Informatica PowerCenter vs SAS Data Management

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Our analysts compared Informatica PowerCenter vs SAS Data Management based on data from our 400+ point analysis of ETL Tools, user reviews and our own crowdsourced data from our free software selection platform.

SAS Data Management Software Tool

Product Basics

Informatica PowerCenter is data integration software for enterprises of all sizes with connectors and metadata. It relies on robust business logic to deliver data on demand.

For cloud users, the vendor offers connectivity to Amazon S3, Microsoft Azure Blob Storage and Google Cloud Storage. It also reads data residing on mainframe systems using PowerCenterExchange and connects to big data platforms like Hadoop and HDFS.

The vendor enables businesses to move PowerCenter loads to the cloud. What's more, AI and automation can drive 95% of cloud upgrades if businesses opt for them. It scales to validate data quality, perform fuzzy matches and identify data anomalies in the cloud.

Pros
  • User-friendly
  • Wide range of connectors
  • Scalable and reliable
  • Robust security features
  • Integrates with Informatica suite
Cons
  • Steeper learning curve
  • Can be expensive
  • Performance issues with large datasets
  • Limited cloud app support
  • Complex to manage
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SAS Data Management empowers organizations to wrangle their data, from ingestion and cleansing to transformation and governance. It excels at handling mountains of complex data, making it ideal for industries like finance, healthcare, and government. Key benefits include streamlined data integration, enhanced data quality, and robust security controls. Popular features include its drag-and-drop interface, automation capabilities, and advanced analytics tools. User experiences suggest it can be powerful for data wranglers but might have a steeper learning curve compared to simpler options. Pricing is typically per seat or core, with annual subscriptions or perpetual licenses available.

Pros
  • Robust for large datasets
  • Scalable & high performance
  • Advanced data manipulation
  • Automate complex tasks
  • Strong statistical analysis
Cons
  • Steep learning curve
  • Costly licensing & maintenance
  • Limited visual analytics
  • Not user-friendly interface
  • Difficult to debug code
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$5,000 Annually
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Tailored to your specific needs
$300 Monthly
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Tailored to your specific needs
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Product Assistance

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

Product Insights

  • Automate Workflows: Ditch the manual grind and leave the nitty-gritty to automation. Once set up, it performs ETL without needing the IT team.
  • Accelerate Insight: Make informed decisions based on hardcore data. Readymade connectors and a graphical UI speed up otherwise complex data integration tasks.
  • Reduce Costs: Cut the overheads due to bad data and grow your business with accurate and reliable insight. Minimize errors and related costs, thanks to its inherent data quality features. Reduce testing time by 50 to 90 percent with automation.
  • Improve Outcomes: Grab opportunities with confidence and turn them into profit-making ventures. Thanks to data quality checks, it pushes accurate and reliable data to downstream processes.
  • Streamline Decisions: Drive a data culture and encourage users to work independently with reports and dashboards. Promote decision-making across the organization.
  • Enhance Offerings: Deliver tailored products based on buyer needs and preferences. This approach combines information from several sources, giving a complete picture of how people perceive the product.
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  • Faster, Deeper Insights: SAS Data Management streamlines data preparation, reducing time spent wrangling data and freeing you to focus on analysis. Dive into complex datasets faster with automated tasks, data quality checks, and efficient transformation tools.
  • Unify Data from Anywhere: Break down data silos and gain a holistic view with seamless access across diverse sources. Connect to databases, cloud platforms, and data lakes with ease, regardless of format or location.
  • Empower Business Users: Equip non-technical users with self-service tools for data discovery and exploration. Drag-and-drop interfaces and intuitive wizards make data manipulation accessible, fostering data-driven decision-making across the organization.
  • Boost Efficiency and Productivity: Automate repetitive tasks and simplify complex workflows with SAS Data Management's powerful scripting language. Eliminate manual processes and free up time for higher-value analysis, boosting team productivity.
  • Build Trustworthy Data: Ensure data quality and compliance with comprehensive governance features. Track data lineage, maintain audit trails, and apply robust security measures to build trust in your data and its insights.
  • Unleash the Power of AI and Machine Learning: Integrate AI and machine learning capabilities directly into your data pipelines. Cleanse data with intelligent algorithms, identify hidden patterns, and generate predictive models, all within the SAS Data Management platform.
  • Scale with Confidence: SAS Data Management scales seamlessly to meet your growing data needs. Handle large and complex datasets efficiently, whether on-premises or in the cloud, with robust infrastructure and performance optimization tools.
  • Future-proof Your Data Strategy: Stay ahead of the curve with SAS Data Management's continuous innovation. Access cutting-edge technologies like in-memory analytics and cloud-native capabilities to adapt to the evolving data landscape.
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  • 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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  • 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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Product Ranking

#20

among all
ETL Tools

#43

among all
ETL Tools

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Analyst Rating Summary

96
94
100
100
100
94
94
84
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Data Delivery
Data Quality
Metadata Management
Platform Security
Workflow Management
Data Delivery
Metadata Management
Performance and Scalability
Platform Capabilities
Platform Security

Analyst Ratings for Functional Requirements Customize This Data Customize This Data

Informatica PowerCenter
SAS Data Management
+ Add Product + Add Product
Data Delivery Data Quality Data Sources And Targets Connectivity Data Transformation Metadata Management Platform Capabilities Workflow Management 100 100 94 91 100 0 100 100 94 84 97 100 100 92 0 25 50 75 100
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85%
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we're gathering data
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80%
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Analyst Ratings for Technical Requirements Customize This Data Customize This Data

we're gathering data
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User Sentiment Summary

Great User Sentiment 773 reviews
Great User Sentiment 99 reviews
85%
of users recommend this product

Informatica PowerCenter has a 'great' User Satisfaction Rating of 85% when considering 773 user reviews from 4 recognized software review sites.

86%
of users recommend this product

SAS Data Management has a 'great' User Satisfaction Rating of 86% when considering 99 user reviews from 4 recognized software review sites.

4.4 (83)
4.2 (18)
4.5 (41)
4.6 (17)
4.3 (557)
4.6 (29)
3.9 (92)
4.0 (35)

Awards

SelectHub research analysts have evaluated Informatica PowerCenter and concluded it earns best-in-class honors for Workflow Management.

Workflow Management Award

we're gathering data

Synopsis of User Ratings and Reviews

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.
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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.
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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.
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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.
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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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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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