SAP Data Services vs InfoSphere Information Server

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

Product Basics

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
read more...
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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$10,000 Annually
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Product Assistance

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Email
Phone
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FAQ
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Knowledge Base
24/7 Live Support
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24/7 Live Support

Product Insights

  • Maximize ROI: Streamline processes and maximize efficiency. Access data where it’s stored, load and move it while ensuring optimal speed and performance. Unlock contextual insights by transforming data. 
  • Source Disparate Data: Draws data from files, XML, relational databases, web services and mainframes. Connects to big data, cloud and NoSQL systems including Amazon Web Services, Google Cloud Platform, Microsoft Azure Marketplace, SAP Cloud Platform, Vertica, MongoDB, Apache Spark on Apache Hive, Teradata and Hadoop. Aggregates unstructured data like text from Adobe PDFs, Microsoft Word, Outlook, Excel and more. 
  • Deploy Anywhere: Implement on-premise, in the cloud or go hybrid. Move existing content and assets to and from SAP Data Intelligence Cloud and use them across the enterprise. 
  • Permissions Management: Uses the Central Management Server (CMS) for users’ accounts and permissions management. Add and remove team members’ rights and manage access to repositories on a per individual basis. 
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  • 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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  • 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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  • 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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Product Ranking

#29

among all
ETL Tools

#32

among all
ETL Tools

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Analyst Rating Summary

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

Analyst Ratings for Functional Requirements Customize This Data Customize This Data

SAP Data Services
InfoSphere Information Server
+ Add Product + Add Product
Data Delivery Data Quality Data Sources And Targets Connectivity Data Transformation Metadata Management Platform Capabilities Workflow Management 100 92 89 100 93 100 96 100 99 94 95 100 0 100 0 25 50 75 100
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80%
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we're gathering data
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Analyst Ratings for Technical Requirements Customize This Data Customize This Data

100%
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we're gathering data
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91%
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User Sentiment Summary

Great User Sentiment 112 reviews
Good User Sentiment 80 reviews
80%
of users recommend this product

SAP Data Services has a 'great' User Satisfaction Rating of 80% when considering 112 user reviews from 3 recognized software review sites.

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.

3.9 (31)
4.0 (21)
4.1 (54)
4.6 (27)
4.0 (27)
3.1 (32)

Awards

SelectHub research analysts have evaluated SAP Data Services and concluded it earns best-in-class honors for Data Transformation.

Data Transformation Award

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

Workflow Management Award

Synopsis of User Ratings and Reviews

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.
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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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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.
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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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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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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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