift, RDS, etc.)
  • Built-in Data Transformations: Apply pre-built & custom transformations within workflows for efficient data cleaning & shaping
  • Visual Data Cleaning (Glue DataBrew): Code-free data cleansing & normalization for analysts & data scientists
  • Scalability & Performance: Auto-scaling resources based on job needs, efficient Apache Spark engine for fast data processing
  • Community & Support: Active user community & helpful AWS support resources for problem-solving & best practices
  • Limited Customization & Control: Visual interface and pre-built transformations may not be flexible enough for complex ETL needs, requiring manual coding or custom Spark jobs.
  • Debugging Challenges: Troubleshooting Glue jobs can be complex due to limited visibility into underlying Spark code and distributed execution, making error resolution time-consuming.
  • Performance Limitations for Certain Workloads: Serverless architecture may not be optimal for latency-sensitive workloads or large-scale data processing, potentially leading to bottlenecks.
  • Vendor Lock-in & Portability: Migrating ETL workflows from Glue to other platforms can be challenging due to its proprietary nature and lack of open-source compatibility.
  • Pricing Concerns for Certain Use Cases: Pay-per-use model can be expensive for long-running ETL jobs or processing massive datasets, potentially exceeding budget constraints.

Key Features

  • Console: Discover, transform and make available data assets for querying and analysis. Builds complex data integration pipelines; handles dependencies, filters bad data and retries jobs after failures. Monitor jobs and get task status alerts via Amazon Cloudwatch. 
  • Data Catalog: Gleans and stores metadata in the catalog for workflow authoring, with full version history. Search and discover desired datasets from the data catalog, irrespective of where they are located. Saves time and money – automatically computes statistics and registers partitions with a central metadata repository. 
  • Automatic Schema Discovery: Creates metadata automatically by gleaning schema, quality and data types through built-in datastore crawlers and stores it in the Data Catalog. Ensure up-to-date assets – run crawlers on a schedule, on-demand or based on event triggers. Manage streaming data schemas with the Schema Registry. 
  • Event-driven Architecture: Move data automatically into data lakes and warehouses by setting triggers based on a schedule or event. Extract, transform and load jobs with a Lambda function as soon as new data becomes available. 
  • Visual Data Prep: Prepare assets for analytics and machine learning through Glue DataBrew. Automate anomaly filtering, convert data to standard formats and rectify invalid values with more than 250 pre-designed transformations – no need to write code. 
  • Materialized Views: Create a virtual table from multiple different data sources by using SQL. Copies data from each source data store and creates a replica in the target datastore as a materialized view. Ensures data is always up-to-date by monitoring data in source stores continuously and updating target stores in real time. 
Talend
Talend
Start Price
$4,800
Annually
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
Request Pricing Request Demo

Why We Picked Talend

Users praise Talend's visual drag-and-drop builder as a major draw, especially when compared to code-heavy platforms like Informatica. "Talend's UI feels much more intuitive and beginner-friendly," one reviewer shared, making it easier to onboard non-technical colleagues. However, its steeper learning curve compared to simpler tools like Snaplogic was also noted, demanding more training and initial investment.Talend's open-source roots and wide range of connectors were lauded, especially for cost-conscious businesses. One user compared it to IBM DataStage, noting "Talend's open-source version offers surprisingly robust functionality, making it a viable alternative for smaller budgets." However, limited native profiling compared to powerful tools like Alteryx was mentioned as a drawback, forcing integration with separate solutions and potentially raising costs.Users appreciated Talend's scalability and cloud-readiness, particularly when contrasted with legacy ETL offerings. "Talend scales seamlessly as our data needs grow," said one reviewer, making it a future-proof investment. However, some found enterprise features locked behind paid editions, like advanced lineage tracking and data quality monitoring, a disadvantage compared to competitors like Boomi that offer these capabilities across the board.Overall, Talend's balance of affordability, user-friendliness, and scalability shines through in user reviews. While it may require deeper initial investment in training and lack certain advanced features out-of-the-box, its flexibility and adaptability make it a strong contender for businesses navigating the evolving data landscape. As one user summed it up, "Talend might not be perfect, but it's a powerful tool that grows with you, and that's what matters most."

