Best ETL Tools Of 2024

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Why We Picked InfoSphere Information Server

User reviews of InfoSphere Information Server paint a picture of a powerful data integration tool, capable of handling complex tasks and diverse data sources. Admiration for its robust ETL/ELT capabilities, data quality tools, and secure architecture echoes frequently, with users citing improved data accuracy and streamlined data movement as major benefits. Automation features and job monitoring are also praised for boosting operational efficiency and freeing up resources. However, the praise comes with caveats. The steep learning curve and demanding resource requirements are consistent gripes, making InfoSphere a better fit for larger organizations with dedicated IT expertise and infrastructure. The high cost of ownership, including licensing, maintenance, and potential hardware upgrades, further strengthens this point. Additionally, the limited out-of-the-box connectors and potential performance bottlenecks with massive datasets are concerns for some users. Compared to competitors, InfoSphere shines in its scalability and security, catering to high-volume, mission-critical scenarios. However, users also acknowledge the presence of simpler, more user-friendly options that might be better suited for smaller setups or less complex data needs. Ultimately, the choice boils down to individual priorities. If data volume, security, and advanced features are paramount, InfoSphere stands out, despite its demanding nature. But for those seeking a smoother learning curve or broader use cases, other solutions might offer a better fit. In essence, user reviews reveal InfoSphere Information Server as a powerful tool for complex data challenges, but its strengths come with a price tag and learning curve. Carefully evaluating data needs and priorities is crucial before choosing this data integration powerhouse.

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Pros & Cons

Powerful Data Handling: Handles complex ETL/ELT processes and diverse data sources (relational, flat files, cloud platforms) with ease, streamlining data movement and integration.
Enhanced Data Quality: Ensures data accuracy and consistency through robust cleansing, validation, and transformation tools, boosting trust and reliability in data insights.
Scalability and Security: Supports large data volumes and complex integrations with confidence, safeguarding sensitive information through granular access controls and encryption.
Job Automation and Monitoring: Automates data pipelines and workflows, reducing manual effort and simplifying task management with comprehensive monitoring capabilities.
Improved Operational Efficiency: Centralizes data integration processes, eliminates data silos, and frees up IT resources for higher-value tasks, leading to cost savings and increased productivity.

Steep Learning Curve: Complex interface and feature-rich platform require significant training and expertise, even for experienced data professionals.
High Cost of Ownership: Licensing fees, maintenance, and potential hardware/infrastructure upgrades can make it a costly solution for smaller organizations or simpler data needs.
Limited Out-of-the-Box Connectors: May require custom development or third-party tools for integration with certain data sources or applications, increasing implementation complexity and costs.
Performance Bottlenecks: Can experience slowdowns or scalability issues with very large datasets or intricate ETL/ELT processes, demanding careful optimization and resource allocation.
Limited Cloud-Native Functionality: Core features are primarily designed for on-premises deployments, with cloud options requiring additional setup and configuration, potentially hindering agility and flexibility.

Key Features

Data Integration: Collect, transform and share large amounts of complex data assets across the organization. Reduce development time, and scale flexibly by leveraging built-in data transformation functions. Deliver data in real time to business applications in bulk, virtually or through change data capture (CDC).
Data Quality: Cleanse and validate data — in batches and real time — then load it into analytical views for consistent monitoring. Establish data quality metrics across the organization by reusing these data views. Ensure consistent information organization-wide by linking related records across systems.
- Business Glossary: Create a single source of truth – consolidate disparate data into unique, reliable records and load into repositories and master data applications. Share insights with confidence powered by complete access to proof of lineage and data quality. A centralized hub maintains data governance rules.
Information Governance Catalog: Empower data scientists and analysts to explore and analyze business data in compliance with enterprise governance standards. Create, manage and share a common business language, design and apply rules and track data lineage. Extend on-premise governance investment to the cloud by integrating with IBM Watson Knowledge Catalog.
Metadata Repository: Share imported metadata and other assets in any server component across. the organization. Stores project configurations, reports and results for all the server’s components in one unified repository

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Talend

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^$4,800

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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."

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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.

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Informatica PowerCenter

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^$5,000

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Why We Picked Informatica PowerCenter

Informatica PowerCenter has a reputation for easy adoption. The drag-and-drop interface makes it a breeze to move data around and connect to different sources.

It seamlessly connects to cloud apps, databases, and legacy systems, saving businesses a ton of manual work. The wide range of connectors and smooth integration with other Informatica tools seem valuable enough to justify the cost.

However, it isn't perfect. Compared to similar software, there’s a steeper learning curve, especially for advanced features. The cost can also be a sticking point, especially for businesses with small data loads.

Performance is another area with mixed reviews. While PowerCenter has traditionally been reliable for everyday tasks, its performance can lag while handling massive amounts of data.

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Pros & Cons

User-Friendly: Dragging and dropping fields and screen elements make daily data tasks simple, though building custom functions might require more effort.
Extensive Connectivity: Pre-made connectors seamlessly link to databases, files, cloud apps and legacy systems.
Data Quality Management: Built-in tools find and fix errors and duplicates in data, keeping it accurate and reliable.
Scalability: Works efficiently with massive volumes of data using parallel processing and optimization techniques, making it perfect for big businesses.
Data Security: Access controls, encryption and activity tracking protect sensitive information.

Learning Curve: While it’s easy to perform basic tasks, mastering some advanced features and fixing complicated problems can be tricky.
Cost: It can be expensive, especially for smaller companies. Though small businesses don’t have vast data sizes, the deployment model and selected features can inflate the price.
Slowdown with Big Data: It might slow down with large data sizes. It’s advisable to ask for demos with high data loads.
Needs Tech Experts: Managing the infrastructure requires IT people and system administrators.

