Apache Sqoop vs. Hydrograph

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Sqoop
Score 8.8 out of 10
N/A
Apache Sqoop is a tool for use with Hadoop, used to transfer data between Apache Hadoop and other, structured data stores.N/A
Hydrograph
Score 8.0 out of 10
N/A
Bitwise offers Hydrograph, a data integration tool with provides ETL functionality on Hadoop and Spark.N/A
Pricing
Apache SqoopHydrograph
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SqoopHydrograph
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Apache SqoopHydrograph
Top Pros
Top Cons
Features
Apache SqoopHydrograph
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Apache Sqoop
-
Ratings
Hydrograph
6.0
1 Ratings
33% below category average
Connect to traditional data sources00 Ratings5.01 Ratings
Connecto to Big Data and NoSQL00 Ratings7.01 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Apache Sqoop
-
Ratings
Hydrograph
6.5
1 Ratings
26% below category average
Simple transformations00 Ratings5.01 Ratings
Complex transformations00 Ratings8.01 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Apache Sqoop
-
Ratings
Hydrograph
5.4
1 Ratings
40% below category average
Data model creation00 Ratings7.01 Ratings
Business rules and workflow00 Ratings4.01 Ratings
Collaboration00 Ratings5.01 Ratings
Testing and debugging00 Ratings6.01 Ratings
feature 100 Ratings5.01 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
Apache Sqoop
-
Ratings
Hydrograph
6.5
1 Ratings
23% below category average
Integration with data quality tools00 Ratings6.01 Ratings
Integration with MDM tools00 Ratings7.01 Ratings
Best Alternatives
Apache SqoopHydrograph
Small Businesses

No answers on this topic

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Score 8.3 out of 10
Medium-sized Companies
Cloudera Manager
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Score 9.7 out of 10
InfoSphere
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Score 10.0 out of 10
Enterprises
IBM Analytics Engine
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Score 9.1 out of 10
InfoSphere
InfoSphere
Score 10.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SqoopHydrograph
Likelihood to Recommend
9.0
(1 ratings)
8.0
(1 ratings)
User Testimonials
Apache SqoopHydrograph
Likelihood to Recommend
Apache
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
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Bitwise
hydrograph is very usefull when we need to analyze big data. in our scenario it helped a lot with rdms databases
Read full review
Pros
Apache
  • 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
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Bitwise
  • Coupling between complex model and MapReduce framework without reducer procedure was simplified.
  • The ability to reduce execution time and handle partial failure
  • The framework adapts to higherned complex model
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Cons
Apache
  • 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.
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Bitwise
  • Microsoft azure is recently joined in 2020 that can be improved.
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Alternatives Considered
Apache
  • 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.
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Bitwise
Snap logic fits good for small/medium whereas hydrograph suits even for Enterprise grade.
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Return on Investment
Apache
  • 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.
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Bitwise
  • Their is no tangible ROI for us Management of bid data is easy.
Read full review
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