Pros & Cons

  • Simplified Lineage Tracking: Visually map data flows and transformations for enhanced auditability and compliance, ensuring your data journey is clear and transparent.
  • Centralized Policy Management: Define and enforce data quality, access, and security rules across the organization, fostering a consistent and controlled data environment.
  • Automated Data Catalog: Discover, document, and govern all data assets for improved data visibility and utilization, empowering your teams to find and leverage the right data.
  • Streamlined Data Masking & Anonymization: Protect sensitive data during development and testing while preserving data utility, safeguarding privacy and complying with regulations.
  • Scalable & Collaborative:** Manage data governance across diverse data sources and teams with ease, adapting to your evolving data landscape and fostering collaboration.
  • Limited Native Profiling: Lacks built-in tools for advanced data profiling and quality monitoring, requiring additional integrations or third-party tools, increasing complexity and potentially costs.
  • Steep Learning Curve: Complex UI and configuration can be challenging for beginners, especially non-technical users, requiring significant training and investment in onboarding and ongoing support.
  • Customization Challenges: Extensive customization options, while offering flexibility, can lead to complexity and maintenance overhead, particularly for large deployments or intricate data governance requirements.
  • Limited Community Support: While the Talend community is active, it may not be as extensive as other data governance solutions, potentially impacting troubleshooting and knowledge sharing, especially for niche issues or advanced configurations.
  • Enterprise Features in Paid Editions: Key data governance features like advanced lineage tracking, data masking, and data quality monitoring often require purchasing higher-tiered paid editions, increasing costs and potentially limiting accessibility for organizations with budget constraints.

Key Features

  • Pipeline Designer: Pull in data to create dashboards that power business insights. Build reusable pipelines to integrate data from any source, then transform it to upload to data warehouses. 
    • DIY Data Workflows: Design and preview data workflows directly in the web browser without IT help. Run pipelines directly where the data lives, with support for disparate filesystems, business apps, databases, data warehouses and messaging services. Automate data stewardship, preparation and cataloging into data pipelines. 
  • Data Inventory: Prepare, consume and share data from a single, centralized hub. Make informed decisions by putting unique, accurate data at the center of business. 
    • View data trustworthiness at a glance with the Trust Score. Aggregates multiple metrics into a single, easy-to-understand score, scaling from 0 to 5. 
    • Flags data silos across sources and resolves the gap with reusable and shareable data assets. 
    • Make data work for you by sharing assets between the data preparation and designer modules. 
  • Talend Open Studio: Handles big data by leveraging Hadoop and its databases, or HBase, HCatalog, HDFS and Hive. Connects to Oracle, Teradata, Microsoft SQL Server, Marketo, Salesforce, Netsuite, SAP, Sugar CRM, Microsoft Dynamics, SMTP, FTP/SFTP, LDAP and more. Provides an Eclipse-based integrated environment (IDE) for system development. 
  • Talend Sandbox: The vendor provides a sandbox environment for data warehouse optimization, clickstream analytics, social media sentiment analysis and Apache weblog analytics. 
Sisense
Sisense
Start Price
$1,500
Monthly, Freemium
Company Size
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Deployment
Cloud On-Premise
Platform
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Why We Picked Sisense

Users appreciate how Sisense creates large datasets from constantly evolving data sources to be seamlessly streamlined into actionable data, and presented easily in the form of graphs, bar and pie charts, scattergrams, line graphs, interactive maps and many more visualization types. Regarding customer support, users praise their prompt and informative responses - from implementation to handling subsequent queries — as high quality customer service that comes neatly packaged with pricing plans.
Users mention that the implementation of this solution, though easy for basic setup, can be quite demanding in terms of scripting and configuration setup, especially for advanced analytics. Non-technical users might find it challenging to set up the data objects - ElastiCubes - without strong IT support. On the front-end, dashboard customization and formatting of visualizations can be tricky as these require basic knowledge of CSS, Javascript and development. Though a large number of users find this solution to be cost-effective, smaller businesses and startups that only need a limited number of licenses might find it expensive.
Overall, Sisense is a strong BI solution with strong data capabilities, and its drill-down functionality empowers users to glean insightful and actionable analytics that drive business strategy by aiding in decision-making processes. To maximize its potential, businesses may require strong IT support for implementation and formatting of visualizations. It is certainly worth considering for enterprise BI needs, if the price is right!

Pros & Cons

  • Customer Service and Support : More than 92% of users mentioning customer support said that responses to their queries were prompt and informative, and that there’s good access to online user communities.
  • Data Integration: Approximately 92% of users who mention data source integrations express satisfaction with its ability to import, blend and streamline data from multiple sources into a cohesive database.
  • Data Visualization: Citing stunning dashboards with a range of visually powerful visual tools like widgets, graphs and scattergrams, 85% of users who mention data visualization say it’s a strong feature of Sisense.
  • Ease of Use: More than 93% of users mentioning the platform’s ease of use praise it as user-friendly, citing self-service BI features such as interactive dashboards and data visualizations.
  • Cost: Among users who comment on its pricing, more than 85% said that Sisense offers great value for its cost, with some reviews coming from long-time customers.
  • Data Preparation and Modeling: About 50% of users who mentioned data modeling say that it is not as intuitive for non-technical staff, requiring strong IT support.
  • Training: More than 60% of users who write about learning how to use the platform say that with frequent new releases, a lack of sufficient documentation, coupled with the difficulty of using a programming language make this a tricky tool to master.