Key Features

Prototyping: Test data flows by building a preview version before creating the final workflow.
In-Production Testing: Identify issues early by checking data quality in production workflows.
Data Transformation: Interpret various data formats like XML, PDF, JSON and IoT data.
Live Analytics: Respond to market changes proactively using the latest insight, thanks to change data capture. Handles large volumes with dynamic partitioning and scalability.
Scalability: Handle growing data sizes, types, sources, projects and users without breaking into a sweat.
Collaboration: Gain visibility into where data originated and which processes it underwent before reaching the system. Analyze the possible impact of a decision before signing off on it. Keep business and IT teams on the same page with shared metadata.
Early Warning: Spot glitches and improper usage of resources early on. Get alerts when something goes wrong with data integration processes.

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SAP Data Services

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^$10,000

Per 250GB of Processed Data

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Why We Picked SAP Data Services

User reviews of SAP Data Services paint a contrasting picture. Proponents praise its robust data transformations, seamless SAP integration, and scalability for handling large data volumes. The visual job design and centralized governance features further attract organizations needing intuitive data flow creation and efficient data lineage management. Additionally, SAP Data Services shines in SAP-centric environments, simplifying data movement within existing infrastructure. However, critics point to its high cost and complex licensing as major drawbacks, making it less compelling for organizations on a budget or using diverse data sources. The steep learning curve and limited open-source compatibility can also be hurdles, requiring dedicated training and potentially restricting customization options. Compared to open-source alternatives like Talend or Apache Airflow, SAP Data Services offers less flexibility and community support. Additionally, its tight dependence on the SAP ecosystem can add complexities and raise costs for organizations not fully invested in SAP solutions. Ultimately, SAP Data Services excels in data transformation, scalability, and seamless SAP integration, making it a powerful choice for SAP-centric organizations with complex data needs and the resources to invest in its capabilities. However, its high cost, limited open source, and SAP dependence make it less suitable for budget-conscious organizations or those seeking greater platform flexibility and broader community support.

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Pros & Cons

Robust Data Transformations: Handles complex data cleansing, manipulation, and enrichment tasks with a wide range of built-in functions, ensuring data quality and accuracy for downstream analytics.
Tight SAP Integration: Seamlessly connects and transforms data within the SAP ecosystem, simplifying data flows and reducing integration complexity for SAP-centric organizations.
Scalable for Large Volumes: Efficiently handles high data volumes with parallel processing, data partitioning, and optimization techniques, ensuring smooth performance for growing data needs.
Visual Job Design: Intuitive drag-and-drop interface simplifies job creation and maintenance, making data integration accessible even for users with less technical expertise.
Centralized Governance: Provides centralized control and monitoring of data flows, ensuring data consistency, lineage tracking, and adherence to compliance regulations.

High Cost and Licensing: Requires separate licensing fees on top of existing SAP licenses, with costs scaling based on features, user licenses, and deployment options. Can be expensive compared to open-source or alternative data integration tools.
Steep Learning Curve: Mastering the visual job design and complex data transformations can require significant training and experience, especially for users unfamiliar with the platform.
Limited Open Source: Relies heavily on proprietary SAP technologies and lacks extensive open-source integrations, potentially restricting customization and community support compared to more open platforms.
SAP Ecosystem Dependence: Tight integration with the SAP ecosystem can limit flexibility and increase costs for organizations using other data sources or platforms.
Complex Job Maintenance: Managing and maintaining complex data flows with numerous transformations and dependencies can be challenging, requiring specialized expertise.

Key Features

Optimize Performance: Pushes down entire processing workflows into the source or target system for the best throughput. Partitions the data and processes it in parallel, fully independent streams.
Process Unstructured Data: Reveal data relationships and interactions – the who, what, when and how of text. Unlock unstructured text data insights through natural-language processing. Understand the meaning and context of information, not just the words.
Merge Datasets: Merges multiple incoming datasets into one output entity with the same schema as the incoming dataset. Performs the SQL UNION ALL operation through merge transform.
Maintain Data Quality: Gain employees’ and clients’ trust with reliable, accurate, unique data. Enforce data quality standards in real time and perform quality checks before analyzing and integrating data. Embed data duplication checks into workflows and systems. See beyond errors and inconsistencies to uncover a single version of the truth.
- Data Profiling: Cleanse and standardize data like names, addresses, emails, phone numbers and dates. Ensure consistency of key reference data used across the organization.
- Master Data Management: Integrates with SAP NetWeaver MDM to provide more cleansing and matching capabilities.

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Oracle Data Integrator

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^$0.09

Per OCPU, Per Hour

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Why We Picked Oracle Data Integrator

Oracle Data Integrator (ODI) receives mixed reviews, with users praising its intuitive interface, wide range of supported data sources, and robust data quality management features. However, some users find its learning curve steep and criticize its limited open-source community and high cost. Many users appreciate ODI's ease of use, particularly its drag-and-drop interface. One user noted, "ODI's intuitive interface made it easy to learn and use, even for someone with limited technical experience." This is a significant advantage compared to other ETL tools with steeper learning curves, like Informatica PowerCenter. ODI's wide range of pre-built connectors and support for various data sources is another highlight. "We were able to integrate data from a variety of sources, including databases, applications, and cloud platforms, without any major challenges," stated a user. This flexibility is crucial for modern businesses working with diverse data landscapes, especially compared to competitors like Talend which may require additional configurations for specific data sources. However, ODI's learning curve can be daunting for new users. One user commented, "It took me a while to feel comfortable using ODI, as I had to learn its specific terminology and concepts." Additionally, the limited open-source community can make it difficult to find answers or support online. "Compared to other ETL tools, the lack of a strong open-source community around ODI can be frustrating," noted a user. This is a disadvantage compared to open-source alternatives like Apache Airflow, which offer extensive online resources and communities. Another drawback is ODI's high cost. "The cost of ODI was a major concern for us, and we had to carefully consider our budget before making a decision," said a user. This high cost can be a deterrent for small and medium-sized businesses, particularly when compared to more cost-effective solutions like Pentaho Data Integration. Overall, ODI offers powerful data integration capabilities with a user-friendly interface and comprehensive data quality features. However, its steep learning curve, limited open-source community, and high cost can be significant drawbacks for some users. Ultimately, the decision of whether ODI is the right fit depends on individual needs and priorities.

