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Top Answer: Data validation and ease of use are the most valuable features.
Top Answer: There should be better integration with other solutions.
Ranking
4th
Views
1,260
Comparisons
903
Reviews
0
Average Words per Review
0
Rating
N/A
3rd
out of 22 in Hadoop
Views
687
Comparisons
285
Reviews
5
Average Words per Review
297
Rating
7.0
Comparisons
Also Known As
AtScale, AtScale Intelligence Platform
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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
Learn more about AtScale Adaptive Analytics (A3)
Learn more about Spark SQL
Sample Customers
Rakuten, TD Bank, Aetna, Glaxo-Smith Kline, Biogen, Toyota, Tyson
UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions
Top Industries
VISITORS READING REVIEWS
Computer Software Company33%
Financial Services Firm15%
Comms Service Provider11%
Insurance Company7%
VISITORS READING REVIEWS
Computer Software Company31%
Comms Service Provider28%
Financial Services Firm8%
Media Company5%

AtScale Adaptive Analytics (A3) is ranked 4th in Data Virtualization while Spark SQL is ranked 3rd in Hadoop with 5 reviews. AtScale Adaptive Analytics (A3) is rated 0.0, while Spark SQL is rated 7.0. On the other hand, the top reviewer of Spark SQL writes "GUI could be improved. Useful for speedily processing big data". AtScale Adaptive Analytics (A3) is most compared with Dremio, Denodo, Alteryx, Looker and Oracle Essbase, whereas Spark SQL is most compared with IBM Db2 Big SQL, Amazon EMR, Apache Spark and Informatica Big Data Parser.

We monitor all Data Virtualization 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.