Key Features

  • In-Chip Analytics: Free up processing power for other tasks, improve the speed of processing and reduce downtime caused by overworked RAM or disks. Combine a columnar database with smart algorithms in an in-chip cache. Choose between either disk or RAM, rather than solely disk or RAM, as an alternative to in-memory processing.
  • Data Connectivity: Draw from many different data sources and formats with hundreds of built-in connectors with cloud applications such as Zendesk and Salesforce. Drag and drop to import from databases and spreadsheets. Pull directly from CRMs or embed the BI interface into the CRM, providing versatility in workflow options.
  • Data Blending: Blend huge datasets from a range of sources instantly into one centralized location, and receive a holistic view of all data. Investigate further through slicing, dicing and exploration of data via a simple, accessible interface.
  • Data Visualizations: Organize user data into easy-to-understand visualizations like bar charts, scattergrams, pie charts, line graphs, interactive maps, etc. Access a rich library of prebuilt graphics and widgets or open source designs, and easily manipulate them from within an intuitive UI. 
  • Interactive Dashboards: Drag-and-drop to build interactive dashboards that encourage deeper data exploration. Help users to build widgets and filters, and leverage AI-assisted exploration and automatic analysis to provide further insight. 
  • Real-Time Insights: Set up and maintain live connections to data and monitor dashboards in real time. Establish push notification alerts to receive automatic updates when changes to KPIs occur. 
  • Single-Stack System: Perform a range of data preparation tasks, such as exploration, analysis, visualization and collaboration, without needing to switch to other platforms.
  • Publish and Share: Enable everyone in an organization to access and filter information with web-based dashboards. Publish a dashboard with one click and open it to the entire company for easy access without needing to download files.
  • Augmented Intelligence: Leverage Sisense Pulse, powered by machine learning algorithms, to continually monitor KPIs and receive proactive alerts when anomalies and variations are detected. Automate previously manual tasks and data-driven workflows, personalize the BI experience with a tailored command center and deliver a dynamically updated feed that keeps users up to date.
  • Embedded Analytics: Turn data into data products that deliver BI to clients with a white label solution in Product Teams.
  • R Integration: Perform predictive analysis and access better BI reporting and decision-making through R programming language.
  • Add-Ons: Extend the functionality of the platform by downloading and equipping any number of free and premium native and third party extensions from the marketplace.
  • Natural Language Processing: Empower users of all data literacy levels to derive the full value of their insights through natural language generation with Sisense Narratives. Simplify into everyday language the complexities of data analysis for accessibility and easier understanding through AI-generated text-based insights.
  • Mobile BI: Access BI on-the-go directly from iOS and Android phones with a native app that renders mobile-optimized responsive dashboards with touchscreen integration. Alternatively, access the platform through a browser from any laptop, tablet or smartphone, no downloads or installation required.

Pricing

License/Subscription Cost Annual license cost is based on tiers depending on the number of users. The first tier allows up to 10 users (Basic). The second tier (Business) supports up to 50 users, and the third tier (Business+) supports an unlimited number of users
Maintenance Cost

There are no additional costs for training or to maintain the software

For each pricing plan, there are free levels of support
Installation/Implementation Cost The solution can be implemented in a short span of time as there are no additional hardware or servers to set up
Customization Cost Dependent on functional requirements and specific needs of the organization
Data Migration Cost/Change Management/Upfront Switching Cost Dependent on your current software, amount of data to be migrated, availability of migration tools, complexity of data and gaps between the existing system and the new system
Recurring/Renewal Costs Renewal cost is equivalent to the fees paid annually

COMPARE THE BEST Data Integration Tools

Select up to 2 Products from the list below to compare

 
Product
Score
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SelectHub’s Analyst Rating is based on data from our 400+ point analysis of Data Integration Tools, user reviews, and our own crowd-sourced data from our free software selection platform. Scores are periodically updated via ongoing selection projects and user activity within our platform.
Start Price
Free Trial
Company Size
Deployment
Platform
Logo
IFTTT
$20,000
Yes
Small Medium Large
Cloud On-Premise
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IFTTT
Domo
$15
Per User, Monthly
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Domo
Mathematica
$1,800
Annually
Yes
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Mathematica
Stata
$48
Annual, Quote-based
Yes
Small Medium Large
Cloud On-Premise
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Stata
Redshift
$0
No
Small Medium Large
Cloud On-Premise
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Redshift
Fivetran
$55
Monthly
Yes
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Cloud On-Premise
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Fivetran
SQL Server Integration Services
$300
Monthly
Yes
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SQL Server Integration Services
AWS Glue
$0.44
Per M-DPU-Hour
No
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Cloud On-Premise
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AWS Glue
Talend
$4,800
Annually
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Talend
Sisense
$1,500
Monthly, Freemium
No
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Cloud On-Premise
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Sisense

All Data Integration Tools (35 found)

