mkpipe 0.3.7

Core ETL pipeline framework for mkpipe.

MkPipe

MkPipe is a modular, open-source ETL (Extract, Transform, Load) tool that allows you to integrate various data sources and sinks easily. It is designed to be extensible with a plugin-based architecture that supports extractors, transformers, and loaders.

Features

• Extract data from multiple sources (e.g., PostgreSQL, MongoDB).
• Transform data using custom Python logic and Apache Spark.
• Load data into various sinks (e.g., ClickHouse, PostgreSQL, Parquet).
• Plugin-based architecture that supports future extensions.
• Cloud-native architecture, can be deployed on Kubernetes and other environments.

Quick Setup

You can deploy MkPipe using one of the following strategies:

1. Using Docker Compose

This method sets up all required services automatically using Docker Compose.

Steps:

1. Clone or copy the deploy folder from the repository.

2. Modify the configuration files:

   • .env for environment variables.
   • mkpipe_project.yaml for your specific ETL configurations.

3. Run the following command to start the services:

   docker-compose up --build
   

   This will set up the following services:

   • PostgreSQL: Required for data storage.
   • RabbitMQ: Required for the Celery run_coordinator=celery.
   • Celery Worker: Required for running the Celery run_coordinator=celery.
   • Flower UI: Optional, but required for monitoring Celery tasks.

   Note: If you only want to use the run_coordinator=singlewithout Celery, only PostgreSQL is necessary.

2. Running Locally

You can also set up the environment manually and run MkPipe locally.

Steps:

1. Set up and configure the following services:
   • RabbitMQ: Required for the Celery run_coordinator.
   • PostgreSQL: Required for data storage.
   • Flower UI: Optional, but required for monitoring Celery tasks.
2. Update the following configuration files in the deploy folder:
   • .env for environment variables.
   • mkpipe_project.yaml for your ETL configurations.
3. Install the python packages

   pip install mkpipe mkpipe-extractor-postgres mkpipe-loader-postgres
   

4. Set the project directory environment variable:

   export MKPIPE_PROJECT_DIR={YOUR_PROJECT_PATH}
   

5. Start MkPipe using the following command:

   mkpipe run
   

Documentation

For more detailed documentation, please visit the GitHub repository.

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

Db Support Plan

For actively supported databases/plugins, please visit the MkPipe-hub repository!

Core Relational Databases

• PostgreSQL
• MySQL
• MariaDB
• SQL Server
• Oracle Database
• SQLite
• Snowflake
• Google BigQuery
• Amazon Redshift
• ClickHouse
• Amazon S3

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NoSQL Databases

• MongoDB
• Cassandra
• DynamoDB
• Redis
• Azure Data Lake Storage (ADLS)
• Google Cloud Storage
• Elasticsearch
• TimescaleDB
• HDFS
• InfluxDB

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ERP/CRM Systems

• Salesforce
• SAP
• Microsoft Dynamics
• NetSuite
• Workday
• HubSpot
• Zoho CRM
• Freshsales
• Zendesk
• Oracle NetSuite

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Emerging Databases & Analytical Tools

Apache Druid

• Vertica
• SingleStore (MemSQL)
• Exasol
• SAP HANA
• IBM Db2
• Neo4j (Graph Database)
• Greenplum
• CockroachDB
• AWS Athena

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Streaming Systems

• Kafka
• RabbitMQ
• Pulsar
• Apache Flink
• Amazon Kinesis
• Google Pub/Sub
• Azure Event Hubs
• Apache NiFi
• ActiveMQ
• Redpanda

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File Formats & Data Lakes

• Parquet
• Avro
• JSON
• CSV
• XML
• ORC
• Google Drive (for raw files)
• Dropbox
• Box
• FTP/SFTP Servers

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Specialized Analytics Tools

• Metabase (Data Visualization)
• Tableau Data Extracts
• Power BI
• Looker
• Google Analytics (GA4)
• Mixpanel
• Amplitude
• Adobe Analytics
• Heap
• Klipfolio

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Industry-Specific Databases

• Aerospike
• RocksDB
• FaunaDB
• ScyllaDB
• ArangoDB
• MarkLogic
• CrateDB
• TigerGraph
• HarperDB
• SAP ASE (Sybase)

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Legacy Databases

• Teradata
• Netezza
• Informix
• Ingres
• Firebird
• Progress OpenEdge
• ParAccel
• MaxDB
• HP Vertica
• Sybase IQ

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Emerging Cloud & Hybrid Databases

• PlanetScale (MySQL-based)
• YugabyteDB
• TiDB
• OceanBase
• Citus (PostgreSQL-based)
• Snowplow Analytics
• Spanner (Google Cloud)
• MariaDB ColumnStore
• CockroachDB Serverless
• Weaviate (Vector Search)

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