Apache Spark vs Data Science Workbench

Likelihood to Recommend

The software appears to run more efficiently than other big data tools, such as Hadoop. Given that, Apache Spark is well-suited for querying and trying to make sense of very, very large data sets. The software offers many advanced machine learning and econometrics tools, although these tools are used only partially because very large data sets require too much time when the data sets get too large. The software is not well-suited for projects that are not big data in size. The graphics and analytical output are subpar compared to other tools.

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Thomas Young

Owner, previous CEO

Econometric StudiosFinancial Services, 11-50 employees

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If you already have a Cloudera partnership and a cluster, having this is a no brainer.
It integrates well with your existing ecosystem and it immediately starts working on projects, accessing full datasets and share analysis and results.
With the inclusion of Kubernetes, CPU and memory across worker nodes can be managed effectively.

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Bharadwaj (Brad) Chivukula

Sr.Technical Manager/Delivery Manager

Nisum Technologies, Inc.Retail, 10,001+ employees

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Feature Rating Comparison

Apache Spark

—

Data Science Workbench

7.0

Connect to Multiple Data Sources

—

6.0

Extend Existing Data Sources

—

7.0

Automatic Data Format Detection

—

7.0

MDM Integration

—

8.0

Apache Spark

—

Data Science Workbench

9.0

Visualization

—

9.0

Interactive Data Analysis

—

9.0

Apache Spark

—

Data Science Workbench

7.8

Interactive Data Cleaning and Enrichment

—

8.0

Data Transformations

—

8.0

Data Encryption

—

8.0

Built-in Processors

—

7.0

Apache Spark

—

Data Science Workbench

9.7

Multiple Model Development Languages and Tools

—

9.0

Single platform for multiple model development

—

10.0

Self-Service Model Delivery

—

10.0

Apache Spark

—

Data Science Workbench

7.0

Flexible Model Publishing Options

—

10.0

Security, Governance, and Cost Controls

—

4.0

Pros

Ease of use, the Spark API allows for minimal boilerplate and can be written in a variety of languages including Python, Scala, and Java.
Performance, for most applications we have found that jobs are more performant running via Spark than other distributed processing technologies like Map-Reduce, Hive, and Pig.
Flexibility, the frameworks comes with support for streaming, batch processing, sql queries, machine learning, etc. It can be used in a variety of applications without needing to integrate a lot of other distributed processing technologies.

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Verified User

Engineer in Engineering

Computer Software Company, 51-200 employees

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One single IDE (browser based application) that makes Scala, R, Python integrated under one tool
For larger organizations/teams, it lets you be self reliant
As it sits on your cluster, it has very easy access of all the data on the HDFS
Linking with Github is a very good way to keep the code versions intact

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Bharadwaj (Brad) Chivukula

Sr.Technical Manager/Delivery Manager

Nisum Technologies, Inc.Retail, 10,001+ employees

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Cons

PySpark does not have the same ease of use and functionality that Pandas does yet.

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Verified User

Analyst in Research & Development

Research Company, 201-500 employees

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Not as great as RStudio; lacks some features when compared with it
It is quite simple still (because its very early in its initiative), and companies may want to wait until they see a more developed product

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Bharadwaj (Brad) Chivukula

Sr.Technical Manager/Delivery Manager

Nisum Technologies, Inc.Retail, 10,001+ employees

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Support

No score

No answers yet

No answers on this topic

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Data Science Workbench 5.0

Based on 1 answer

It is expensive and difficult to install and maintain.

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Verified User

Professional in Research & Development

Utilities Company, 10,001+ employees

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Alternatives Considered

It is easy to learn, read and to maintain. It brings the best of the Ruby on Rails framework from Java that helps to create a web service so easily. Communication is one of the most distinctive features of Apache Spark compared to alternative products. You are able to communicate with your colleague in your team who also uses Spark while you are on the phone.

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Verified User

Analyst in Information Technology

Computer Networking Company, 1001-5000 employees

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Both the tools have similar features and have made it pretty easy to install/deploy/use. Depending on your existing platform (Cloudera vs. Azure) you need to pick the Workbench. Another observation is that Cloudera has better support where you can get feedback on your questions pretty fast (unlike MS). As its a new product, I expect MS to be more efficient in handling customers questions.

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Bharadwaj (Brad) Chivukula

Sr.Technical Manager/Delivery Manager

Nisum Technologies, Inc.Retail, 10,001+ employees

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Return on Investment

overall positive impact to the business for analysis of big data using hadoop file system
very well received by data scientists in the business despite its shortcoming on analytical dashboarding

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Shiv Shivakumar

Acquisitions Leader

AbbottComputer Software, 1001-5000 employees

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As the tool itself can access all the HDFS, Spark data easily, the wait time between teams has reduced
Installation was a breeze, and ramp up time was fairly easy

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Bharadwaj (Brad) Chivukula

Sr.Technical Manager/Delivery Manager

Nisum Technologies, Inc.Retail, 10,001+ employees

View all 2 answers on this topic

Likelihood to Recommend

Apache Spark

Data Science Workbench

Feature Rating Comparison

Connect to Multiple Data Sources

Extend Existing Data Sources

Automatic Data Format Detection

MDM Integration

Visualization

Interactive Data Analysis

Interactive Data Cleaning and Enrichment

Data Transformations

Data Encryption

Built-in Processors

Multiple Model Development Languages and Tools

Single platform for multiple model development

Self-Service Model Delivery

Flexible Model Publishing Options

Security, Governance, and Cost Controls

Pros

Apache Spark

Data Science Workbench

Cons

Apache Spark

Data Science Workbench

Support

Apache Spark

Data Science Workbench

Alternatives Considered

Apache Spark

Data Science Workbench

Return on Investment

Apache Spark

Data Science Workbench

Pricing Details

General

Free Trial

Free/Freemium Version

Premium Consulting/Integration Services

Entry-level set up fee?

General

Free Trial

Free/Freemium Version

Premium Consulting/Integration Services

Entry-level set up fee?

Rating Summary

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