Compare AtScale Adaptive Analytics (A3) vs. Spark SQL

AtScale Adaptive Analytics (A3) is ranked 31st in Business Intelligence (BI) Tools with 1 review while Spark SQL is ranked 7th in Hadoop with 2 reviews. AtScale Adaptive Analytics (A3) is rated 5.0, while Spark SQL is rated 7.6. 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". On the other hand, the top reviewer of Spark SQL writes "An excellent solution that continues to mature but needs graphing capabilities". AtScale Adaptive Analytics (A3) is most compared with JethroData, Datameer and Arcadia Data, whereas Spark SQL is most compared with Informatica Big Data Parser, Apache Spark and AtScale Adaptive Analytics (A3).
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Most Helpful Review
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Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
The GUI interface is nice and easy to use.

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Overall the solution is excellent.The stability was fine. It behaved as expected.

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

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The solution needs to include graphing capabilities. Including financial charts would help improve everything overall.In the next release, maybe the visualization of some command-line features could be added.

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Ranking
Views
3,080
Comparisons
1,678
Reviews
1
Average Words per Review
166
Avg. Rating
5.0
7th
out of 24 in Hadoop
Views
627
Comparisons
502
Reviews
1
Average Words per Review
220
Avg. Rating
8.0
Top Comparisons
Compared 20% of the time.
Also Known As
AtScale, AtScale Intelligence Platform
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AtScale
Apache
Overview

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.



Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers can easily switch back and forth between different APIs based on which provides the most natural way to express a given transformation.
Offer
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Sample Customers
Rakuten, TD Bank, Aetna, Glaxo-Smith Kline, Biogen, Toyota, Tyson
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