# Best Practice: Change Data Capture with Spark in Big Data

In the Data driven world, companies who have access to data real time have a significant advantage on their competitors who do not. One of the things that prevents IT to provide real time data is the time it takes to extract data from legacy systems and load into the Analytical systems.

As such, loading all the data into these systems every time is not efficient use of the computing resources and it also takes longer to get the data to the users. This is where Change Data Capture (CDC) plays a significant role in integrating data faster into target systems.

Fore more information, see the following articles:

* [Four Steps Strategy for Incremental Updates in Apache Hive on Hadoop](http://hortonworks.com/blog/four-step-strategy-incremental-updates-hive/)

* [About Change Data Capture (SQL Server)](https://msdn.microsoft.com/en-us/library/cc645937.aspx)

* [Just-in-Time Data Warehousing on Databricks](http://go.databricks.com/jit-dw-databricks-cdc-schema-on-read)

### In this section

* [What is Change Data Capture?](https://help.qlik.com/talend/en-US/components/7.2/change-data-capture/what-is-change-data-capture)
* [CDC in traditional databases](https://help.qlik.com/talend/en-US/components/7.2/change-data-capture/cdc-in-traditional-databases)
* [CDC with Spark in Big Data](https://help.qlik.com/talend/en-US/components/7.2/change-data-capture/cdc-with-spark-in-big-data)