Envelope is a configuration-driven framework for Apache Spark that makes it easy to develop Spark-based data processing pipelines.
Envelope is simply a pre-made Spark application that implements many of the tasks commonly found in ETL pipelines. In many cases, Envelope allows large pipelines to be developed on Spark with no coding required. When custom code is needed, there are pluggable points in Envelope for core functionality to be extended. Envelope works in batch and streaming modes.
Some examples of what you can easily do with Envelope:
- Run a graph of Spark SQL queries, all in the memory of a single Spark job
- Stream in event data from Apache Kafka, join to reference data, and write to Apache Kudu
- Read in from an RDBMS table and write to Apache Parquet files on HDFS
- Automatically merge into slowly changing dimensions (Type 1 and 2, and bi-temporal)
- Insert custom DataFrame transformation logic for executing complex business rules
Envelope requires Apache Spark 2.1.0 or above.
Additionally, if using these components, Envelope requires:
- Apache Kafka 0.10 or above
- Apache Kudu 1.4.0 or above
- Apache HBase 1.2.0 or above
- Apache ZooKeeper 3.4.5 or above
- Apache Impala 2.7.0 or above
For Cloudera CDH 5, Kafka requires Cloudera's Kafka 2.1.0 or above, HBase and ZooKeeper requires CDH 5.7 or above, and Impala requires CDH 5.9 or above. For Cloudera CDH 6, any CDH 6.0 or above is required.
Envelope and its dependencies can be downloaded as a single jar file from the GitHub repository Releases page.
Alternatively, you can build the Envelope application from the top-level directory of the source code by running the Maven command:
mvn clean install
This will create
envelope-0.7.0.jar in the
Envelope provides these example pipelines that you can run for yourself:
- FIX: simulates receiving financial orders and executions and tracking the history of the orders over time.
- This example includes a walkthrough that explains in detail how it meets the requirements.
- FIX HBase: simulates receiving financial orders and executions and tracking the history of the orders over time in HBase.
- Traffic: simulates receiving traffic conditions and calculating an aggregate view of traffic congestion.
- Filesystem: demonstrates a batch job that reads a JSON file from HDFS and writes the data back to Avro files on HDFS.
- Cloudera Navigator: implements a streaming job to ingest audit events from Cloudera Navigator into Kudu, HDFS and Solr.
- Impala DDL: demonstrates updating Impala metadata, such as adding partitions and refreshing table metadata
You can run Envelope by submitting it to Spark with the configuration file for your pipeline:
spark-submit envelope-0.7.0.jar your_pipeline.conf
Note: CDH5 uses
spark2-submit instead of
spark-submit for Spark 2 applications such as Envelope.
A helpful place to monitor your running pipeline is from the Spark UI for the job. You can find this via the YARN ResourceManager UI.
If you are ready for more, dive in:
- User Guide - details on the design, operations, configuration, and usage of Envelope
- Configuration Specification - a deep-dive into the configuration options of Envelope
- Inputs Guide - detailed information on each provided input, and how to write custom inputs
- Derivers Guide - detailed information on each provided deriver, and how to write custom derivers
- Planners Guide - directions and details on when, why, and how to use planners and associated outputs
- Looping Guide - information and an example for defining loops in an Envelope pipeline
- Decisions Guide - information on using decisions to dynamically choose which parts of the pipeline to run
- Tasks Guide - how to apply side-effects in an Envelope pipeline that are separate from the data flow
- Security Guide - how to run Envelope in secure cluster configurations
- Repetitions Guide - how to re-run cached steps in a streaming job based on provided criteria
- Events Guide - how to handle Envelope application lifecycle events