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Pros & Cons

Easy to Use: Intuitive drag-and-drop interface simplifies data integration tasks, even for non-technical users.
Pre-built Connectors: Supports a wide range of data sources and targets, including databases, applications, and cloud platforms.
Scalable and Robust: Handles large data volumes and complex data integration processes efficiently.
Data Quality Management: Built-in features for data cleansing, validation, and transformation ensure data accuracy.
Workflow Automation: Schedule and automate data integration tasks for timely data delivery.
Security and Governance: Comprehensive security features and role-based access control ensure data privacy and compliance.

Steep Learning Curve: Mastering ODI's features and functionalities requires significant training and experience.
Limited Open-Source Community: Compared to other ETL tools, ODI has a smaller open-source community, which can lead to fewer resources and support.
High Cost: Oracle Data Integrator can be expensive to purchase and maintain, especially for small and medium-sized businesses.
Limited Cloud Support: While ODI supports cloud deployments, its cloud capabilities are not as mature as some other ETL tools.
Performance Bottlenecks: Complex mappings and large data volumes can lead to performance issues.

Key Features

Simple Design: Save on a separate ETL server and engine; transform complex datasets using only the source and target servers. Deploys E-LT architecture based on existing RDBMS engines and SQL. Uses database CPU and memory to run transformations.
- Service-Oriented Architecture (SOA): Consolidate databases, ERP and middleware in a single business solution by building a shared services layer with Oracle SOA Suite. Improve bulk data transfer performance, business optimization, process visibility and exception handling.
ODI Studio: Configure and manage ODI; administer the infrastructure, reverse engineer the metadata, develop projects, schedule, operate and monitor executions.
Administer Centrally: Set up production environments, manage and monitor run-time operations and diagnose errors with the ODI Enterprise Edition Console.
- Get read access to the metadata repository, and perform topology configuration and production operations through a web-based UI.
- Integrates with the Oracle Enterprise Manager Fusion Middleware Control Console for single-screen monitoring of data integration and Fusion Middleware components.
- Manage all ODI environment components from Oracle Enterprise Manager Cloud Control through the Management Pack.
Data Quality Firewall: Automatically detects and recycles faulty data before incorporating it in the target system – no need for programming. Follows the data integrity rules and constraints defined on the target platform and in ODI.

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Pentaho

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^$100

Monthly, Freemium

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Why We Picked Pentaho

Reviews of Pentaho paint a picture of a powerful, open-source data platform with both promise and pitfalls. Many users cite its wide range of tools and impressive scalability as major strengths, allowing them to tackle diverse tasks without needing multiple products. "It's a Swiss Army knife for data," one reviewer enthusiastically declared. But this power comes with a caveat – a steeper learning curve compared to more user-friendly options like Tableau. "It's not drag-and-drop intuitive," another user cautioned. Documentation is another point of contention. While some praise the available resources, others lament it as incomplete or outdated, often requiring community forums for troubleshooting. This is where the strong, active community becomes a saving grace – a true differentiator for Pentaho compared to pricier competitors. "The community is like having a built-in support team," a user noted, highlighting the value of shared knowledge and collaboration. However, users also report occasional bugs and glitches, especially in the free Community Edition. This can be a frustration for those seeking enterprise-level stability. And while Pentaho handles large datasets admirably, its resource-intensive nature can demand costly hardware upgrades, a factor to consider against competitors with built-in cloud options. Overall, Pentaho emerges as a versatile platform for those willing to invest time in learning its intricacies. Its open-source nature and powerful toolset make it a budget-friendly choice for startups and data-savvy teams. But for those prioritizing user-friendliness and seamless workflows, alternatives might be more appealing. Ultimately, the choice boils down to balancing Pentaho's strengths and weaknesses against your specific needs and technical expertise.

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Pros & Cons

Open-source and free core version: Makes Pentaho accessible to individuals and small teams, reducing initial investment costs.
Wide range of tools: Covers various data analysis needs, from basic reporting to advanced analytics, eliminating the need for multiple tools.
Scalable for large datasets: Handles growing data volumes efficiently, ensuring smooth performance for complex analyses.
Active community support: Provides valuable resources and troubleshooting assistance, especially for the open-source version.
Integration with various platforms: Connects seamlessly with existing data sources and BI tools, simplifying data workflows.

Steeper learning curve: Compared to user-friendly options, Pentaho's interface and features might require more technical expertise to master.
Limited documentation: While resources exist, some users find the documentation incomplete or outdated, hindering troubleshooting and advanced usage.
Occasional bugs and glitches: Users report encountering bugs and glitches, especially in the open-source version, potentially impacting data analysis workflows.
Resource-intensive: Large-scale data processing and complex analyses can demand powerful hardware, increasing infrastructure costs.
Limited customization options: While customization is possible, some users crave more flexibility and control over the platform's look and feel.