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Phocas

by Phocas Software
Phocas
Phocas is a cloud-based data analytics tool that helps users manage their business operations. Designed for non-technical users, it uses simplified self-service reporting to deliver customized insights. Users can build dashboards to their specifications and view them through an ad hoc analysis layer called “The Grid” to uncover business opportunities.
User Sentiment User satisfaction level icon: excellent
Cost Breakdown
$100 - $500
Company Size
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Deployment
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Platform
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TARGIT

by Targit
TARGIT
Targit is built from the ground up for reporting and business metrics, getting actionable insight from trillions of rows of data in seconds. It surfaces complex events and provides key information to make business decisions. It merges internal and external data and presents it in customizable dashboards and storyboards. Businesses can figure out how to grow in target markets by accurately analyzing data. Deployment options vary from stand-alone to embedded, with web browser compatibility built-in.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Infor Birst

by Infor, Inc.
Infor Birst
Infor Birst is a cloud-based analytics software tool that aims to help users discover insights without the need for analyst input. It unifies IT-managed enterprise data with user-owned data, supporting the blending of both in a top-down and bottom-up manner. It uses consistent business metrics to structure raw data into organized sets and visualizations. It helps users identify patterns and better understand their organization’s KPIs. It offers a seamless, integrated UI that allows users to perform every step of the data analysis process in a single interface, enabling a smooth experience. It can be deployed either from the cloud or self-hosted on-premise. Users can purchase it in three available formats: per-user fee, by department or business unit or by end-customer in embedded scenarios.To explore other popular solutions that Infor offers, visit our Infor company page.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Integrate.io

by Integrate.io
Integrate.io
Integrate.io (formerly Xplenty) is an ETL (Extract, Transform, Load) software helping businesses move data between various sources, clean and organize it, and deliver it to analytics platforms. It caters to companies needing to streamline dataflows for insights and reporting. Integrate.io shines with its user-friendly interface, pre-built connectors, and drag-and-drop functionality, making it accessible to non-technical users. Key features include data pipeline building, data transformation tools, and scheduling capabilities. Compared to peers, users praise Integrate.io's intuitive design, ease of use, and vast connector library. However, some mention limited data lineage tracking and high costs for larger data volumes. Pricing varies based on data volume and features needed, typically starting in the hundreds per month and scaling upwards. Consider your data needs and technical expertise when comparing Integrate.io to similar ETL solutions. Pros User-friendly interface Drag-and-drop functionality Vast connector library Intuitive data mapping Solid customer support Cons Limited data lineage tracking High costs for larger data volumes Occasional performance issues Customization options can be limited Error handling could be more robust
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Logi Symphony

by insightsoftware
Logi Symphony
Logi Symphony (formerly Logi Analytics) provides businesses with interactive data analytics and visualizations, offering advanced reporting and visualization features in a minimal-code environment. It offers self-service data analysis and data discovery; then connects and blends operational intelligence to prepare it; and finally lets users turn it into dashboards, reports and other visualizations.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Hevo

by Hevo Data
Hevo
Hevo is a fully managed end-to-end data pipeline service for enterprises of all sizes. Businesses can glean proprietary insights from disparate sources that include live data from streaming devices. With its no-code interface and automated workflows, data teams can integrate business information for reporting and analytics. It enables running minimum-impact data workflows, setting aside erroneous events and anomalous records for redressal, then assimilating them directly into the pipeline. The vendor offers a 14-day free trial while providing a few free SaaS sources to create data pipelines. Users of the Free plan have access to both free and paid sources. Pros: Cloud-based, low overhead Easy-to-use interface Wide range of connectors Cost-effective for basic needs Automated data refresh Cons: Limited customization options Not ideal for complex needs Scalability concerns Limited data transformations Limited open source integration Pricing can increase with usage Vendor lock-in concerns
User Sentiment User satisfaction level icon: excellent
Cost Breakdown
$10 - $100
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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SAP Data Services

by SAP, Inc.
SAP Data Services
Formerly known as BusinessObjects Data Services (BODS), it’s part of the information management layer of SAP’s Business Technology Platform. With a focus on data quality, it extracts, transforms and loads all data types to enterprise interfaces. Dashboards enable visual analytics and display the impact of data quality issues on downstream workflows. Auto documentation allows teams to write dataflow information and add comments for collaboration. An intuitive UI and Unicode compliance provide localization across more than 190 countries. Data security is ensured with AES 128-bit encryption. Pros: Robust transformations Scalable for large volumes Tight SAP integration Visual job design Centralized governance Cons: High cost and licensing Steep learning curve Limited open source SAP ecosystem dependence Complex job maintenance
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Grow

by Grow
Grow
Grow is a cloud-based data visualization solution that helps users derive insights from their data to evolve their businesses. As their brand name may suggest, the vendor designed it to help growing small to mid-sized businesses to access actionable insights. Suitable for the average user, it turns spreadsheets into dashboards that simplify metrics for understanding and can be sent quickly to customers or other employees. It has three offerings: Basic, Professional and Enterprise, which offer varying levels of connectivity as well as varying prices for additional dashboards. All three allow for an unlimited number of users.
User Sentiment User satisfaction level icon: excellent
Cost Breakdown
$10 - $100
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Oracle Data Integrator

