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AtScale Adaptive Analytics (A3) vs Spark SQL comparison

Review summaries and opinions

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

Categories and Ranking

AtScale Adaptive Analytics ...
Average Rating
5.0
Number of Reviews
1
Ranking in other categories
Data Virtualization (6th), BI (Business Intelligence) Tools (40th), Data Governance (40th), BI on Hadoop (2nd)
Spark SQL
Average Rating
7.8
Reviews Sentiment
7.6
Number of Reviews
14
Ranking in other categories
Hadoop (5th)
 

Mindshare comparison

AtScale Adaptive Analytics (A3) and Spark SQL aren’t in the same category and serve different purposes. AtScale Adaptive Analytics (A3) is designed for Data Virtualization and holds a mindshare of 5.9%, down 10.4% compared to last year.
Spark SQL, on the other hand, focuses on Hadoop, holds 8.0% mindshare, down 9.9% since last year.
Data Virtualization Market Share Distribution
ProductMarket Share (%)
AtScale Adaptive Analytics (A3)5.9%
Denodo25.4%
TIBCO Data Virtualization18.9%
Other49.800000000000004%
Data Virtualization
Hadoop Market Share Distribution
ProductMarket Share (%)
Spark SQL8.0%
Cloudera Distribution for Hadoop19.1%
Apache Spark17.1%
Other55.8%
Hadoop
 

Featured Reviews

it_user822762 - PeerSpot reviewer
The GUI interface is nice and easy to use, but the organization of the icons is not saved across users
Connecting to a Hadoop database to create a cube to connect to Tableau. We want to be able to easily create cubes which can be connected to Tableau for visualization The product had many issues. We had great collaboration with the product development team, but the product was not able to meet our…
SurjitChoudhury - PeerSpot reviewer
Offers the flexibility to handle large-scale data processing
My experience with the initial setup of Spark SQL was relatively smooth. Understanding the system wasn't overly difficult because the data was structured in databases, and we could use notebooks for coding in Python or Java. Configuring networks and running scripts to load data into the database were routine tasks that didn't pose significant challenges. The flexibility to use different languages for coding and the ability to process data using key-value pairs in Python made the setup adaptable. Once we received the source data, processing it in SparkSQL involved writing scripts to create dimension and fact tables, which became a standard part of our workflow. Setting up Spark SQL was reasonably quick, but sometimes we face performance issues, especially during data loading into the SQL Server data warehouse. Sequencing notebooks for efficient job runs is crucial, and managing complex tasks with multiple notebooks requires careful tracking. Exploring ways to optimize this process could be beneficial. However, once you are familiar with the database architecture and project tools, understanding and adapting to the system become more straightforward.

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."
"The stability was fine. It behaved as expected."
"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."
"Data validation and ease of use are the most valuable features."
"I find the Thrift connection valuable."
"This solution is useful to leverage within a distributed ecosystem."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."
"It is a stable solution."
 

Cons

"The organization of the icons is not saved across users."
"There was an issue with the incremental aggregation not working as indicated."
"The product was not able to meet our 10 second refresh requirements."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."
"There are many inconsistencies in syntax for the different querying tasks."
"I've experienced some incompatibilities when using the Delta Lake format."
"It would be useful if Spark SQL integrated with some data visualization tools."
"Anything to improve the GUI would be helpful."
"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."
"In the next update, we'd like to see better performance for small points of data. It is possible but there are better tools that are faster and cheaper."
 

Pricing and Cost Advice

Information not available
"The on-premise solution is quite expensive in terms of hardware, setting up the cluster, memory, hardware and resources. It depends on the use case, but in our case with a shared cluster which is quite large, it is quite expensive."
"The solution is bundled with Palantir Foundry at no extra charge."
"There is no license or subscription for this solution."
"We use the open-source version, so we do not have direct support from Apache."
"We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small."
"The solution is open-sourced and free."
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Top Industries

By visitors reading reviews
Financial Services Firm
16%
Healthcare Company
13%
Manufacturing Company
11%
Media Company
9%
Financial Services Firm
15%
University
14%
Retailer
11%
Manufacturing Company
10%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business5
Midsize Enterprise5
Large Enterprise4
 

Questions from the Community

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What do you like most about Spark SQL?
Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline.
What needs improvement with Spark SQL?
In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL. There could be additional features that I haven't explored but the current solution for working ...
What is your primary use case for Spark SQL?
I employ Spark SQL for various tasks. Initially, I gathered data from databases, SAP systems, and external sources via SFTP, storing it in blob storage. Using Spark SQL within Jupyter notebooks, I ...
 

Also Known As

AtScale, AtScale Intelligence Platform
No data available
 

Overview

 

Sample Customers

Rakuten, TD Bank, Aetna, Glaxo-Smith Kline, Biogen, Toyota, Tyson
UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions