Hadoop vs SAS Viya

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Our analysts compared Hadoop vs SAS Viya based on data from our 400+ point analysis of Big Data Analytics Tools, user reviews and our own crowdsourced data from our free software selection platform.

Hadoop Software Tool

Product Basics

Apache Hadoop is an open source framework for dealing with large quantities of data. It’s considered a landmark group of products in the business intelligence and data analytics space, and is comprised of several different components. It functions on basic analytics principles like distributed computing, large data processing, machine learning and more.

Hadoop is part of a growing family of free, open source software (FOSS) projects from the Apache Foundation, and works well in conjunction with other third-party products.
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SAS Viya is a cloud-based in-memory analytics engine that provides data visualization, reporting and analytics to businesses for actionable data insights. Powered by AI, it brings together visual analytics, visual statistics and data science for enterprises to achieve end-to-end self-service analytics. It uses a standardized code base with support for programming in R, Python, SAS, Java and Lua.

Deployable in the cloud, on-premises and hybrid environments, it integrates with a wide range of business applications through an agile, scalable architecture. The vendor offers an introductory 30-day free trial.

Pros
  • Comprehensive features
  • Powerful analytics capabilities
  • User-friendly interface
  • Scalable architecture
  • Strong support from SAS
Cons
  • Steep learning curve
  • Limited customization options
  • High cost of ownership
  • Potential vendor lock-in
  • Resource-intensive
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Product Insights

