InfoSphere Information Server vs Oracle Data Integrator

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Our analysts compared InfoSphere Information Server vs Oracle Data Integrator based on data from our 400+ point analysis of ETL Tools, user reviews and our own crowdsourced data from our free software selection platform.

Oracle Data Integrator Software Tool

Product Basics

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
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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
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$20,000 Annually
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$0.09/OCPU, /Hour
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Product Assistance

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Product Insights

  • Unified Data Landscape: Break down data silos and seamlessly integrate information from diverse sources, including databases, applications, and cloud platforms, providing a holistic view for improved decision-making.
  • Enhanced Data Quality: Ensure data accuracy and consistency through powerful cleansing, standardization, and transformation tools, minimizing errors and boosting trust in your data assets.
  • Streamlined Data Movement: Automate and orchestrate data movement using flexible ETL/ELT workflows, speeding up data delivery and reducing manual effort for critical business processes.
  • Powerful Data Transformation: Manipulate and enrich data to meet specific needs through a comprehensive set of transformation functions, unlocking its full potential for analytics and reporting.
  • Scalable and Secure Architecture: Handle large data volumes and complex integrations with confidence thanks to a robust and secure architecture, ensuring uninterrupted data flow and safeguarding sensitive information.
  • Improved Operational Efficiency: Automate routine data tasks, reduce IT workload, and optimize resource utilization, freeing up staff and resources for higher-value activities.
  • Enhanced Data Governance: Implement data governance policies and ensure compliance with regulations through features like lineage tracking and access controls, fostering data transparency and responsible usage.
  • Greater Business Agility: Respond rapidly to changing data needs and support new initiatives with agile data integration capabilities, empowering faster time-to-market and increased business flexibility.
  • Improved Collaboration and Decision-Making: Foster greater collaboration across teams by providing everyone with access to reliable and consistent data, leading to better informed decisions and data-driven strategies.
  • Reduced Integration Costs: Streamline data management processes, eliminate data redundancies, and optimize infrastructure usage by consolidating data integration needs into a single platform, potentially leading to cost savings.
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  • Maximize ROI: Reduces infrastructure costs by eliminating the need for an ETL server and engine. Save on labor costs with a smaller learning curve and reduce TCO with lower development costs. 
  • Integrate Disparate Data: Supports all RDBMS like Oracle, Exadata, Teradata, IBM DB2, Netezza, Sybase IQ, ERPs, LDAP, XML and flat files, among others. 
  • Deploy Faster: Enhance user experience and developer productivity with a flow-based declarative user interface. Enables developers to focus on describing what’s to be done visually, with data architects defining processes and executing data integration separately. Shorten implementation times and simplify maintenance. 
  • Map Big Data: Transform large, complex data sets by leveraging its flexible and highly performant architecture. Generate Apache Spark code as per big data standards, with native support for big data and parallel processing. 
  • Access Data 24*7: Scales as the data grows with clustered deployments for high availability. Optimizes workloads with JDBC connection pooling, load balancing and a connection retry mechanism to recover failed sessions. 
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  • 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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  • 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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Product Ranking

#32

among all
ETL Tools

#31

among all
ETL Tools

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Analyst Rating Summary

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Data Delivery
Metadata Management
Platform Security
Workflow Management
Data Quality
Data Delivery
Data Quality
Metadata Management
Performance and Scalability
Platform Capabilities

Analyst Ratings for Functional Requirements Customize This Data Customize This Data

InfoSphere Information Server
Oracle Data Integrator
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Data Delivery Data Quality Data Sources And Targets Connectivity Data Transformation Metadata Management Platform Capabilities Workflow Management 100 99 94 95 100 0 100 100 100 88 96 100 100 89 0 25 50 75 100
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Analyst Ratings for Technical Requirements Customize This Data Customize This Data

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User Sentiment Summary

Good User Sentiment 80 reviews
Great User Sentiment 243 reviews
77%
of users recommend this product

InfoSphere Information Server has a 'good' User Satisfaction Rating of 77% when considering 80 user reviews from 3 recognized software review sites.

81%
of users recommend this product

Oracle Data Integrator has a 'great' User Satisfaction Rating of 81% when considering 243 user reviews from 5 recognized software review sites.

4.0 (21)
4.0 (17)
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4.39 (18)
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4.4 (18)
4.6 (27)
4.2 (69)
3.1 (32)
3.9 (121)

Awards

SelectHub research analysts have evaluated InfoSphere Information Server and concluded it earns best-in-class honors for Workflow Management.

Workflow Management Award

we're gathering data

Synopsis of User Ratings and Reviews

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.
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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.
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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.
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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.
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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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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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Screenshots

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