by Oracle America
Oracle Data Integrator
Oracle Data Integrator (ODI) is a data integration platform designed to extract, transform, and load (ETL) data from various sources to target systems. It offers a visual interface for building and managing data pipelines, including pre-built connectors for popular databases, applications, and cloud services. ODI is ideal for organizations needing to integrate data from diverse sources for business intelligence, data warehousing, and other analytical needs. Its key benefits include ease of use, scalability, high performance, and extensive out-of-the-box functionality. Popular features include graphical mapping interface, data quality checks, data lineage tracking, and support for complex data transformations. User reviews highlight ODI's strengths in simplifying complex data integration tasks, offering robust data quality tools, and ensuring efficient data processing. However, some users report occasional performance issues and limited flexibility compared to more open-source solutions. Pricing varies based on deployment options and required features, typically ranging from several thousand to tens of thousands of dollars per year, with payment options including annual licenses and subscription plans. Pros Easy to use interface Strong data quality tools High performance scalable Extensive built-in functionality Connects to popular data sources Cons Occasional performance issues Less flexible than open-source tools Steeper learning curve for advanced tasks Potentially high cost depending on deployment Limited community support compared to open-source options
User Sentiment User satisfaction level icon: great
Cost Breakdown
$10 or less
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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InfoSphere Information Server

by IBM
InfoSphere Information Server
InfoSphere Information Server is a data integration powerhouse designed to unify information across complex, diverse systems. It excels at extracting, transforming, and loading data (ETL/ELT) for tasks like building data warehouses, powering analytics, and driving business insights. Best suited for large enterprises with demanding data needs and dedicated IT resources, InfoSphere boasts robust features like comprehensive data source/target connectors, powerful transformation tools, and advanced governance capabilities. User feedback highlights its scalability, security, and job automation as key benefits. However, its complexity and steep learning curve can be daunting for smaller setups. Additionally, the high licensing costs and resource-intensive nature might dissuade budget-conscious organizations. Compared to other data integration tools, InfoSphere leans towards high-volume, mission-critical scenarios, while alternative options might offer simpler setups or cater to broader use cases. Choosing the right fit depends on individual needs and priorities. Ultimately, InfoSphere Information Server shines when organizations need a robust, feature-rich platform to conquer complex data challenges, even at the cost of increased upfront investment and initial learning hurdles. Pros Powerful ETL ELT capabilities Wide range of data sources targets Job scheduling monitoring Data quality transformation tools Scalable secure architecture Cons Steep learning curve complexity High licensing costs Limited out-of-the-box connectors Performance bottlenecks with large datasets Resource-intensive for deployment maintenance
User Sentiment User satisfaction level icon: good
Cost Breakdown
$1,000 or more
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Exago

by insightsoftware
Exago
Exago BI is embeddable, flexible and scalable. It’s available as a standalone solution and as part of Logi Analytics, a data suite from insightsoftware. It’s designed for multi-tenant SaaS use and requires no additional desktop downloads or plug-ins. It offers an open API and customizable UI, giving users control and flexibility. It includes express reports, pixel-perfect reports and report formatting templates.
User Sentiment User satisfaction level icon: excellent
Cost Breakdown
$10 or less
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
Request with no obligation:
Demo Pricing

ClicData

by ClicData
ClicData
ClicData is a cloud-based solution that collects data from multiple sources and delivers insights that are easy to use, embed and access on any device. It enables users to design and share dashboards. It has four different pricing plans with varying levels of functionality: Premium, Team, Enterprise and Dedicated. Users can purchase subscriptions at a yearly or monthly rate.
User Sentiment User satisfaction level icon: excellent
Cost Breakdown
$10 - $100
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Dundas BI

by Dundas Data Visualization
Dundas BI
Dundas BI is a self-service analytics solution with a keen focus on embeddability. With an eye on the end-user experience, the vendor provides options to design and embed dashboards into applications. Ranked five on our leaderboard, Dundas BI is a user favorite. The platform supports standard file formats and connects to cloud storage and enterprise systems.Report templates and interactive visualizations make data accessible to all users. Some available graphics include bar and line graphs, scattergrams, pie charts, scorecards and maps. The system also suggests visualizations that fit the analyzed data.Besides a long list of connectors, the platform has built-in ETL. You can switch from a data warehouse to live sources with the click of a button and write back to the warehouse from the interface.The vendor, insightsoftware, offers it under the Logi Symphony umbrella, but Dundas BI remains a distinct product, and support is available. It supports multi-tenancy, and you can manage multiple clients with assured data security.Roles include developers, power users and report consumers. The vendor provides shared concurrent licenses to keep pricing within reach. Unlimited data refreshes at no extra cost make it an attractive option.Reviews praise its visual capabilities and ease of use, though most users say there’s a steep learning curve and performance can lag sometimes. Pricing starts from $2,500 monthly.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Qlik Talend Data Integration

