StreamSets Data CollectorTM is a lightweight, powerful design and execution engine that streams data in real time. Use Data Collector to route and process data in your data streams.

StreamSets Data Collector EdgeTM (SDC Edge) is a lightweight execution agent without a UI that runs pipelines on edge devices. Use SDC Edge to read data from an edge device or to receive data from another pipeline and then act on that data to control an edge device.

StreamSets Control HubTM is a central point of control for all of your dataflow pipelines. Use Control Hub to allow your teams to build and execute large numbers of complex dataflows at scale.

After you start Data Collector, you can log in to Data Collector and create your first pipeline.

Data Collector provides a web-based user interface (UI) to configure pipelines, preview data, monitor pipelines, and review snapshots of data.

Data Collector displays a list of all available pipelines and related information on the Home page. You can select a category of pipelines, such as Running Pipelines, to view a subset of all available pipelines.

StreamSets provides multiple tutorials and sample pipelines to help you learn about using Data Collector.

In Data Collector, you can configure pipelines that are run by Data Collector and pipelines that are run by Data Collector Edge.

Pipelines and most pipeline stages include advanced options with default values that should work in most cases. By default, each pipeline and stage hides the advanced options. Advanced options can include individual properties or complete tabs.

Runtime values are values that you define outside of the pipeline and use for stage and pipeline properties. You can change the values for each pipeline run without having to edit the pipeline.

Many stages can use SSL/TLS encryption to securely connect to the external system.

When you use an origin to read log data, you define the format of the log files to be read.

You can use the whole file data format to transfer entire files from an origin system to a destination system. With the whole file data format, you can transfer any type of file.

An origin stage represents the source for the pipeline. You can use a single origin stage in a pipeline.

This solution describes how to convert Avro files to the columnar format, Parquet.

This solution describes how to configure a Drift Synchronization Solution for Hive pipeline to automatically refresh the Impala metadata cache each time changes occur in the Hive metastore.

This solution describes how to design a pipeline that writes output files to a destination, moves the files to a different location, and then changes the permissions for the files.

This solution describes how to design a pipeline that stops automatically after it finishes processing all available data.

This solution describes how to offload data from relational database tables to Hadoop.

This solution describes how to design a pipeline to send email notifications at different moments during pipeline processing.

This solution describes how to design a pipeline that preserves an audit trail of pipeline and stage events that occur.

You can use several solutions to load data into a Delta Lake table on Databricks.

The Drift Synchronization Solution for Hive detects drift in incoming data and updates corresponding Hive tables.

The Drift Synchronization Solution for PostgreSQL detects drift in incoming data and automatically creates or alters corresponding PostgreSQL tables as needed before the data is written.

StreamSets Control HubTM is a central point of control for all of your dataflow pipelines. Control Hub allows teams to build and execute large numbers of complex dataflows at scale.

You must register a Data Collector to work with StreamSets Control Hub. When you register a Data Collector, Data Collector generates an authentication token that it uses to issue authenticated requests to Control Hub.

After you register a Data Collector with StreamSets Control Hub, you can manage how the pipelines work with Control Hub.

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

StreamSets Data Collector EdgeTM (SDC Edge) is a lightweight execution agent without a UI that runs pipelines on edge devices with limited resources. Use SDC Edge to read data from an edge device or to receive data from another pipeline and then act on that data to control an edge device.

Download and install SDC Edge on each edge device where you want to run edge pipelines.

Data Collector Edge (SDC Edge) includes several sample pipelines that make it easy to get started. You simply import one of the sample edge pipelines, create the appropriate Data Collector receiving pipeline, download and install SDC Edge on the edge device, and then run the sample edge pipeline.

Edge pipelines run in edge execution mode. You design edge pipelines in Data Collector.

Administering SDC Edge involves configuring, starting, shutting down, and viewing logs for the agent. When using StreamSets Control Hub, you can also use the SDC Edge command line interface to register SDC Edge with Control Hub.

After designing edge pipelines in Data Collector, you deploy the edge pipelines to SDC Edge installed on an edge device. You run the edge pipelines on SDC Edge.

After designing edge pipelines in Data Collector and then deploying the edge pipelines to SDC Edge, you can manage the pipelines on SDC Edge. Managing edge pipelines includes previewing, validating, starting, stopping, and monitoring the pipelines as well as resetting the origin for the pipelines.

A microservice pipeline is a pipeline that creates a fine-grained service to perform a specific task.

When using SDC RPC pipelines, consider your needs and environment carefully as you design the deployment architecture.

The delivery guarantee determines when a pipeline commits the offset. When configuring the delivery guarantee for SDC RPC pipelines, use the same option in origin and destination pipelines.

The RPC ID is a user-defined identifier that allows an SDC RPC origin and SDC RPC destination to recognize each other.

You can enable SDC RPC pipelines to transfer data securely using SSL/TLS. To use SSL/TLS, enable TLS in both the SDC RPC destination and the SDC RPC origin.

Cluster EMR batch and cluster batch mode pipelines can process data from Amazon S3.

You can use Data Collector to view how data passes through the pipeline.

Data preview displays different colors for different types of data. Preview also uses other codes and formatting to highlight changed fields.

You can preview data for a group of linked stages within a pipeline.

You can edit preview data to view how a stage or group of stages processes the changed data. Edit preview data to test for data conditions that might not appear in the preview data set.

In data preview, you can edit stage properties to see how the changes affect preview data. For example, you might edit the expression in an Expression Evaluator to see how the expression alters data.

Duplicate a pipeline when you want to keep the existing version of a pipeline while continuing to configure a duplicate version. A duplicate is an exact copy of the original pipeline.

You can view the directories that the Data Collector 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 view metrics about Data Collector, such as the CPU usage or the number of pipeline runners in the thread pool.

If you use file-based authentication, you can view all user accounts granted access to this Data Collector instance, including the roles and groups assigned to each user.

You can view REST response JSON data for different aspects of the Data Collector, such as pipeline configuration information or monitoring details.

Data Collector provides a command line interface that includes a basic cli command. Use the command to perform some of the same actions that you can complete from the Data Collector UI. Data Collector must be running before you can use the cli command.

The basic tutorial creates a pipeline that reads a file from a directory, processes the data in two branches, and writes all data to a file system. You'll use data preview to help configure the pipeline, and you'll create a data alert and run the pipeline.

The extended tutorial builds on the basic tutorial, using an additional set of stages to perform some data transformations and write to the Trash development destination. We'll also use data preview to test stage configuration.