Compare AtScale vs. Spark SQL

AtScale is ranked 1st in BI on Hadoop with 1 review while Spark SQL is ranked 8th in Hadoop with 1 review. AtScale is rated 5.0, while Spark SQL is rated 8.0. The top reviewer of AtScale 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 "A good stable and scalable solution for processing big data". AtScale is most compared with JethroData, Datameer and Arcadia Data, whereas Spark SQL is most compared with Apache Spark, AtScale and Informatica Big Data Parser.
You must select at least 2 products to compare!
AtScale Logo
3,573 views|2,024 comparisons
Spark SQL Logo
656 views|524 comparisons
Most Helpful Review
Use Spark SQL? Share your opinion.
Quotes From Members

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

The GUI interface is nice and easy to use.

Read more »

The stability was fine. It behaved as expected.

Read more »

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.

Read more »

In the next release, maybe the visualization of some command-line features could be added.

Read more »

Use our free recommendation engine to learn which BI on Hadoop solutions are best for your needs.
366,090 professionals have used our research since 2012.
out of 8 in BI on Hadoop
Average Words per Review
Avg. Rating
out of 24 in Hadoop
Average Words per Review
Avg. Rating
Top Comparisons
Compared 44% of the time.
Compared 24% of the time.
Compared 12% of the time.
Compared 27% of the time.
Compared 17% of the time.
Video Not Available

With AtScale, you can query data in-place where it lands in your Big Data Lake or Hadoop cluster, without additional data movement, but with OLAP and your BI tool of choice.

Drive Hadoop adoption for your business

With AtScale, I.T. can give business analysts to direct and efficient access the valuable data in your Hadoop or other Data Lake, all while preserving the control, security, and responsiveness of your existing big data platform.

  • Dynamic Cubes ensure consistency and control
  • Support for any Hadoop distribution
  • Smart Aggregations dramatically increase query throughput
  • Support for SQL or MDX over ODBC, JDBC, or OLE DB
  • Zero-footprint install + no data movement = reduced complexity

Simplify Big Data access for BI users

AtScale turns your Big Data Lake or Hadoop cluster into scaled-out analytical server. Now you can use your BI tool of choice – from Tableau to Microstrategy to Microsoft Excel – to connect directly and query data in Hadoop or other Data Lake, with speed, security and simplicity. 

  • Virtual cubes present complex data as simple hierarchies measures and dimensions
  • Works with virtually any BI tool that can talk SQL or MDX
  • Analyze billions of rows of data directly on your Hadoop cluster
  • Eliminate need for costly data marts, extracts, and custom cubes
  • Consistent data definitions across all BI tools mean consistent answers across users
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.
Learn more about AtScale
Learn more about Spark SQL
Sample Customers
Cloudera, Wargaming, eBates, AltiScale
Information Not Available
We monitor all BI on 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.
Sign Up with Email