Airbyte is an open-source data integration platform that syncs data from applications, APIs & databases to data warehouses, lakes and other destinations, from the company of the same name in San Francisco. Pricing of the commercial version is based solely on compute time.
$2.50
per credit
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.
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Pricing
Airbyte
Apache Sqoop
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Airbyte
Apache Sqoop
Free Trial
Yes
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Airbyte
Apache Sqoop
Features
Airbyte
Apache Sqoop
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Airbyte
10.0
1 Ratings
20% above category average
Apache Sqoop
-
Ratings
Connect to traditional data sources
10.01 Ratings
00 Ratings
Connecto to Big Data and NoSQL
10.01 Ratings
00 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
I think Airbyte is well suited for any company that needs one tool that can move data from one or many sources into a consolidated warehousing solution. Even if it's just one source to target connection, Airbyte simplifies the ability to perform extract and load actions without having to get knee deep in python scripting.
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
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.
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.
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.
