How Does Incremental Load Work In Sqoop: A Step-By-Step Guide

Incremental loading in Sqoop refers to the technique used to efficiently synchronize updated or modified data, often referred to as “delta data,” from a Relational Database Management System (RDBMS) to the Hadoop ecosystem. The goal is to keep the Hadoop data store up-to-date with changes in the RDBMS without transferring all the data each time. This is achieved through the utilization of Sqoop’s incremental load command, which enables the extraction and transfer of only the newly changed or added records from the source database to Hadoop. This approach reduces data transfer times and minimizes network and storage overhead. As of December 17, 2018, Sqoop offered this capability to enhance the data synchronization process between RDBMS and Hadoop.

In Apache Sqoop, two essential data loading strategies are commonly employed: Full Load and Incremental Load.

Full Load involves loading an entire table or all tables from a database in a single command. This method is particularly useful when you want to replicate the entire dataset from a source to a target system.

On the other hand, Incremental Load is a feature within Apache Sqoop that enables the selective loading of updated portions of a table. This approach proves valuable when you only need to transfer the changes made to the data since a certain point in time, which helps save processing time and bandwidth.

For instance, let’s say you have a database, and you’ve been regularly updating records within it. With Incremental Load in Sqoop, you can efficiently transfer only the modified or newly added data, minimizing the data transfer workload and optimizing the data synchronization process.

These two data loading methods, Full Load and Incremental Load, are essential tools in the Sqoop toolkit, offering flexibility in managing data transfer operations based on your specific requirements. Please note that the information mentioned above is current as of September 2021, and there may have been updates or changes to Sqoop’s functionality since then.

Sqoop offers two distinct types of incremental data loading methods: “append” and “lastmodified.” When utilizing Sqoop for incremental imports, you can employ the “–incremental” parameter to specify which method to use based on your data source’s characteristics.

The “append” mode should be chosen when importing a table that constantly receives new rows with incrementing row IDs. This mode efficiently identifies and imports the newly added rows, ensuring your dataset remains up-to-date.

On the other hand, the “lastmodified” mode is ideal for situations where the source data records are modified, and you want to synchronize the changes. By using this mode, Sqoop identifies and imports only the rows that have been modified since the last import operation, allowing you to maintain an accurate and current representation of your data.

These two incremental loading modes in Sqoop provide flexibility and efficiency in managing data updates and additions, making it a valuable tool for data integration tasks.