  • Scalability: Hadoop's distributed computing model allows it to scale up from a single server to thousands of machines, each offering local computation and storage. This means businesses can handle more data simply by adding more nodes to the network, making it highly adaptable to the exponential growth of data.
  • Cost-Effectiveness: Unlike traditional relational database management systems that can be prohibitively expensive to scale, Hadoop enables businesses to store and manage vast amounts of data at a fraction of the cost, thanks to its ability to run on commodity hardware.
  • Flexibility: Hadoop is designed to efficiently process large volumes of data of different types, from structured to unstructured. This flexibility allows organizations to harness the power of big data without the constraints of a predefined schema, making it easier to make data-driven decisions.
  • Fault Tolerance: Hadoop automatically replicates data to multiple nodes, ensuring that the system is highly resilient to hardware failure. If a node goes down, tasks are automatically redirected to other nodes to ensure continuous operation, minimizing downtime and data loss.
  • Processing Speed: With its unique storage method based on a distributed file system that maps data wherever it is located on a cluster, Hadoop can process large volumes of data much more quickly than traditional systems. This speed makes it ideal for applications that require processing terabytes or petabytes of data, such as analyzing customer behavior patterns.
  • Efficient Data Processing: Hadoop's MapReduce programming model is designed for processing large data sets in parallel across a distributed cluster, which significantly speeds up the data processing tasks. This efficiency is crucial for performing complex calculations and analytics on big data in a timely manner.
  • Community Support: Being an open-source framework, Hadoop benefits from a vast community of developers and users who continuously contribute to its development and improvement. This community support ensures that Hadoop stays at the forefront of big data processing technology, with regular updates and a wide range of compatible tools and extensions.
  • Data Locality Optimization: Hadoop moves computation closer to data rather than moving large data sets across the network to be processed. This approach reduces the time taken to process data, as it minimizes network congestion and increases the overall throughput of the system.
  • Improved Business Continuity: The fault tolerance and high availability features of Hadoop ensure that businesses can maintain continuous operations, even in the face of hardware failures or other issues. This reliability is critical for organizations that depend on real-time data analysis for operational decision-making.
  • Enhanced Data Security: Hadoop includes robust security features, such as Kerberos authentication, to ensure that data is protected against unauthorized access. This security framework is essential for businesses that handle sensitive information, providing peace of mind that their data is secure.
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  • Improve Decision-Making: Make informed business decisions by using historical and current proprietary information to derive analytical insights. Compute vast amounts of data faster and resolve complexities through parallel processing. Boost workflow efficiencies by deploying operational decisions that define real-time best actions at scale. 
  • Self-Service Analytics: Easily perform automated forecasting, goal-seeking and scenario analysis — no technical skills needed. Identify user sentiment through text analytics and incorporate geographical data for a complete picture of business metrics. 
  • Maximize ROI: Save time through built-in automation for data prep, feature engineering, algorithm selection and AI-powered data discovery. Innovate, rather than spending time on tedious data management and analytics tasks. 
  • Data Security: Ensure data encryption at rest and while moving across systems, in addition to auditing protocols. Connect to external data management systems like Oracle, Teradata, Facebook, Amazon and Esri seamlessly through Kerberos, SAML, OAuth and OpenID. 
  • Data Management: Import data by using an IDE or through REST APIs, and visualize and analyze it through self-service data prep. Join tables, apply functions and perform calculations, or drag-and-drop, pivot, and slice and dice to view desired metrics. 
  • Augmented Analytics: Identify relationships in data through automatically generated suggestions and guided analysis, and track anomalies and outliers. Tell data stories by generating easy-to-understand visuals and dashboard summaries in natural language. Derive meaningful insights for the future by creating what-if scenarios for forecasting and predictive analytics. 
  • Mobility: Access business reports on the go through a native mobile app that supports a variety of charts, graphs and tables. Configure app functionality per device for specific users to add and view reports, share links, add and view comments and view alerts. 
  • Scalable Architecture: Leverage its modular microservices architecture to scale as per business needs. Monitor and manage the health and configuration of individual microservices instances through the SAS Environment Manager. Deploys seamlessly to any type of environment, including the cloud, and runs on Cloud Foundry as a platform-as-a-service (PaaS). 
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  • Distributed Computing: Also known as the Hadoop Distributed File System (HDFS), this feature can easily spread computing tasks across multiple nodes, providing faster processing and data redundancy in the event that there’s a critical failure. Hadoop is the industry standard for big data analytics. 
  • Fault Tolerance: Data is replicated across nodes, so even in the event of one node failing, the data is left intact and retrievable. 
  • Scalability: The app is able to run on less robust hardware or scale up to industrial data processing servers with ease. 
  • Integration With Existing Systems: Because Hadoop is so central to so many big data analytics applications, it integrates easily into a number of commercial platforms like Google Analytics and Oracle Big Data SQL or with other Apache software like YARN and MapR. 
  • In-Memory Processing: Hadoop, in conjunction with Apache Spark, is able to quickly parse and process large quantities of data by storing it in-memory. 
  • Hadoop MapR: MapR is a component of Hadoop that combines a number of features like redundancy, POSIX compliance and more into a single, enterprise grade component that looks like a standard file server. 
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  • Visualization and Reporting: Dig into data for in-depth analysis and view key business metrics through autonomous data exploration and manipulation. Create and customize interactive reports and charts to share with others across the organization for collaborative insight. Get suggestions on graphics best suited to display pertinent data through auto charting. 
  • Data Modeling: Analyze data with predictive models through regression, clustering and neural networks. Ensure version control by tracking data models from creation through usage by registering, validating and monitoring each version. Creates snapshots of model properties and files and retains them for the future. 
  • Visual Statistics: Build diverse scenarios simultaneously and refine them with what-if analyses to uncover insights through experimentation. Unifies all business tools, irrespective of the language they support, into a common visual analytics solution. 
  • Cloud Integrations: Develop low-code technologies by porting SAS open-source models into mobile and business applications through its cloud-native capability. Optimize analytics workloads on clouds like Microsoft Azure and ensure cost-efficient migration of analytics to the cloud through a workload management tool. 
  • ML-Based Insights: Get valuable insights from new data types by combining structured and unstructured data in integrated machine learning programs. Choose the desired ML algorithm from a range of options and easily find the optimal parameter settings. Use Python within Jupyter notebooks for deep learning functions like computer vision, natural language processing, forecasting and speech processing. 
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Product Ranking

#1

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Big Data Analytics Tools

#41

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Big Data Analytics Tools

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Analyst Rating Summary

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Analyst Ratings for Functional Requirements Customize This Data Customize This Data

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Augmented Analytics Computer Vision And Internet Of Things (IoT) Dashboarding And Data Visualization Data Management Data Preparation Geospatial Visualizations And Analysis Machine Learning Mobile Capabilities Platform Capabilities Reporting 96 100 98 96 86 93 70 96 0 25 50 75 100
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User Sentiment Summary

Great User Sentiment 474 reviews
Great User Sentiment 203 reviews
85%
of users recommend this product

Hadoop has a 'great' User Satisfaction Rating of 85% when considering 474 user reviews from 3 recognized software review sites.