Key Features

Data Visualizations: Includes built-in tools and panel configurations. In-memory data caching aids speed-of-thought analysis on large data volumes. Understand and exclude outliers and drill down into supporting reports using visual lasso filtering and zooming.
Data Source: Build interactive analysis reports by using data from CSV files as well as relational and multidimensional data models.
Data Integration: Flexible data ingestion ensures no limitation in terms of data type or source that’s accessible. Provides Extract, Transform, and Load (ETL) capabilities to capture, cleanse and store data using a uniform and consistent format.
Reporting: View interactive reports in dashboards, with different capabilities such as column resizing and sorting, drag-and-drop report design, font selection, unlimited undo and redo functionality, and more. Export formats include HTML, PDF, CSV, Excel and Excel 93-2003.
OLAP Analytics: Mondrian, an open-source business analytics engine, enables interactive data analysis in real time. Build business intelligence solutions as an Online Analytical Processing (OLAP) engine, enabling multidimensional queries against business data using the MDX query language.
Data Modeling: Maps the physical structure of the database into a logical business model and Streamlined Data Refinery (SDR) using a relational data model. Helps augment and blend raw data through a request form to then be published.
Data Transformation: Design transformations and jobs to run with a graphical user interface, executed in XML or in a database repository. A data transformation engine reads, writes and manipulates data to and from various sources.
Role-Based Security: Restricts access to certain portions of a metadata model that are used as a data source. Offers table, column and row-level authorization control.
Mobility: Get immediate access to business analysis at any time using a mobile app for iPad that uses touch navigation technology.
Big Data Sources: Supports more than 15 big data sources such as Microsoft, Google Cloud, Apache Hive, MAPR and more.
Data Model Integration: Integrate third-party models like R, Python, Scala with Spark MLlib and Weka into data flows.
Integrations with Data Processing Distributions: Integrates Hadoop (and its distributions), NoSQL stores, log files, and JSON and XML data formats.
Customization: A highly-customizable, web-based UI and its API integrations ensure complete control over look, feel and function.
Tailored Training: Access architect-level staff with a proven track record of success with hundreds of customers.

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Dataflow

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^$1

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Why We Picked Dataflow

Dataflow, a cloud-based streaming analytics platform, garners praise for its ease of use, scalability, and cost-effectiveness. Users, particularly those new to streaming analytics or with limited coding experience, appreciate the intuitive interface and visual pipeline building, making it a breeze to get started compared to competitors that require more programming expertise. Additionally, Dataflow's serverless architecture and pay-as-you-go model are highly attractive, eliminating infrastructure management burdens and aligning costs with actual data processing needs, unlike some competitors with fixed costs or complex pricing structures.

However, Dataflow isn't without its drawbacks. Some users find it less customizable than competing solutions, potentially limiting its suitability for highly specific use cases. Occasional processing delays, especially for intricate pipelines or high data volumes, can also be a concern, impacting real-time analytics capabilities. Furthermore, while Dataflow integrates well with other Google Cloud services, this tight coupling can restrict portability to other cloud platforms, something competitors with broader cloud compatibility might offer.

Ultimately, Dataflow's strengths in user-friendliness, scalability, and cost-effectiveness make it a compelling choice for those new to streaming analytics or seeking a flexible, cost-conscious solution. However, its limitations in customization and potential processing delays might necessitate exploring alternatives for highly specialized use cases or mission-critical, real-time analytics.

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Pros & Cons

Ease of use: Users consistently praise Dataflow's intuitive interface, drag-and-drop pipeline building, and visual representations of data flows, making it accessible even for those without extensive coding experience.
Cost-effectiveness: Dataflow's pay-as-you-go model is highly appealing, as users only pay for the compute resources they actually use, aligning costs with data processing needs and avoiding upfront infrastructure investments.
Serverless architecture: Users appreciate Dataflow's ability to automatically scale resources based on workload, eliminating the need for manual provisioning and management of servers, reducing operational overhead and streamlining data processing.
Scalability: Dataflow's ability to seamlessly handle massive data volumes and fluctuating traffic patterns is highly valued by users, ensuring reliable performance even during peak usage periods or when dealing with large datasets.
Integration with other cloud services: Users find Dataflow's integration with other cloud services, such as storage, BigQuery, and machine learning tools, to be a significant advantage, enabling the creation of comprehensive data pipelines and analytics workflows within a unified ecosystem.

Limited customization: Some users express constraints in tailoring certain aspects of Dataflow's behavior to precisely match specific use cases, potentially requiring workarounds or compromises.
Occasional processing delays: While generally efficient, users have reported occasional delays in processing, especially with complex pipelines or during periods of high data volume, which could impact real-time analytics.
Learning curve for complex pipelines: Building intricate Dataflow pipelines can involve a steeper learning curve, especially for those less familiar with Apache Beam concepts or distributed data processing principles.
Dependency on other cloud services: Dataflow's seamless integration with other cloud services is also seen as a potential drawback by some users, as it can increase vendor lock-in and limit portability across different cloud platforms.
Need for more built-in templates: Users often request a wider range of pre-built templates and integrations with external data sources to accelerate pipeline development and streamline common use cases.

Key Features

Pipeline Authoring: Build data processing workflows with ML capabilities through Google’s Vertex AI Notebooks and deploy with the Dataflow runner. Design Apache Beam pipelines in a read-eval-print-loop (REVL) workflow.
- Templates: Run data processing tasks with Google-provided templates. Package the pipeline into a Docker image, then save as a Flex template in Cloud Storage to reuse and share with others.
Streaming Analytics: Join streaming data from publish/subscribe (Pub/Sub) messaging systems with files in Cloud Storage and tables in BigQuery. Build real-time dashboards with Google Sheets and other BI tools.
Workload Optimization: Automatically partitions data inputs and consistently rebalances for optimal performance. Reduces the impact of hot keys on pipeline functioning.
- Horizontal Autoscaling: Automatically chooses and reallocates the number of worker instances required to run the job.
- Task Shuffling: Moves pipeline tasks out of the worker VMs into the backend, separating compute from state storage.
Security: Turn off public IPs; secure data with a customer-managed encryption key (CMEK). Mitigate the risk of data exfiltration by integrating with VPC Service Controls.
Pipeline Monitoring: Monitor job status, view execution details and receive result updates through the monitoring or command-line interface. Troubleshoot batch and streaming pipelines with inline monitoring. Set alerts for exceptions like stale data and high system latency.