by QLIK TECHNOLOGIES INC.
Qlik Talend Data Integration
Qlik Talend Data Integration empowers organizations to unify, govern, and integrate data from diverse sources. It shines for businesses grappling with data fragmentation and seeking to establish a trusted data foundation for analytics, AI, and informed decision-making. Key benefits include streamlined data pipelines, automated data quality checks, and real-time data synchronization. Notably, its visual drag-and-drop interface fosters ease of use, while pre-built connectors and code-free transformations accelerate development. Compared to similar tools, user experiences highlight Talend's scalability, flexibility, and robust data governance capabilities. While pricing varies based on specific needs and deployment options, expect a tiered subscription model with per-user or per-month charges.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$10 - $100
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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SAS Data Management

by SAS Institute
SAS Data Management
SAS Data Management empowers organizations to wrangle their data, from ingestion and cleansing to transformation and governance. It excels at handling mountains of complex data, making it ideal for industries like finance, healthcare, and government. Key benefits include streamlined data integration, enhanced data quality, and robust security controls. Popular features include its drag-and-drop interface, automation capabilities, and advanced analytics tools. User experiences suggest it can be powerful for data wranglers but might have a steeper learning curve compared to simpler options. Pricing is typically per seat or core, with annual subscriptions or perpetual licenses available. Pros Robust for large datasets Scalable high performance Advanced data manipulation Automate complex tasks Strong statistical analysis Cons Steep learning curve Costly licensing maintenance Limited visual analytics Not user-friendly interface Difficult to debug code
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Buyer's Guide

Data Integration Software Is All About Comprehensive Insight 

By Ritinder Kaur, Market Analyst at SelectHub 

Data Integration BG Intro Header

Tracking key performance metrics is critical for staying competitive and establishing your brand reputation. But, enterprises struggle with combining large, complex proprietary datasets to derive business insight. Business intelligence solutions that collect and store data for analytics and reporting are called data integration tools.

When in the market for a data integration platform, analyzing various products’ features to select a suitable tool can be overwhelming. This buyer’s guide intends to equip you with the research tools required to decide.

Executive Summary

  • Besides ETL tools and data warehouse platforms, data integration software includes application integration, quality management and master data management systems.
  • Data integration techniques have evolved over the years to optimize retrieval speed and storage, ELT and streaming data integration being two of them.
  • Data integration software gives you a competitive edge by helping you connect to sources faster.
  • Analyzing the features of the most popular data integration tools in the market can help you create your requirements list.
  • Asking the right questions within your organization and to software vendors about their services and the product narrows down your buying options.

What This Guide Covers:

What Is Data Integration Software?

Data integration systems collect large amounts of disparate data into repositories for subsequent transformation and analysis. Some systems organize and transform data before storing it, while others perform the structuring and cleansing after, but they are all data integration solutions.

Data integration’s origin lies in extract-transform-load workflows, and it is the most critical data management component. Also called big data integration, it gives you a single source of truth, essential if you deal with huge data volumes and complex datasets.

A data integration framework includes a network of data sources, clients and a primary server. The server accepts data requests to pull data from various sources, consolidates the datasets and structures them before delivering them to the client for use.

Data integration evolved to provide enterprise application integration, cloud integration, data consolidation and master data management.

  • Application integration involves engineering multiple applications to work together through service-oriented architecture and electronic data interchange.
  • Integration-platform-as-a-service tools are cloud-based and enable data integration across on-premise applications and cloud platforms.
  • Data consolidation involves centrally pulling data from multiple sources or moving it from one repository to another.
  • Master data management ensures a centralized data source with consistently accurate and unique data, helping you identify inconsistencies and operational inefficiencies in your business.
Data Integration Software Categories

Data integration techniques include:

  • Change Data Capture: It accelerates updates by capturing live data changes and applying them to repositories and applications rather than transferring actual datasets. ETL systems work on CDC and enterprise application integration.
  • Data Replication: Based on business requirements or priority, full or partial data replication are possible in on-premise and cloud systems. You can transfer data on demand, in bulk or batches, or write to target databases in real time.
  • Data Virtualization: Instead of combining data, the system collates datasets’ information in a unified virtual layer that functions as an index. It points to the location where the dataset is housed while abstracting you from how the system stores and formats it. A subset of virtualization, data federation enables two databases to present as one, whether on-premises or cloud-based.
  • Extract, Transform and Load: ETL involves data gathering, structuring and transformation before loading into the target storage.
  • Extract, Load and Transform: An improvement on ETL, this technique uploads unstructured data into storage for at-will processing. Data lakes store structured and unstructured data types.
  • Streaming Data Integration: This technique is the answer to ETL’s periodic batch updates. It’s a constant flow of data to which multiple clients can subscribe at a time without impacting the source. Data producers and consumers can join and leave the stream at will, making it a horizontally scalable system.

