Talend vs SAS Data Management

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Our analysts compared Talend 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

Talend is an open-source data integration and management platform that enables big data ingestion, transformation and mapping at the enterprise level. The vendor provides cross-network connectivity, data quality and master data management in a single, unified hub – the Data Fabric. Based on industry standards like Eclipse, Java and SQL, it helps businesses create reusable pipelines – build once and use anywhere, with no proprietary lock-in.

The open-source version is free, with the cloud data integration module available for a monthly and annual fee. The price of Data Fabric is available on request.

Pros
  • Open source & affordable
  • Visual drag-and-drop UI
  • Wide range of connectors
  • Scalable & cloud-ready
  • Active community & support
Cons
  • Steeper learning curve
  • Limited native profiling
  • Customization can be complex
  • Debugging can be challenging
  • Enterprise features require paid editions
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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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$4,800 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

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Product Insights

  • Maximize ROI: Save money and time – build data pipelines faster. Lower operational costs with a zero-footprint solution. Build long-term customer associations by making reliable, accurate data available. Reduce risk and ensure regulatory compliance. Get a fast return on investment with its usage-based subscription model. 
  • Make Business Decisions Quicker: Live preview business data and diagnose issues instantly. Refer to dataset documentation to understand how datasets relate and change through the data flow and how variables used in analysis are defined. Transform data while maintaining optimum quality by applying built-in functions across batch and streaming pipelines. 
  • Integrate Big Data: Connect to a variety of data sources with more than 800 data connectors, with native support for Hadoop, MapReduce and NoSQL. Delivers clean, consistent assets at a big data scale. 
  • Deploy Anywhere: Get data from behind firewalls, data centers or in secure cloud environments. Manage data in the public cloud, with self-managed private clouds and on-premise platforms, or go hybrid. 
  • Scale As The Business Grows: Scales as more complex, larger data sets are generated to provide advanced analytics. Works with Amazon Web Services, Microsoft Azure, Google Cloud Platform, Snowflake and Databricks. 
  • Maintain Data Quality: Identify, highlight and fix issues as data moves across systems. Ensure data quality at every stage of the data pipeline with embedded checks. Preempt inconsistencies before they can impact crucial decisions. 
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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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  • 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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  • 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

#10

among all
ETL Tools

#43

among all
ETL Tools

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Analyst Rating Summary

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Data Delivery
Data Quality
Data Transformation
Metadata Management
Performance and Scalability
Data Delivery
Metadata Management
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Analyst Ratings for Functional Requirements Customize This Data Customize This Data

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Data Delivery Data Quality Data Sources And Targets Connectivity Data Transformation Metadata Management Platform Capabilities Workflow Management 100 100 92 100 100 100 89 100 94 84 97 100 100 92 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 270 reviews
Great User Sentiment 99 reviews
82%
of users recommend this product

Talend has a 'great' User Satisfaction Rating of 82% when considering 270 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.

n/a
4.2 (18)
4.19 (22)
n/a
4.0 (52)
4.6 (17)
4.1 (145)
4.6 (29)
4.1 (51)
4.0 (35)

Awards

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

Data Transformation Award

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Synopsis of User Ratings and Reviews

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.
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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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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.
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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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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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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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