awesome-etl
A curated list of awesome ETL frameworks, libraries, and software.
https://github.com/pawl/awesome-etl
Last synced: 6 days ago
JSON representation
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Job Scheduling
- Jenkins - "the leading open-source automation server. Built with Java, it provides over 1000 plugins to support automating virtually anything, so that humans can actually spend their time doing things machines cannot."
- Chronos - "a distributed and fault-tolerant scheduler that runs on top of Apache Mesos that can be used for job orchestration."
- Dagobah - "a simple dependency-based job scheduler written in Python. Dagobah allows you to schedule periodic jobs using Cron syntax. Each job then kicks off a series of tasks (subprocesses) in an order defined by a dependency graph you can easily draw with click-and-drag in the web interface."
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Python
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Libraries
- Pandas - Implements dataframes in Python for easier data processing and includes a number of tools that make it easier to extract data from multiple file formats.
- parse - The opposite of Python's format(). Easier to use than regex, but more limited.
- BeautifulSoup - Popular library used to extract data from web pages.
- dataset - A wrapper around SQLAlchemy that simplifies database operations (including upserting).
- Ruffus - "The Ruffus module is a lightweight way to add support for running computational pipelines."
- Toolz - "A functional standard library for python." Includes a `pipe` function that allows you to pipe a value through a sequence of functions. There's also a cython implementation here: https://github.com/pytoolz/cytoolz
- BeautifulSoup - Popular library used to extract data from web pages.
- Dask - Ever tried using Pandas to process data that won't fit into memory? Dask makes it easy. Dask also has functionality to make it easy to processing continuous streams of data.
- dataset - A wrapper around SQLAlchemy that simplifies database operations (including upserting).
- Ruffus - "The Ruffus module is a lightweight way to add support for running computational pipelines."
- Toolz - "A functional standard library for python." Includes a `pipe` function that allows you to pipe a value through a sequence of functions. There's also a cython implementation here: https://github.com/pytoolz/cytoolz
- riko - A python stream processing engine modeled after Yahoo! Pipes.
- xmltodict - Makes working with XML as easy as working with JSON. Also allows streaming so you don't run out of memory on large XML files. Great for simple operations on small XML files.
- PETL - "a general purpose Python package for extracting, transforming and loading tables of data." Slower than Pandas and not as good for larger amounts of data, but simpler.
- PyQuery - Extracts data from web pages with a jquery-like syntax.
- polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
- Retrying - Allows you to add a decorator to any function/method to retry on an exception.
- Requests-HTML - Combines PyQuery, Requests, parse, and other libraries for a pleasant and intuitive web scraping experience.
- Blaze - "translates a subset of modified NumPy and Pandas-like syntax to databases and other computing systems."
- ijson - Allows processing JSON iteratively (as a stream) without loading the whole file into memory at once.
- Ruffus - "The Ruffus module is a lightweight way to add support for running computational pipelines."
- Bonobo - Simple, modern and atomic data transformation graphs for Python 3.5+.
- lxml - Parses XML using C libraries libxml2 and libxslt, so it's very fast. Also supports a "recover" mode that will try its best to use invalid xml or discard it. Great for large XML files and advanced functionality (like using xpaths). IBM also has a great article on high-performance parsing with lxml here: http://www.ibm.com/developerworks/library/x-hiperfparse/
- Odo - Moves data across containers (SQL, CSV, MongoDB, Pandas, etc). Claims to be the easiest and fastest way to load a CSV into your database.
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Go
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Talks/Articles
- Pachyderm - A system for running processing pipeline jobs in containers and version controlling all data using a commit-based distributed filesystem.
- Crunch - "A fast to develop, fast to run, Go based toolkit for ETL and feature extraction on Hadoop."
- CloudQuery - An open source high performance ELT Framework.
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Workflow Management/Engines
- Dray.it - "Docker workflow engine. Allows users to separate a workflow into discrete steps each to be handled by a single container."
- Pinball - "a scalable workflow management platform developed at Pinterest. It is built based on layered approach."
- TaskFlow - "allows the creation of lightweight task objects and/or functions that are combined together into flows (aka: workflows) in a declarative manner. It includes engines for running these flows in a manner that can be stopped, resumed, and safely reverted."
- prefect - "a new workflow management system, designed for modern infrastructure and powered by the open-source Prefect Core workflow engine. Users organize Tasks into Flows, and Prefect takes care of the rest."
- Luigi - "a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in."
- Toil - Similar to Luigi, jobs are classes with a run method. Supports executing jobs on other machines (workers) which can include AWS spot instances.
- Dray.it - "Docker workflow engine. Allows users to separate a workflow into discrete steps each to be handled by a single container."
