Apache Spark vs AtScale Adaptive Analytics (A3) comparison

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Executive Summary

We performed a comparison between Apache Spark and AtScale Adaptive Analytics (A3) based on real PeerSpot user reviews.

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To learn more, read our detailed Hadoop Report (Updated: March 2024).
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Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"I feel the streaming is its best feature.""The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast.""The most valuable feature of this solution is its capacity for processing large amounts of data.""The most valuable feature of Apache Spark is its flexibility.""The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics.""We use Spark to process data from different data sources.""AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI.""It is useful for handling large amounts of data. It is very useful for scientific purposes."

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"The GUI interface is nice and easy to use."

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Cons
"One limitation is that not all machine learning libraries and models support it.""It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster.""Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases.""In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that.""Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn.""The setup I worked on was really complex.""The solution needs to optimize shuffling between workers.""The solution must improve its performance."

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"The product was not able to meet our 10 second refresh requirements.""There was an issue with the incremental aggregation not working as indicated.""The organization of the icons is not saved across users."

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Pricing and Cost Advice
  • "Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."
  • "Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
  • "We are using the free version of the solution."
  • "Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."
  • "Apache Spark is an expensive solution."
  • "Spark is an open-source solution, so there are no licensing costs."
  • "On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
  • "It is an open-source solution, it is free of charge."
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    Questions from the Community
    Top Answer:The product’s most valuable features are lazy evaluation and workload distribution.
    Top Answer:They provide an open-source license for the on-premise version. However, we have to pay for the cloud version including data centers and virtual machines.
    Top Answer:They could improve the issues related to programming language for the platform.
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    Ranking
    2nd
    out of 22 in Hadoop
    Views
    2,468
    Comparisons
    1,915
    Reviews
    20
    Average Words per Review
    387
    Rating
    8.6
    5th
    Views
    228
    Comparisons
    200
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    Comparisons
    Also Known As
    AtScale, AtScale Intelligence Platform
    Learn More
    Overview

    Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

    AtScale is the leading provider of intelligent data virtualization for big data analytical workloads, empowering citizen data scientists to accelerate and scale their business’ data analytics and science capabilities and ultimately build insight-driven 

    AtScale connects people to live disparate data without the need to move or extract it, leveraging existing investments in big data platforms, applications and tools. AtScale creates automated data engineering using a single set of semantics so consumers can query live data (either on premise or in the cloud) in seconds without having to understand how or where it is stored—providing security, governance and predictability in data usage and storage costs.

    Benefits:

    No data movement: AtScale is agnostic to data platforms and data location, whether on-premises or in the cloud, in a data lake or a data warehouse.

    Automatic “smart” aggregate creation: AtSacle’s intelligent aggregates adapt to the data model and how it is used, automating the data engineering tasks required to support those activities and reducing time spent from weeks to hours.

    Use your existing BI and AI tools: AtScale provides access to live, atomic-level data without the user needing to understand where or how to access the data, so you can keep using your tools of choice.

    No more extracts or shadow IT: AtScale eliminates the need for extracts with a single, consistent, governed view of live data, regardless of which BI and AI tools are used.

    Data-as-a-service: AtScale allows metadata to be created once, with centrally defined business rules and calculations, exposing data assets as a service.

    Data platform portability: Models built in AtScale are portable, with no need to recreate them for different platforms. AtScale can easily be repointed to new data platforms, making migration seamless to business users.

    Faster time-to-insight: AtScale reduces time-to-insight from weeks and months to minutes and hours. AtScale virtual models can be created and deployed in no time, with no ETL or data engineering.

    Future-proof your data architecture: AtScale alleviates the complexities of data platform and analytics tool integration, making cloud, hybrid-cloud and multi-cloud data architectures a reality without compromising performance, security, agility or existing governance and security policies.

    Features:

    Design CanvasTM: AtScale’s Design Canvas visually and intuitively connects to any data platform, allowing you to create virtual multidimensional cubes without ETL.

    Autonomous Data Engineering: Just-in-time query optimization that anticipates the needs of the data consumer.

    Universal Semantic LayerTM: A workspace with a Design Canvas for your data consumers to define business meaning and get a single-source-of-truth.

    Security & Data Governance: Centralized security policy to decentralize access using the tenants of Zero Trust.

    Virtual Cube Catalog: A gateway to data that is easily discoverable and frictionless—and available to use every day, en masse.

    AtScale connects people to live disparate data without the need to move or extract it, leveraging existing investments in big data platforms, applications and tools. AtScale creates automated data engineering using a single set of semantics so consumers can query live data (either on premise or in the cloud) in seconds without having to understand how or where it is stored—providing security, governance and predictability in data usage and storage costs.



    Sample Customers
    NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
    Rakuten, TD Bank, Aetna, Glaxo-Smith Kline, Biogen, Toyota, Tyson
    Top Industries
    REVIEWERS
    Computer Software Company30%
    Financial Services Firm15%
    University9%
    Marketing Services Firm6%
    VISITORS READING REVIEWS
    Financial Services Firm25%
    Computer Software Company13%
    Manufacturing Company7%
    Comms Service Provider6%
    VISITORS READING REVIEWS
    Financial Services Firm26%
    Manufacturing Company11%
    Computer Software Company8%
    Healthcare Company7%
    Company Size
    REVIEWERS
    Small Business40%
    Midsize Enterprise19%
    Large Enterprise40%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise12%
    Large Enterprise71%
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise8%
    Large Enterprise75%
    Buyer's Guide
    Hadoop
    March 2024
    Find out what your peers are saying about Cloudera, Apache, Amazon and others in Hadoop. Updated: March 2024.
    765,234 professionals have used our research since 2012.

    Apache Spark is ranked 2nd in Hadoop with 58 reviews while AtScale Adaptive Analytics (A3) is ranked 5th in Data Virtualization. Apache Spark is rated 8.4, while AtScale Adaptive Analytics (A3) is rated 5.0. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". On the other hand, the top reviewer of AtScale Adaptive Analytics (A3) writes "The GUI interface is nice and easy to use, but the organization of the icons is not saved across users". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Cloudera Distribution for Hadoop, whereas AtScale Adaptive Analytics (A3) is most compared with Denodo, Dremio, SAP BusinessObjects Business Intelligence Platform, ThoughtSpot and Alation Data Catalog.

    We monitor all Hadoop reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.