85%
of users recommend this product

SAS Viya has a 'great' User Satisfaction Rating of 85% when considering 203 user reviews from 3 recognized software review sites.

4.3 (101)
4.2 (157)
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Awards

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SelectHub research analysts have evaluated SAS Viya and concluded it deserves the award for the Best Overall Big Data Analytics Tools available today and earns best-in-class honors for Augmented Analytics and Computer Vision and Internet of Things (IoT).

Analysts' Pick Award
Augmented Analytics Award
Computer Vision and Internet of Things (IoT) Award

Synopsis of User Ratings and Reviews

Scalability: Hadoop can store and process massive datasets across clusters of commodity hardware, allowing businesses to scale their data infrastructure as needed without significant upfront investments.
Cost-Effectiveness: By leveraging open-source software and affordable hardware, Hadoop provides a cost-effective solution for managing large datasets compared to traditional enterprise data warehouse systems.
Flexibility: Hadoop's ability to handle various data formats, including structured, semi-structured, and unstructured data, makes it suitable for diverse data analytics tasks.
Resilience: Hadoop's distributed architecture ensures fault tolerance. Data is replicated across multiple nodes, preventing data loss in case of hardware failures.
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Ease of Use: All users who mention its interface say that it makes autonomous analysis and data modeling accessible to users of all skill levels.
Support: All users who review support say that representatives are responsive and helpful in resolving issues and queries.
Functionality: Around 71% of the users who comment on its feature set say that the software helps discover data insights through powerful visualizations and on-the-fly calculations.
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Complexity: Hadoop can be challenging to set up and manage, especially for organizations without a dedicated team of experts. Its ecosystem involves numerous components, each requiring configuration and integration.
Security Concerns: Hadoop's native security features are limited, often necessitating additional tools and protocols to ensure data protection and compliance with regulations.
Performance Bottlenecks: While Hadoop excels at handling large datasets, it may not be the best choice for real-time or low-latency applications due to its batch-oriented architecture.
Cost Considerations: Implementing and maintaining a Hadoop infrastructure can be expensive, particularly for smaller organizations or those with limited IT budgets.
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Cost: All users who discuss its pricing say that the cost of acquisition is high.
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Hadoop has been making waves in the Big Data Analytics scene, and for good reason. Users rave about its ability to scale like a champ, handling massive datasets that would make other platforms sweat. Its flexibility is another major plus, allowing it to adapt to different data formats and processing needs without breaking a sweat. And let's not forget about reliability – Hadoop is built to keep on chugging even when things get rough. However, it's not all sunshine and rainbows. Some users find Hadoop's complexity a bit daunting, especially if they're new to the Big Data game. The learning curve can be steep, so be prepared to invest some time and effort to get the most out of it. So, who's the ideal candidate for Hadoop? Companies dealing with mountains of data, that's who. If you're in industries like finance, healthcare, or retail, where data is king, Hadoop can be your secret weapon. It's perfect for tasks like analyzing customer behavior, detecting fraud, or predicting market trends. Just remember, Hadoop is a powerful tool, but it's not a magic wand. You'll need a skilled team to set it up and manage it effectively. But if you're willing to put in the work, Hadoop can help you unlock the true potential of your data.

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SAS Viya is an AI-powered data management and visual analytics platform with a robust, scalable architecture. All users who reviewed data source connectivity said that it connects to multiple sources and integrates easily with business applications, giving a seamless user experience. With fast in-memory processing of big data sets, it leverages the power of R to enable visual statistics. All users who mentioned predictive analysis said that it enables automated forecasting through what-if scenarios, goal-seeking, text mining and decision trees. Citing ease of use, all users say that the platform is intuitive and enables easy data modeling and self-service visual analytics. All users who mentioned support said that they are responsive and knowledgeable. Around 71% of the users who comment on its functionality say that it is a robust, scalable and flexible platform that enables visualization and analysis of business data, though some users say visual statistics need improvement. On the flip side, all users who review its cost say that the tool is expensive. In summary, SAS Viya is an analytics tool that provides data management, visualization and AI-powered analytics to enterprises for improved decision making, though small organizations and startups might find it cost-prohibitive.

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