Mambo

Mambo is a configuration-driven framework for Apache Spark that makes it easy for data engineers or analysts to quickly develop Spark-based data processing pipelines.

Mambo is simply a pre-made Spark application that implements many of the tasks commonly found in ETL pipelines. In many cases, Mambo allows large pipelines to be developed on Spark with no coding required. When custom code is needed, there are pluggable points in Mambo for core functionality to be extended. Mambo works in batch and streaming modes.

Some examples of what you can easily do with Mambo:

• 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

Available Processors

Generate

GenerateDataset - generate a dataset that is useful for testing

Process

• ExecuteSql - Execute a sql command against an in-memory dataset.
• ExecuteSqlEvaluation - Execute a sql evaluation command (if > then > else) against one or more in-memory datasets in order to fail the execution of the job.
• ExecuteCommand - Execute a command against the host operating system and stores the result to an in-memory dataset.
• ExecuteCdc - Execute change data capture between two in-memory datasets. The output is three new in-memory datasets appended with the following suffixes: _adds, _updates, _unchanged.

Ingest

• GetFile - Import files (json, avro, parquet, csv, xls) into an in-memory dataset*
• GetRdbms - Import date (table/query) from RDBMS into an in-memory dataset.
• GetRedis - Import date (key/value) from a Redis Key Value store into an in-memory dataset.

Distribute

• PutFile - Save an in-memory dataset to file (csv, json, parquet, avro).
• PutRdbms - Save an in-memory dataset to RDBMS.
• PutRedis - Save an in-memory dataset to Redis Key Value store.

*Supports local and remote files based on the specified fs (http://, file://, hdfs://)

Get started

Compiling Mambo

You can build the Mambo application from the top-level directory of the source code by running the Maven command:

mvn clean package

This will create mambo-0.1.0.jar in the target directory.

Finding examples

TlMambo provides example pipelines that you can run for yourself:

• Ingest Local Excel File: Example that reads a local XLS file, adds a timestamp column and saves as a json file.
• Ingest Remote CSV File: Example that reads remote (HTTP) csv file, aggregates the data and then saves as a json file.
• Generate Data: Example that generates test data, adds a column and saves as a json file.
• RDBMS Ingest: Example that reads an rdbms table, adds a timestamp column and saves as a json file.

Running Mambo

You can run Mambo by submitting it to Spark with the configuration file for your pipeline:

spark-submit mambo-0.1.0.jar yourpipeline.conf