# tCacheOut

Persists the input RDDs depending on the specific storage level you define in order to offer faster access to these datasets later.

tCacheOut writes RDDs (Resilient Distributed Datasets) to the cache for later use in the same Job.

For more technologies supported by Talend, see [Talend components](https://help.qlik.com/talend/r/en-US/7.3/components/processing).

Depending on the Talend product you are using, this component can be used in one, some or all of the following Job frameworks:

* Spark Batch: see [tCacheOut properties for Apache Spark Batch](https://help.qlik.com/talend/en-US/components/7.3/processing/tcacheout-properties-for-apache-spark-batch).

  The component in this framework is available in all Talend products with Big Data and Talend Data Fabric.

* Spark Streaming: see [tCacheOut properties for Apache Spark Streaming](https://help.qlik.com/talend/en-US/components/7.3/processing/tcacheout-properties-for-apache-spark-streaming).

  This component is available in Talend Real Time Big Data Platform and Talend Data Fabric.

### In this section

* [tCacheOut properties for Apache Spark Batch](https://help.qlik.com/talend/en-US/components/7.3/processing/tcacheout-properties-for-apache-spark-batch)
* [tCacheOut properties for Apache Spark Streaming](https://help.qlik.com/talend/en-US/components/7.3/processing/tcacheout-properties-for-apache-spark-streaming)

**Related concepts**

[Performing download analysis using a Spark Batch Job](https://help.qlik.com/talend/en-US/components/7.3/processing/tcacheout-textractdelimitedfields-ts3configuration-performing-download-analysis-using-spark-batch-job-spark-batch-component-in-this)