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Azure Data Factory

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^$0.07

Per DIU Hour

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Why We Picked Azure Data Factory

Overall, user reviews of Azure Data Factory (ADF) paint a picture of a powerful and versatile data integration tool with both strengths and limitations. Many users praise its ease of use, particularly the drag-and-drop interface and pre-built connectors, which significantly simplify ETL/ELT tasks even for complex scenarios. This is especially valuable for reducing development time and making data pipelines accessible to users with less coding expertise.

Another major advantage highlighted by users is faster time to insights. Streamlined data pipelines in ADF lead to quicker data availability for analysis, enabling data-driven decision making with minimal delay. Additionally, the pay-as-you-go pricing model and built-in optimization features are appreciated for helping users control costs. This is particularly important for organizations with fluctuating data volumes or unpredictable usage patterns.

However, some limitations also emerge from user reviews. Debugging complex pipelines can be challenging due to the lack of advanced debugging tools and reliance on basic logging. This can lead to frustration and lost time when troubleshooting issues. Additionally, the learning curve for data flows, while ultimately powerful, can hinder adoption for less technical users who might prefer a more code-centric approach.

Compared to similar products, ADF's strengths lie in its user-friendliness, scalability, and cost-effectiveness. Notably, its extensive library of pre-built connectors gives it an edge over some competitors in terms of out-of-the-box integration capabilities. However, other tools might offer more advanced debugging features or cater better to users with strong coding skills.

Ultimately, the decision of whether ADF is the right choice depends on individual needs and priorities. For organizations looking for a user-friendly, scalable, and cost-effective data integration solution, ADF is a strong contender. However, it's essential to consider its limitations, particularly around debugging and data flow learning curve, and compare it to alternative tools to ensure the best fit for specific requirements.

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Pros & Cons

Ease of Use for ETL/ELT Tasks: Users praise the intuitive drag-and-drop interface and pre-built connectors for simplifying data movement and transformation, even for complex ETL/ELT scenarios.
Faster Time to Insights: Many users highlight the improved data pipeline efficiency leading to quicker data availability for analysis and decision-making.
Cost Savings and Optimization: Pay-as-you-go pricing and built-in optimization features are frequently mentioned as helping users keep data integration costs under control.
Reduced Development Time: Code-free and low-code capabilities are appreciated for enabling faster pipeline development and reducing reliance on coding expertise.
Improved Data Governance: Unified data security and compliance across hybrid environments are valued by users dealing with sensitive data.

Limited Debugging Tools: Troubleshooting complex pipelines can be challenging due to lack of advanced debugging features and reliance on basic log analysis.
Cost Overruns: Unoptimized pipelines or unexpected usage spikes can lead to higher-than-anticipated costs in the pay-as-you-go model.
Learning Curve for Data Flows: The code-free data flow visual designer, while powerful, can have a learning curve for non-technical users, hindering adoption.
Azure Ecosystem Reliance: Integration with non-Azure services often requires workarounds or custom development, limiting flexibility.
Version Control Challenges: Lack of native version control features necessitates integration with external tools for effective pipeline management.

Key Features

Data Source Connectivity: Visually integrate data sources with more than 90 pre-defined connectors through guided workflows. Connect to Amazon Redshift, Google BigQuery, HDFS, Oracle Exadata, Teradata, Salesforce, Marketo and ServiceNow, and all Azure data services. View data previews and customize as needed.
Mapping Data Flow: Design code-free data transformation logic with an intuitive interface and visual tools. Schedule, control and monitor transformation tasks with easy point-and-click actions — the vendor manages code translation, path optimization and job runs at the back end.
Authoring: Drag and drop to create end-to-end data processing workflows – from ingestion to reporting. Operationalize the pipeline using Apache Hive, Apache Pig, Azure HDInsight, Apache Spark and Azure Databricks. Upload data to warehouses like Azure Storage, then connect to analytics platforms for visual insights and reporting.
Debugging: Debug the data pipeline as a whole or in parts — set breakpoints on specific workflows.
Data Processing: Set event and schedule-based triggers to kick off the pipelines. Scales with Azure Event Grid to run event-based processing after upstream operations are complete. Speeds up ML-based pipelines and retrains processes as new data comes in.

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SAS Data Management

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Why We Picked SAS Data Management

User reviews of SAS Data Management paint a nuanced picture. Fans praise its streamlined workflow, robust data quality tools, and scalability for handling massive datasets. They appreciate its seamless integration with various data sources and analytics platforms, enabling a holistic view and fostering trust in data-driven decisions. Regulatory compliance support is another major plus, offering peace of mind and reducing risks. However, critics point to the hefty price tag and complex licensing structures as major barriers, especially for smaller companies or budget-constrained projects. The steep learning curve can be daunting for new users, requiring dedicated training and potentially slowing down productivity. Limited open-source integration and a closed-ecosystem nature restrict flexibility and collaboration with external tools. The black-box nature of its algorithms can also make troubleshooting and debugging difficult. Some users feel locked in due to data dependencies and non-standard export formats, making transitioning to other solutions costly and cumbersome. Ultimately, SAS Data Management's strengths in robust data handling, scalability, and compliance shine for organizations with complex data needs and strict regulations. However, its high cost, limited open-source compatibility, and steep learning curve make it less ideal for smaller companies or those seeking greater flexibility and affordability. Users weighing options should carefully consider their specific needs and resources before making a decision.

