Talentcrowd operates as a digital talent platform — providing employers with pipelines of highly vetted senior-level technology talent and on-demand engineering resources. We're tech agnostic and cost-competitive.

About Apache Sqoop

Apache Sqoop is an open-source tool designed for efficiently transferring data between Apache Hadoop and structured data stores, such as relational databases. The name "Sqoop" is a combination of "SQL" (Structured Query Language) and "Hadoop." Sqoop simplifies the process of importing data from relational databases into Hadoop's HDFS (Hadoop Distributed File System) and exporting data from Hadoop back to relational databases.

Key Features:

  1. Data Transfer: Sqoop allows users to efficiently transfer data between Hadoop and various relational databases, including MySQL, Oracle, SQL Server, PostgreSQL, and more.

  2. Parallel Data Import: Sqoop uses parallel processing to import large datasets from relational databases to HDFS. This approach speeds up the data transfer process by dividing the data into smaller chunks and importing them concurrently.

  3. Incremental Imports: Sqoop supports incremental imports, allowing users to import only the data that has changed or been added since the last import. This reduces the overhead of transferring the entire dataset repeatedly.

  4. Export to Databases: Sqoop also enables the export of data stored in Hadoop back to relational databases. This is useful for scenarios where data has been processed or transformed in Hadoop and needs to be persisted in a database.

  5. Data Serialization: Sqoop supports various data serialization formats, including Avro and Parquet, which help optimize storage and improve query performance.

  6. Data Transformation: Users can specify transformations to be applied during the data transfer process, such as filtering rows, changing data types, and more.

  7. Connectivity: Sqoop supports connectivity to a wide range of databases and data sources, allowing seamless integration with various data systems.

  8. Integration with Hadoop Ecosystem: Sqoop integrates well with other Hadoop ecosystem components, such as HBase and Hive, allowing data to be imported into these systems for further processing and analysis.

Use Cases:

  • Data Warehousing: Sqoop is used to transfer data from relational databases into Hadoop for storage and analysis in data warehousing scenarios.

  • Data Lake Ingestion: Organizations use Sqoop to ingest data from diverse sources into Hadoop data lakes, where the data can be centralized and analyzed.

  • Archival: Sqoop can be used to archive data from databases to Hadoop for long-term storage and analysis.

  • Data Migration: Sqoop assists in migrating data from legacy systems to modern data platforms like Hadoop.

  • ETL (Extract, Transform, Load): Sqoop is often used in ETL processes to extract data from databases, transform it in Hadoop, and load it back into databases or other data stores.

Apache Sqoop streamlines the process of transferring data between traditional relational databases and Hadoop, facilitating the integration of structured data with big data analytics. It helps bridge the gap between the world of relational databases and the distributed computing capabilities of Hadoop.

The Apache Sqoop project was retired in June 2021 and moved to the Apache Attic.

Ask Question
Do You Have a Question?
We’re more than happy to help through our contact form on the Contact Us page, by phone at +1 (858) 203-1321 or via email at
Need Short Term Help?

Hire Talent for a Day

Already know what kind of work you're looking to do?
Access the right people at the right time.

Elite expertise, on demand