Primary Benefits

Data integration software’s benefits extend beyond ETL and data warehousing.

Let’s look at a few.

Data Integration Software Benefits

Gain a Competitive Edge

Efficient data integration improves data analytics quality in your organization. Modern data integration does away with manual information gathering, letting you focus on using numbers rather than entering them. Streamlined operations enhance company productivity and performance, building customer trust and brand reputation.

Siloed organizational systems hurt your business more than you realize. For instance, if your Microsoft Dynamics ERP doesn’t share data with your Salesforce instance, you could be losing out on valuable insight. Data integration combines this information, giving you a 360-degree perspective to analyze performance and make better business decisions.

Many data integration tools provide issue-reporting so you can track and repair data issues in real time.

Connect to Sources Faster

Modern data integration tools help you connect faster to sources with built-in connectors. You can add new sources as they become available with custom connectors and control them through a centralized console.

Even with the technical debt of legacy systems, software-based data integration frameworks are easy to build, not always expensive and require primary coding skills. You can be up and running in no time, speeding up the time to insight.

Get Valuable Insights

Manually combining data from various sources is tedious, time-consuming and error-prone. Besides, data becomes outdated by the time you get the results. With modern data integration software, almost no data gets left behind, uncovering hidden insight.

Having all your data in one place gives you a single reference point and eliminates duplicate data. Automated workflows review and organize datasets to ensure they’re accurate and updated, reducing the chance of errors. Whether generating reports or building applications, all information is at hand.

Collaborate

B2B application integration facilitates client collaboration on projects. By giving them controlled access to desired data, you can invite instant feedback and have meaningful discussions around key metrics. Faster turnaround translates to an agile product delivery pipeline, optimizing your resources and creating a pleasant client experience.

A reliable data integration framework is part of industry best practices and can build lasting customer relationships.

Key Features & Functionality

ExpertMarketResearch predicts that the data integration software market will grow to $20.13 billion during 2021-2027.

Data Integration Market Summary

While data integration software vendors advertise their newest, shiniest features, identifying your basic functional requirements early on sets a sound foundation for your software selection search.

Ubiquitous Deployment

The data integration platform should combine data from various systems, whether on-premise or in the cloud. Data integration between business applications should be seamless, irrespective of their deployment.
Data Source Connectivity

The solution should access relational, columnar, in-memory and NoSQL databases, OLAP systems, Hadoop and other big data platforms. Draw data from flat files, including CSVs, spreadsheets, enterprise messaging systems, XML and JSON.

It should incorporate data from business applications like ERPs and CRMs, web services, mobile applications, SaaS applications, social media sites, emails, images and videos.
Enterprise Application Integration

Acquire rich insight by simplifying data flow between applications. Automate cross-platform data movement — no need to transcribe data across apps manually.

EAI enables customization of OS, data formats and languages to ensure compatibility across applications.
Master Data Management

The solution should facilitate fast data access by allowing data quality and usage management. Data integration centralizes all business data, deletes duplicates and keeps all information updated. Improves business operations and reduces risk by identifying inefficiencies and blockers.
Data Quality Management

Make data-backed business decisions with consistent access to complete, accurate and up-to-date information. Build trust in employees and clients and inspire confidence in business intelligence reports and analytics results.

Discover data quality issues, risks and overall trends through data profiling. Uncover metadata, key relationships and functional dependencies that impact your business.
ETL/ELT

Transform and map all data types into analysis-ready statistics with data munging, aggregation, visualization and modeling. Or load them into data warehouses and lakes to be cleansed and prepared when needed.
Self-Service Capabilities

Data integration tasks are no longer limited to data scientists alone with a no-code approach. Visual workflows and automation enable self-service data integration. Business teams are no longer dependent on data scientists, enabling faster decision-making.

With automated workflow templates, anyone can learn how to build a data pipeline in minutes.
Performance and Scalability

The solution should be highly performant and available, with failover and redundancy mechanisms. It’s scalable if you can add new data sources as they become available, especially when your business grows.

Software Comparison Strategy

Every business has a unique set of needs. Create a requirements checklist of the features your company requires. Once you have a clearer idea of what you want in a reporting platform, compare applications and vendors with our software comparison report.

Cost & Pricing Considerations

The cost of an enterprise reporting tool can make or break your purchase decision. Check pricing details on the vendor’s website or reach out to them for a quote. Additional user seats and storage will cost you extra for capacity-based pricing plans. Plus, add-ons can add to the overhead.

Ask if the vendor provides implementation support and on what terms. Check if they offer email, chat and phone support within the product price.

Get our free pricing guide to determine which top data integration software vendors align with your budget.

The Most Popular Data Integration Software

Sifting through the various data integration tools available in the market is time-consuming but essential. You can start by checking out the software list on our analytics leaderboard, curated by the SelectHub analyst team. It ranks the best data integration software based on how well they deliver on the essential requirements.