- Mara Pipelines - "A lightweight opinionated ETL framework, halfway between plain scripts and Apache Airflow"
- Argo - Container based workflow management system for Kubernetes. Workflows are specified as a directed acyclic graph (DAG), and each step is executed on a container, and the latter is run on a Kubernetes Pod. There is also support for Airflow DAGs.
- Airflow - "Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed."
- Azkaban - "a batch workflow job scheduler created at LinkedIn to run Hadoop jobs. Azkaban resolves the ordering through job dependencies and provides an easy to use web user interface to maintain and track your workflows."
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Java
- Scriptella - Java-XML ETL toolbox for every day use.
- Spring Batch - ETL on Spring ecosystem
- JSR 352 - Java native API for batch processing
- GETL - Groovy toolbox for ETL Tasks from practicing architectures
- Spring Batch - ETL on Spring ecosystem
- Scriptella - Java-XML ETL toolbox for every day use.
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Cloud Services
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Talks/Articles
- Alteryx - Cloud ETL tool with an interface similar to GUI ETL tools.
- Google Dataflow - "Google Cloud Dataflow provides a simple, powerful model for building both batch and streaming parallel data processing pipelines."
- Alteryx - Cloud ETL tool with an interface similar to GUI ETL tools.
- Google Dataflow - "Google Cloud Dataflow provides a simple, powerful model for building both batch and streaming parallel data processing pipelines."
- AWS Batch - Allows executing jobs as containerized applications running on Amazon ECS. Also includes features for dynamically bidding for Spot Instances, integration with existing workflow engines, scheduling, monitoring, dependency modeling, and dynamic scaling/provisioning based on amount of work.
- Hevo - Hevo is a Fully Automated, No-code Data Pipeline Platform that supports 150+ ready-to-use integrations across Databases, SaaS Applications, Cloud Storage, SDKs, and Streaming Services.
- AWS Glue - AWS Glue generates the code (using Python and Spark) to execute your data transformations and data loading processes.
- Amazon Simple Workflow Service (SWF) - "helps developers build, run, and scale background jobs that have parallel or sequential steps. You can think of Amazon SWF as a fully-managed state tracker and task coordinator in the Cloud."
- Stitch - "Stitch is a cloud-first, open source platform for rapidly moving data. A simple, powerful ETL service, Stitch connects to all your data sources – from databases like MySQL and MongoDB, to SaaS applications like Salesforce and Zendesk – and replicates that data to a destination of your choosing."
- Cloud Data Fusion - "Fully managed, cloud-native data integration platform."
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ETL Tools (GUI)
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Talks/Articles
- Microsoft SSIS - "a component of the Microsoft SQL Server database software that can be used to perform a broad range of data migration tasks."
- Pentaho Kettle - The most popular open-source graphical ETL tool.
- Pentaho Kettle - The most popular open-source graphical ETL tool.
- Talend - "an open source application for data integration job design with a graphical development environment"
- N8n - "Free and open fair-code licensed node based Workflow Automation Tool. Easily automate tasks across different services."
- CDAP - "Use Cask Data Application Platform to visually build and manage data applications in hybrid and multi-cloud environments."
- Microsoft SSIS - "a component of the Microsoft SQL Server database software that can be used to perform a broad range of data migration tasks."
- Microsoft SSIS - "a component of the Microsoft SQL Server database software that can be used to perform a broad range of data migration tasks."
- Apache NiFi - "a rich, web-based interface for designing, controlling, and monitoring a dataflow."
- "inner-platform effect" - of-the-box functionality can make some tasks simpler, especially for people not comfortable with writing code.
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Ruby
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Javascript
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Related Lists
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Big Data (Hadoop Stack)
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Talks/Articles
- Pig - "a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs."
- Spark - "a fast and general-purpose cluster computing system. It provides high-level APIs in Scala, Java, and Python that make parallel jobs easy to write, and an optimized engine that supports general computation graphs. It also supports a rich set of higher-level tools including Shark (Hive on Spark), MLlib for machine learning, GraphX for graph processing, and Spark Streaming."
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Programming Languages
Categories
Sub Categories
Keywords
etl
6
data
5
python
5
workflow
4
data-engineering
3
automation
3
data-science
3
elt
3
data-integration
3
data-analysis
2
xml
2
workflow-engine
2
orchestration
2
pipeline
2
sql
2
ruby
2
go
2
kubernetes
2
cli
2
analytics
1
big-data
1
scheduling
1
orchestration-framework
1
luigi
1
dataframe-library
1
dataframe
1
arrow
1
pipelines-as-code
1
jenkins
1
java
1
groovy
1
devops
1
continuous-integration
1
continuous-deployment
1
continuous-delivery
1
cicd
1
pachyderm
1
docker
1
distributed-systems
1
containers
1
ai
1
apis
1
data-flow
1
development
1
integration-framework
1
integrations
1
ipaas
1
low-code
1
low-code-platform
1
mcp
1