What users are saying about
4 Ratings
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Score 8.8 out of 101
13 Ratings
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Score 7.5 out of 101

Likelihood to Recommend

Apache Sqoop

Sqoop is great for sending data between a JDBC compliant database and a Hadoop environment. Sqoop is built for those who need a few simple CLI options to import a selection of database tables into Hadoop, do large dataset analysis that could not commonly be done with that database system due to resource constraints, then export the results back into that database (or another). Sqoop falls short when there needs to be some extra, customized processing between database extract, and Hadoop loading, in which case Apache Spark's JDBC utilities might be preferred
Jordan Moore profile photo

Data Science Workbench

  • 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.
Bharadwaj (Brad) Chivukula profile photo

Feature Rating Comparison

Platform Connectivity

Apache Sqoop
Data Science Workbench
7.0
Connect to Multiple Data Sources
Apache Sqoop
Data Science Workbench
6.0
Extend Existing Data Sources
Apache Sqoop
Data Science Workbench
7.0
Automatic Data Format Detection
Apache Sqoop
Data Science Workbench
7.0
MDM Integration
Apache Sqoop
Data Science Workbench
8.0

Data Exploration

Apache Sqoop
Data Science Workbench
9.0
Visualization
Apache Sqoop
Data Science Workbench
9.0
Interactive Data Analysis
Apache Sqoop
Data Science Workbench
9.0

Data Preparation

Apache Sqoop
Data Science Workbench
7.8
Interactive Data Cleaning and Enrichment
Apache Sqoop
Data Science Workbench
8.0
Data Transformations
Apache Sqoop
Data Science Workbench
8.0
Data Encryption
Apache Sqoop
Data Science Workbench
8.0
Built-in Processors
Apache Sqoop
Data Science Workbench
7.0

Platform Data Modeling

Apache Sqoop
Data Science Workbench
9.7
Multiple Model Development Languages and Tools
Apache Sqoop
Data Science Workbench
9.0
Single platform for multiple model development
Apache Sqoop
Data Science Workbench
10.0
Self-Service Model Delivery
Apache Sqoop
Data Science Workbench
10.0

Model Deployment

Apache Sqoop
Data Science Workbench
7.0
Flexible Model Publishing Options
Apache Sqoop
Data Science Workbench
10.0
Security, Governance, and Cost Controls
Apache Sqoop
Data Science Workbench
4.0

Pros

Apache Sqoop

  • Provides generalized JDBC extensions to migrate data between most database systems
  • Generates Java classes upon reading database records for use in other code utilizing Hadoop's client libraries
  • Allows for both import and export features
Jordan Moore profile photo

Data Science Workbench

  • The ability to use multiple languages.
  • GitHub integration.
  • Scalable.
No photo available

Cons

Apache Sqoop

  • Sqoop2 development seems to have stalled. I have set it up outside of a Cloudera CDH installation, and I actually prefer it's "Sqoop Server" model better than just the CLI client version that is Sqoop1. This works especially well in a microservices environment, where there would be only one place to maintain the JDBC drivers to use for Sqoop.
Jordan Moore profile photo

Data Science Workbench

  • 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
Bharadwaj (Brad) Chivukula profile photo

Support

Apache Sqoop

No score
No answers yet
No answers on this topic

Data Science Workbench

Data Science Workbench 5.0
Based on 1 answer
It is expensive and difficult to install and maintain.
No photo available

Alternatives Considered

Apache Sqoop

  • Sqoop comes preinstalled on the major Hadoop vendor distributions as the recommended product to import data from relational databases. The ability to extend it with additional JDBC drivers makes it very flexible for the environment it is installed within.
  • Spark also has a useful JDBC reader, and can manipulate data in more ways than Sqoop, and also upload to many other systems than just Hadoop.
  • Kafka Connect JDBC is more for streaming database updates using tools such as Oracle GoldenGate or Debezium.
  • Streamsets and Apache NiFi both provide a more "flow based programming" approach to graphically laying out connectors between various systems, including JDBC and Hadoop.
Jordan Moore profile photo

Data Science Workbench

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.
Bharadwaj (Brad) Chivukula profile photo

Return on Investment

Apache Sqoop

  • When combined with Cloudera's HUE, it can enable non-technical users to easily import relational data into Hadoop.
  • Being able to manipulate large datasets in Hadoop, and them load them into a type of "materialized view" in an external database system has yielded great insights into the Hadoop datalake without continuously running large batch jobs.
  • Sqoop isn't very user-friendly for those uncomfortable with a CLI.
Jordan Moore profile photo

Data Science Workbench

  • Paid off for demonstration purposes.
No photo available

Pricing Details

Apache Sqoop

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No

Data Science Workbench

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No

Rating Summary

Likelihood to Recommend

Apache Sqoop
9.0
Data Science Workbench
6.4

Support

Apache Sqoop
Data Science Workbench
5.0

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