IFTTT

It’s a data integration service that allows you to connect to over 630 apps through integrations — applets — on desktop and mobile. The vendor provides webhooks services to create and receive updates from your favorite applications.

Popular IFTTT webhook integrations include Discord YouTube WebHook Integration and Discord Twitter Webhook Integration. You can also connect IFTTT to Siri through webhooks using the Shortcuts app on your iPhone.

IFTTT

App Integrations Are Available on Desktop and Mobile.

Wolfram Mathematica

Wolfram Mathematica is a powerful data integration tool that empowers you to unify diverse data sources into a single, comprehensive environment. With its intuitive interface and a vast library of built-in functions, it simplifies data ingestion, cleansing and manipulation, allowing you to extract meaningful insights.

Key features include symbolic and numeric computation, cryptography, graphics and visualization and data computation.

Wolfram Mathematica

Use statistical visualization to analyze financial data. Source

Domo

It’s a business intelligence and analytics solution with data integration capabilities. More than 1000 built-in connectors enable wide-ranging connectivity to manipulate, transform and aggregate data for company-wide insight. You can move data across systems with writeback connectors and APIs, including ODBCs.

Domo integrates with cloud data warehouses to provide flexible storage, caching, querying and analytics. You can create apps to activate and automate workflows.

Domo

Data Integration Software Shows the Latest Results.

 

 

Questions to Ask

While assessing your organization’s internal needs, asking the right questions of key stakeholders can give you valuable insight into what they want in a data integration tool.

Data Integration Key Questions To Ask

Use these questions as a starting point for internal conversations.

  • What are the gaps in your current data integration framework?
  • Which data sources do you need to connect to, and where are they housed?
  • What kind of data do you need to analyze — transactional or operational?
  • How much data will your company need to move and how fast?
  • Which deployment model would you prefer — on-premise, cloud or hybrid?

When considering data integration software options, you might not need all the attractive features on offer. The platform might match your primary requirements and fit your budget but lack a must-have feature. If you plan on adding it later in-house, factor in the cost of IT resources to assess if it’s worth the trouble.

If the price is more or less comparable with an advanced solution with the desired functionality, it might make more business sense to buy this solution instead.

Asking discerning questions about the software and vendor services at the onset can help you answer these and many other lingering doubts in your mind.

Use these questions to start conversations with data integration vendors.

  • Is the software compatible with your tech stack?
  • Is data management part of the software package?
  • Is the solution user-friendly? Is there a central management console where team administrators can manage data integration across sources and targets?
  • Does the vendor provide training, documentation and support post-implementation?
  • Do they have a reputation for delivering competitive data integration solutions?

In Conclusion

Searching for a suitable data integration solution requires deep knowledge of your organization’s business goals with critical analysis skills to conduct market research. This buyer’s guide intends to equip procurement managers with research tips and sufficient domain knowledge to start their software search.

About The Contributors

The following expert team members are responsible for creating, reviewing, and fact checking the accuracy of this content.

Ritinder
By Ritinder Kaur
Technical Content Writer
Ritinder Kaur is a Senior Technical Content Writer at SelectHub and has eight years of experience writing about B2B software and quality assurance. She has a Masters degree in English language and literature and writes about Business Intelligence and Data Science. Her articles on software testing have been published on Stickyminds.
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Sagardeep
Technical Research By Sagardeep Roy
Senior Analyst
Sagardeep is a Senior Research Analyst at SelectHub, specializing in diverse technical categories. His expertise spans Business Intelligence, Analytics, Big Data, ETL, Cybersecurity, artificial intelligence and machine learning, with additional proficiency in EHR and Medical Billing. Holding a Master of Technology in Data Science from Amity University, Noida, and a Bachelor of Technology in Computer Science from West Bengal University of Technology, his experience across technology, healthcare, and market research extends back to 2016. As a certified Data Science and Business Analytics professional, he approaches complex projects with a results-oriented mindset, prioritizing individual excellence and collaborative success.
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Manan
Technical Review By Manan Roy
Principal Analyst
Manan is a native of Tezpur, Assam (India), who currently lives in Kolkata, West Bengal (India). At SelectHub, he works on categories like CRM, HR, PPM, BI, and EHR. He has a Bachelor of Technology in CSE from The Gandhi Institute of Engineering and Technology, a Master of Technology from The Institute of Engineering and Management IT, and an MBA in Finance from St. Xavier's College. He's published two research papers, one in a conference and the other in a journal, during his Master of Technology.
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Hunter
Edited By Hunter Lowe
Content Editor
Hunter Lowe is a Content Editor, Writer and Market Analyst at SelectHub. His team covers categories that range from ERP and business intelligence to transportation and supply chain management. Hunter is an avid reader and Dungeons and Dragons addict who studied English and Creative Writing through college. In his free time, you'll likely find him devising new dungeons for his players to explore, checking out the latest video games, writing his next horror story or running around with his daughter.
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