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Pros & Cons

Streamlined Workflow: Simplifies data management tasks with drag-and-drop interface and automated processes, saving time and improving efficiency.
Robust Data Quality: Ensures data accuracy and consistency through comprehensive cleaning, validation, and transformation tools, fostering trust in data-driven decisions.
Scalability and Performance: Handles large datasets efficiently with parallel processing and optimized algorithms, enabling complex analyses without performance bottlenecks.
Extensive Integrations: Connects seamlessly with various data sources and analytics platforms, facilitating a holistic view of data across the organization.
Regulatory Compliance: Supports secure data governance and auditability for meeting industry regulations, providing peace of mind and reducing compliance risks.

Cost and Licensing: Requires significant upfront investment and ongoing licensing fees, making it less accessible to smaller organizations or budget-constrained projects.
Steep Learning Curve: Complex interface and proprietary language can be challenging for users without prior SAS experience, requiring dedicated training and support.
Limited Open-Source Integration: Primarily focused on its own ecosystem, with limited compatibility and integration with open-source tools and platforms.
Black-Box Nature: Limited transparency into internal algorithms and processes can make troubleshooting and debugging complex issues challenging.
Vendor Lock-in: Switching to other data management solutions can be difficult and costly due to data dependencies and lack of standard export formats.

Key Features

Integrated Development Environment (IDE): Access source systems virtually and create target structures. Manage processes with an intuitive, point-and-click, role-based GUI – import and export metadata functions and run ETL and ELT process flows. Supports interactive debugging and testing of jobs with full log access.
- Unified Architecture: Leverage the complete data pipeline — from data quality to data federation — in one platform. Ensure data transparency and accountability with auditing tools and source data lineage.
Process Designer: Build and update data management processes with a visual, end-to-end event designer. Control and run data integration tasks and fork jobs to execute in parallel. Run shell scripts by calling REST and SOAP web services.
Embeddable Data Quality: Access customizable business rules within batch, near-time and real-time processes and reuse as needed. Identify incomplete, ambiguous and inaccurate data with its interactive GUI. Get alerts for when data quality falls below acceptable standards. Supports data cleansing in native languages for more than 38 regions globally.
Data Transformation: Build data warehouses, data marts, and BI and analytic data stores by pulling data from multiple sources. Extract required data with more than 300 out-of-the-box SQL-based transforms. Reuse transform functions in different projects and environments through custom exits, message queues and web services.

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Buyer's Guide

ETL Software Is All About Boosting Business with Quality Data

By Ritinder Kaur, Senior Technical Content Writer at SelectHub

ETL Tools BG Intro Header

ETL platforms collect your digital business assets in a structured format for analysis and reporting. Most analytics and BI tools have built-in ETL capabilities. Whether you want end-to-end data management or a standalone ETL solution, assessing your requirements early on helps in software selection.

This guide includes the definition of ETL tools, benefits, features and current trends. It also covers a most popular products section to acquaint you with common features of ETL platforms.

Executive Summary

ETL tools give you a single source of truth to work with unique, accurate information.
Deciding whether to deploy on-premise or in the cloud depends on your business needs, infrastructure and budget.
The need for real-time insight makes live updates a must-have feature for many enterprises.
Automated ETL workflows keep information up-to-date without the need for manual intervention.
Advanced ETL features include machine learning algorithms and event-driven architecture.
Define your requirements by framing questions to ask within your organization and of the vendor.

What This Guide Covers:

What Is ETL Software?
Deployment Methods
Primary Benefits
Implementation Goals
Key Features & Functionality
Advanced Features
Industry Trends
Software Comparison
Cost & Pricing Considerations
Questions to Ask Yourself
Questions to Ask Vendors
In Conclusion

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What Is ETL Software?

ETL solutions pull business data from sources, transform it into a structured format and upload it to storage. Modern ETL systems populate warehouses by generating the extraction code automatically through workflow designers.

ETL includes the following steps:

Extraction: Drawing assets from various sources and storing them in a staging area.
Transformation: Mapping the data’s schema to the target storage and converting it into a compatible format.
Loading: Loading the data into the repository.

Modern ETL software ingests disparate asset types, including text, audio, video and streaming data. It surfaces undiscovered business opportunities and discrepancies that might impact operations. You can make more focused, data-driven strategies.

Deployment Methods

As new data types become available, you need a scalable, performant ETL solution to stay competitive. Will an on-premise solution scale with your business, or will you need to migrate to the cloud? Each deployment model has its pros and cons.

It’s a good idea to assess what you’re willing to compromise on and which attribute is a must-have for your organization.

On-premise

Deploying on premise frees you from internet dependence and connectivity issues. Opting for a perpetual license with a one-time, lump-sum payment is cost-effective compared to an annual subscription which might get pricier over time.

Enforcing governance and security protocols is your responsibility, as is maintenance, though you have greater control over your data.

However, deployment can be effort-intensive and time-consuming. Infrastructure and training costs can add up, making it an expensive option. Scaling the system with industry-grade security and governance protocols might cost extra.

Cloud-based

You can opt to self-host on a private cloud or subscribe to a software-as-a-service version. If you’re considering a hosted version, ask if the vendor will provide a sole or shared instance. Sharing computing and storage resources is cost-effective, though it might impact performance. On the other hand, a single-tenant option can be expensive.

