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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 (5th), BI (Business Intelligence) Tools (42nd), Data Governance (36th), BI on Hadoop (6th)
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 8.2%, down 11.7% compared to last year.
Spark SQL, on the other hand, focuses on Hadoop, holds 10.5% mindshare, down 11.3% since last year.
Data Virtualization
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."
"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."
"Certain data sets that are very large are very difficult to process with Pandas and Python libraries. Spark SQL has helped us a lot with that."
"The performance is one of the most important features. It has an API to process the data in a functional manner."
"The speed of getting data."
"Overall the solution is excellent."
"The solution is easy to understand if you have basic knowledge of SQL commands."
"Data validation and ease of use are the most valuable features."
"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."
 

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."
"This solution could be improved by adding monitoring and integration for the EMR."
"SparkUI could have more advanced versions of the performance and the queries and all."
"In the next release, maybe the visualization of some command-line features could be added."
"Anything to improve the GUI would be helpful."
"It would be useful if Spark SQL integrated with some data visualization tools."
"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."
"There are many inconsistencies in syntax for the different querying tasks."
 

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."
"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."
"We use the open-source version, so we do not have direct support from Apache."
"There is no license or subscription for this solution."
"The solution is open-sourced and free."
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Top Industries

By visitors reading reviews
Financial Services Firm
21%
Manufacturing Company
12%
Healthcare Company
10%
Computer Software Company
10%
Financial Services Firm
18%
Computer Software Company
12%
Retailer
10%
Healthcare Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

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 is your experience regarding pricing and costs for Spark SQL?
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.
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 ...
 

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