SQL Server Integration Services vs InfoSphere Information Server

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Our analysts compared SQL Server Integration 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

SQL Server Integration Services (SSIS) is a data integration tool built within Microsoft SQL Server. It excels at orchestrating data movement and transformation tasks between diverse sources and destinations, making it ideal for data warehousing, ETL (Extract, Transform, Load) processes, and database management in Windows environments. Users praise its visual workflow editor, robust data transformation capabilities, and seamless integration with other Microsoft tools. Additionally, SSIS offers built-in security features and scalability for handling large datasets. However, its reliance on the Microsoft ecosystem, limited open-source compatibility, and potentially complex learning curve can be drawbacks. Pricing is part of the SQL Server license, ranging from affordable Express editions to more expensive Enterprise versions depending on user needs and server configurations. Overall, SSIS is a powerful and cost-effective option for organizations heavily invested in the Microsoft suite and primarily focused on Windows server environments. For those seeking open-source flexibility or broader platform compatibility, alternative data integration tools may be worth exploring.

Pros:
  • Visual workflow
  • Strong data transformations
  • Microsoft integration
  • Built-in security
  • Scalable for large volumes
Cons:
  • Windows only
  • Limited open source
  • Steep learning curve
  • Pricing with SQL Server
  • Closed-source ecosystem
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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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Product Assistance

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Product Insights

  • Maximize ROI: Performs complex transformations without needing a staging area. Persists data temporarily to a native raw format file using the file system. 
  • Connect to Data Sources: Draws data from Microsoft SQL Server, IBM DB2, Oracle, HP Vertica, MySQL, MongoDB and OData. Pull data from repositories that can’t be sourced directly like FTP, HTTP, MSMQ, Analysis Services and Server Management Objects (SMO). 
  • Integrate with SAP Products: Natively accesses the application model and relevant metadata in SAP Business Suite offerings. Reads data based on programming languages such as ABAP, IDocs, BAPI, RFC and SAP extractors. 
  • Extend As Needed: Write code to define connection objects, log providers, transforms and tasks. 
  • Maintain Data Quality: Handles data from heterogeneous data sources within the same package. Monitor errors through a variety of logging and auditing options. 
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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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  • Big Data Support: Connects to new, in-demand big data sources like databases, systems, files and unstructured content. Access mainframe sources and captured data changes in real time. Expand cloud support for big data with Microsoft Azure, Impala, Cassandra, OData and Apache Hive.
  • Import/Export Wizard: Move data from a variety of source types to disparate destination types, including text files and other SQL Server instances. Create packages that move data across systems seamlessly, without transformations. 
  • Build Integration Packages: Create and maintain integration packages through the SSIS Designer. Deploy the package and view the execution status at run time. Add functionality to packages through dialog boxes and windows. Configure the development environment through SQL Server Data Tools (SSDT). 
  • Built-in Data Transformations: Provides aggregation, pivot, unpivot, cache transform, fuzzy lookup, data conversion, data mining query and partition processing. Leverage a wide range of transform capabilities like fuzzy logic, data profiling, data and text mining and direct insert to SSAS. 
  • Secure Business Data: Provides threat and vulnerability mitigation, and access control. Sign packages with digital certificates that ensure customers open and run packages only from trusted sources. 
  • Precedence Constraints: Control task runs by defining precedence constraints. Connect tasks to control the workflow and configure to work based on an SSIS expression or the status of the preceding job. 
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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

#8

among all
ETL Tools

#32

among all
ETL Tools

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Analyst Rating Summary

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Data Quality
Data Transformation
Platform Security
Metadata Management
Workflow Management
Data Delivery
Metadata Management
Platform Security
Workflow Management
Data Quality

Analyst Ratings for Functional Requirements Customize This Data Customize This Data

SQL Server Integration 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 88 100 76 100 93 0 91 100 99 94 95 100 0 100 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

Great User Sentiment 503 reviews
Good User Sentiment 80 reviews
84%
of users recommend this product

SQL Server Integration Services has a 'great' User Satisfaction Rating of 84% when considering 503 user reviews from 2 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.

n/a
4.0 (21)
4.3 (279)
4.6 (27)
4.1 (224)
3.1 (32)

Awards

SelectHub research analysts have evaluated SQL Server Integration 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

Visual Workflow: Drag-and-drop interface simplifies complex data flows, making integration tasks intuitive and manageable, even for users without extensive coding experience.
Robust Data Transformations: Cleanses, transforms, and validates data to ensure accuracy and consistency before integration, improving data quality and trust in downstream analytics.
Microsoft Integration: Seamlessly integrates with other Microsoft tools and platforms like SQL Server and Azure, streamlining data workflows within existing infrastructure and reducing the need for additional software.
Scalability and Performance: Handles large datasets efficiently with parallel processing and optimization techniques, minimizing processing time and ensuring smooth data integration for growing data volumes.
Built-in Security: Supports encryption, data masking, and role-based access controls for secure data handling and compliance with industry regulations, providing peace of mind and reducing security risks.
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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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Limited Open Source: Relies heavily on Microsoft technologies and lacks extensive open-source integrations, potentially restricting customization and community support compared to more open platforms.
Steep Learning Curve: While the visual interface is helpful, mastering complex data flows and transformations can require significant training and experience, especially for users unfamiliar with the platform.
Windows Only: Limited to Windows environments, excluding non-Microsoft operating systems like Linux or macOS, hindering platform flexibility and potentially requiring additional infrastructure investment.
Closed-Source Ecosystem: Limited transparency into internal algorithms and processes can make troubleshooting and debugging complex issues challenging, requiring specialized knowledge or relying on Microsoft support.
Cost Tied to SQL Server: Pricing depends on the chosen SQL Server edition, potentially increasing costs for organizations already invested in other database solutions or needing only basic data integration functionalities.
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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 SQL Server Integration Services paint a contrasting picture. Proponents praise its intuitive visual workflow, robust data transformation capabilities, and seamless integration with the Microsoft ecosystem. This makes it ideal for organizations already invested in Microsoft tools and requiring efficient data movement within Windows environments. The built-in security features and scalability for handling large datasets are further pluses, offering peace of mind and ensuring smooth performance for growing data volumes. However, critics point to its heavy reliance on Microsoft technologies and limited open-source compatibility as major drawbacks. This can restrict customization and community support compared to more open platforms like Talend or Apache Airflow. The steep learning curve and Windows-only limitation can also be hurdles, requiring dedicated training and potentially hindering platform flexibility. Additionally, the closed-source nature can make troubleshooting complex issues challenging. Finally, pricing tied to SQL Server editions may not be cost-effective for organizations needing only basic data integration functionalities or using other database solutions. Ultimately, SQL Server Integration Services shines for its robust data handling, intuitiveness, and Microsoft integration within Windows environments. However, its limited open-source compatibility, steep learning curve, and reliance on SQL Server licensing make it less ideal for organizations seeking greater flexibility, affordability, or platform independence. Carefully weighing your specific needs and resources against its strengths and limitations is crucial before choosing SSIS for your data integration needs.

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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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Screenshots

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