StreamSets TransformerTM is an execution engine that runs data processing pipelines on Apache Spark, an open-source cluster-computing framework. Because Transformer pipelines run on Spark deployed on a cluster, the pipelines can perform transformations that require heavy processing on the entire data set in batch or streaming mode.

Transformer functions as a Spark client that launches distributed Spark applications.

Transformer can run pipelines in batch mode. A batch pipeline processes all available data in a single batch, and then stops.

Transformer can run pipelines in streaming mode. A streaming pipeline maintains connections to origin systems and processes data at user-defined intervals. The pipeline runs continuously until you manually stop it.

For users already familiar with StreamSets Data Collector pipelines, here's how Transformer pipelines are similar... and different.

StreamSets provides tutorials and sample pipelines to help you learn about using Transformer.

To register Transformer with Control Hub, you generate an authentication token and modify the Transformer configuration files.

You can unregister a Transformer from Control Hub when you no longer want to use that Transformer installation with Control Hub.

You can customize how a registered Transformer works with Control Hub by editing the Control Hub configuration file, $TRANSFORMER_CONF/dpm.properties, located in the Transformer installation.

A Transformer pipeline describes the flow of data from origin systems to destination systems and defines how to transform the data along the way.

Transformer provides sample pipelines that you can use to learn about Transformer features or as a template for building your own pipelines.

Typically, you run a Transformer pipeline on a cluster. You can also run a pipeline on a Spark installation on the Transformer machine. This is known as a local pipeline.

A Transformer pipeline runs on one or more Spark executors.

When you start a pipeline, StreamSets Transformer launches a Spark application. Spark runs the application just as it runs any other application, splitting the pipeline data into partitions and performing operations on the partitions in parallel.

Batch header attributes are attributes in batch headers that you can use in pipeline logic.

Transformer's offset handling ensures that, in times of sudden failures, a Transformer pipeline does not lose data - it processes data at least once. If a sudden failure occurs at a particular time, up to one batch of data may be reprocessed. This is an at-least-once delivery guarantee.

You can configure most origins and processors to cache data. You might enable caching when a stage passes data to more than one downstream stage.

You can preview data to help build or fine-tune a pipeline. You can preview complete or incomplete pipelines.

In Preview mode, Transformer displays different colors for different types of data. Transformer uses other codes and formatting to highlight changed fields.

When previewing data for a processor, you can preview both the input and the output data. You can display the output records in the order that matches the input records or in the order produced by the processor.

When running preview, you can edit stage properties to see how the changes affect preview data. For example, you might edit the condition in a Stream Selector processor to see how the condition alters which records pass to the different output streams.

When Transformer runs a pipeline, you can view real-time statistics about the pipeline.

When you monitor a pipeline, you can view real-time summary statistics for the pipeline and for stages in the pipeline.

In monitor mode, the Monitoring panel provides URLs for the cluster or the Spark application that runs the pipeline.

You can view the run history of a pipeline when you configure or monitor a pipeline. View the run history from either the Summary or History tab.

You can view the directories that Transformer uses. You might check the directories being used to access a file in the directory or to increase the amount of available space for a directory.

You can shut down and then manually launch Transformer to apply changes to the Transformer configuration file, environment configuration file, or user logins.

You can restart Transformer to apply changes to the Transformer configuration file, environment configuration file, or user logins. During the restart process, Transformer shuts down and then automatically restarts.

You can help to improve Transformer by allowing StreamSets to collect usage statistics about Transformer system performance and features that you use. This information helps StreamSets to improve product performance and to make product development decisions.