Cloud systems have a low cost of entry since pricing is often subscription-based and includes maintenance and support. It makes cloud software an attractive option for small enterprises. Implementation is painless as maintenance, troubleshooting and upgrades are on the vendor.

Cloud systems are scalable with your business and available on all internet-connected devices.

But, connectivity issues can cause the software to lag. Deploying on the public cloud forces you to rely on the vendor’s servers. The business might suffer if the servers go down or the vendor implements downtime for upgrades or patch fixes.

Though a monthly subscription seems affordable, costs can stack up quickly with additional licenses, modules and upgrades. Large enterprises with the resources to pick and choose might find on-premises deployment a viable option.

Security and performance are primary concerns with cloud-based systems, while on-premise systems are resource-intensive. Vendors offer an in-between solution with the best attributes of the two — hybrid cloud — and many enterprises are opting for it.

Hybrid Cloud

A hybrid cloud solution allows storage, backup, compliance and security on private servers while giving you access to cloud-native capabilities. A single interface lets you manage the cloud and on-premises components while an orchestration element joins the two.

It’s ideal for enterprises seeking to make their legacy systems work. Many organizations find the hybrid cloud a convenient first step to migrating to the cloud entirely.

Not sure which deployment model will fit your needs? Receive advice from the experts for greater clarity.

Expert recommendations and analysis on the top ETL Tools

Benefits

Data accessibility, or lack of it, can make or break your company, and ETL software fills this gap.

Let’s look at other ways in which it benefits your business.

ETL Benefits

Centralize Digital Assets

Previously, sources were few, but establishing connections through manual code took days, even weeks. Thanks to modern connectors, ETL tools pull information from multiple sources in less time. These assets are stored in a centralized repository and serve as the final source of truth, helping you stay competitive.

Get Faster Insights

Parallel processing helps manage large asset volumes in little time. Get access to the latest, accurate insight by scheduling automatic data refreshes or updating information manually with a single click. Fact-based decisions enhance performance and position your company to take advantage of business opportunities.

Manage Big Data

ETL software supports complex filters, conditions, parameterization and aggregation. It speeds up transformation by reusing data maps, irrespective of the underlying assets. Built-in error handling empowers developers to build operationally resilient integration solutions.

Integrate With BI Tools

Flexibility and interoperability ensure your ETL tool works well with other data integration framework components, including your company’s software and hardware. A central metadata repository facilitates integrating with other systems, including BI tools. Metadata includes data definitions, models for target databases and source-to-target transformation rules.

Expert recommendations and analysis on the top ETL Tools

Implementation Goals

What do you hope to achieve by implementing ETL? Though every business might have a different answer to this question, here are some common implementation goals to get you started.

Goal 1 Stay Competitive	You want to boost revenue. You hope to outperform others in the market. You wish to prepare for opportunities and eventualities in advance.
Goal 2 Track Business Performance	You want to know how your business is doing every day. You require accurate month-end financial reports. You need to monitor employee performance to identify improvement areas.
Goal 3 Make Data-Backed Decisions	You want the ETL software to manage your large, complex datasets. You wish to plan ahead with accurate, up-to-date, easy-to-read reports. You want to base your decisions on hardcore figures, not hunches.
Goal 4 Centralize ETL Processes	You want to view all ETL processes on a centralized interface. Administrators should have the right to manage all sources and ETL workflows centrally. Creating automated workflows should be easy. User access to the central console should be role-specific.

Including implementation goals when assessing ETL platforms will help you narrow down your options.

Expert recommendations and analysis on the top ETL Tools

Key Features & Functionality

Identifying your essential requirements at the onset sets a sound foundation for your software search.

Here are some basic features of ETL software.

Source Connectivity	Connect to your organization's file formats, databases, CRMs, ERPs, and other solutions. Pull information from text, CSV, Excel and XML files and applications like Salesforce, HubSpot, etc.
Self-Service Data Management	Democratize data extraction and transformation through no-code ETL. Blend complex data volumes and process them at scale on a visual interface. Anyone can learn how to build a data pipeline in minutes with automated workflow templates.
Data Preparation	Cleanse, sort, group and migrate large volumes of data across systems and warehouses. Incremental transformation is a lightweight updating technique that changes only new, unchanged data to align with the warehouse schema and requires little ETL overhead. Ask for it when talking to potential vendors.
Reporting	Get periodic ETL job status reports or generate them ad hoc. Set alert notifications for incomplete jobs, halted workflows and other errors and discrepancies.
Quality Management	Maintain information in complete sync irrespective of how the updates happen, whether in batches or real time. Ensure accurate and reliable data with an intricate network of connectors and databases.
Security	Secure data while conveying it across systems and networks with encryption in motion and at rest. Choose an ETL solution that complies with current security regulations like GDPR, SOC II, CCPA and HIPAA.

Expert recommendations and analysis on the top ETL Tools

Advanced Features

If you seek advanced features for your ETL tool, reach out to the vendor to learn what it will cost. If starting from scratch, select a tool you can scale later by adding advanced attributes.

Here’s a ready reckoner.

Real-Time ETL	Get real-time updates, especially if your industry deals with time-critical use cases, like booking systems, point-of-sale terminals or healthcare-related workflows. Make faster decisions with continuous real-time data integration through container-based ETL. Deploy a distributed, scalable, near real-time ETL environment using J2EE technology.
Event-Driven Architecture	Ingest streaming data from Amazon Redshift, Snowflake, Google BigQuery, Google Analytics, Salesforce, SAP, social media platforms, etc. Enrich the data with externally sourced information through ML-driven algorithms. Traditional ETL systems can’t process this data, while few modern tools can. They have loosely coupled data sources and consumer systems. When streaming systems publish new events, consumer modules connect to them and populate the warehouse accordingly.
Automation	Schedule transformation tasks and complex workflows through built-in job scheduling. Automate repetitive tasks, freeing up resources to focus on effort-intensive tasks. Artificial intelligence automates the ETL process — there’s no need to enter information into the database manually. Stay updated about how the ETL jobs are performing with live notifications.
Pre-Built Transformations	Convert data to a usable format with little or no technical skills. Deliver information faster by simplifying complex data changes with built-in transformations.

Expert recommendations and analysis on the top ETL Tools

Industry Trends

Staying informed about current trends can impact your software purchase decision. If you’re planning to scale the business, which popular functionality should you factor in today?

Here’s a list of ETL software trends.

ETL Trends

Data Literacy

Enterprises seek faster time to insight, hence the push for data literacy. Business owners want everyone to know how to manipulate, visualize and analyze data. Vendors align their offerings to include an intuitive interface and a centralized task management console.

User-friendly actions like drag and drop, selecting and clicking, etc., let you autonomously manage ETL processes. The learning curve is shorter, and organizations save time on employee training. You can start building entire data pipelines within days after deploying a self-service ETL solution.

Greater autonomy requires guardrails, and role-based access restrictions and audit trails ensure accountability through transparency. With the industry's focus on reducing the time to market, this trend is likely to evolve.

Cloud Migration

Anywhere accessibility, interoperability and pay-as-you-go models make the cloud an attractive option. Statista predicts investment in global cloud IT infrastructure to from $90 billion in 2022 to $133.7 billion by 2026.

ETL Cloud Software Market Growth Chart

Security is a primary enterprise concern when considering cloud migration. However, studies say it isn’t so much the data’s location as the origin of malicious intent that puts your assets at risk. 34% of data breaches happen through internal theft and twenty seven percent through accidental data leaks.

Cloud software vendors provide compliance with industry-grade security standards like GDPR, CCPA, HIPAA, SOC 2, etc. Cloud ETL systems are enterprises’ first choice when migrating their digital assets, and this trend is likely to stay.

Automation

It executes all ETL steps in the correct sequence without error-prone manual intervention. Automated ETL workflows support software development on two fronts — app and database updates.

Did you know that Amazon deploys updated code to production every 11 seconds, while Netflix does it at least a thousand times a day? ML-based automated data preprocessing improves data quality before transforms happen.

Mapping extract, transform and load processes to an ETL tool and invoking the automated script through the command line can set these processes in motion. Automated ETL manages warehouse functions, coordinating operations across applications and databases.

Database release automation keeps your repositories updated. When developers commit the database changes to version control, the ETL tool initiates the integration process, and the data warehouse reflects the changes. It helps enterprise reporting tools generate real-time reports.

Automated ETL speeds up data availability and is likely to be a must-have feature on enterprises' lists for the foreseeable future.

Modern ETL

Traditional ETL systems are monolithic, rigid and unable to adapt to large data volumes. They support batch processing of structured and semi-structured data only. Efficient metadata management and compliance are often lacking, and vendor lock-in and license costs make legacy systems a liability.

Support for unstructured data, complex transformations and real-time processing is essential to use proprietary data to its fullest potential. Adopting ETL modernization techniques is a way for enterprises to make it happen.

These include big data pipelines using Apache Spark/Azure Databricks, containers and serverless platforms, and ELT. The extract, load and transform technique involves loading the assets into the warehouse and processing only the required data when necessary.

Your ETL modernization strategy should align with your digital and IT roadmap, deployment framework and skill levels.

Check out our Business Intelligence Trends article to learn more about the latest, most relevant trends.

Expert recommendations and analysis on the top ETL Tools

Software Comparison

You should have enough information now to start making your own requirements checklist. Or get our free requirements template to get started. Some businesses require batch processing only, and a traditional ETL tool can serve their needs. However, if you need live updates, you’ll need an ETL tool with streaming data capture, especially if you deal with big data.

Contacting colleagues and industry peers is a great way to get tried-and-tested product references. Compare and contrast the features of various tools with our comparison matrix.

Cost & Pricing Considerations

Product costs will vary depending on the deployment model you choose.

If planning to deploy on-premise, you can opt for a one-off purchase or recurring payment model with capacity pricing. When calculating the cost of ownership, factor in infrastructure deployment, maintenance and technical overheads.

Include the cost of add-ons and additional features if you’re considering a cloud-based solution.

The total cost of ownership may include but is not limited to the below considerations:

Deployment support
Upgrades
Maintenance
Add-ons
Customization
Training

The best ETL tool for you will be a solution that meets your business requirements without breaking the bank.

Expert recommendations and analysis on the top ETL Tools

Questions to Ask Yourself

ETL Questions to Ask

Use these questions as a starting point for internal conversations:

Which deployment option will you prefer — on-premises, cloud or hybrid cloud?
Which are your preferred data sources?
Who will use the solution? Will they require training?
Will your business need streaming data?
How important are self-service capabilities for your organization?

Questions to Ask Software Vendors

Use these questions as a starting point for conversations with vendors:

About the Software

Is the software compatible with your legacy systems?
Can you build data pipelines autonomously?
Does it comply with HIPAA, GDPR and SOC II?
Does it integrate with big data sources?
Is automation available?
What are the pricing plans? What features and customization options will cost extra?
Is it easy to use?

About the Vendor

Does the vendor specialize in ETL software?
How often do they release updates?
Is support included, and what are the available tiers? If not, how much will it cost?
What is the learning curve like? Is training included?
Which security protocols do they have in place?

In Conclusion

Selecting an ETL tool needs careful thought and lots of research. This buyer’s guide is a primer for IT professionals looking for the right product for their organization.

About The Contributors

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

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.

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.

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.

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.