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  2. Data build tool - Wikipedia

    en.wikipedia.org/wiki/Data_build_tool

    It started at RJMetrics in 2016 as a solution to add basic transformation capabilities to Stitch (acquired by Talend in 2018). [3] The earliest versions of dbt allowed analysts to contribute to the data transformation process following the best practices of software engineering.

  3. Apache Beam - Wikipedia

    en.wikipedia.org/wiki/Apache_Beam

    Apache Beam is an open source unified programming model to define and execute data processing pipelines, including ETL, batch and stream (continuous) processing. [2] Beam Pipelines are defined using one of the provided SDKs and executed in one of the Beam’s supported runners (distributed processing back-ends) including Apache Flink, Apache Samza, Apache Spark, and Google Cloud Dataflow.

  4. Apache Airflow - Wikipedia

    en.wikipedia.org/wiki/Apache_Airflow

    Airflow is written in Python, and workflows are created via Python scripts. Airflow is designed under the principle of "configuration as code". While other "configuration as code" workflow platforms exist using markup languages like XML, using Python allows developers to import libraries and classes to help them create their workflows.

  5. Extract, transform, load - Wikipedia

    en.wikipedia.org/wiki/Extract,_transform,_load

    Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container.

  6. Pipeline Pilot - Wikipedia

    en.wikipedia.org/wiki/Pipeline_pilot

    Pipeline Pilot was initially developed by SciTegic, a company that was acquired by BIOVIA in 2004. In 2014, BIOVIA became part of Dassault Systèmes.. Originally designed for applications in chemistry, Pipeline Pilot's capabilities have since been expanded to support a wider range of data processing tasks, including extract, transform, and load (ETL) processes, as well as general analytical ...

  7. pandas (software) - Wikipedia

    en.wikipedia.org/wiki/Pandas_(software)

    By default, a Pandas index is a series of integers ascending from 0, similar to the indices of Python arrays. However, indices can use any NumPy data type, including floating point, timestamps, or strings. [4]: 112 Pandas' syntax for mapping index values to relevant data is the same syntax Python uses to map dictionary keys to values.

  8. KNIME - Wikipedia

    en.wikipedia.org/wiki/KNIME

    KNIME allows users to visually create data flows (or pipelines), selectively execute some or all analysis steps, and later inspect the results, models, using interactive widgets and views. KNIME is written in Java and based on Eclipse. It makes use of an extension mechanism to add plugins providing additional functionality.

  9. Dask (software) - Wikipedia

    en.wikipedia.org/wiki/Dask_(software)

    Dask is an open-source Python library for parallel computing.Dask [1] scales Python code from multi-core local machines to large distributed clusters in the cloud. Dask provides a familiar user interface by mirroring the APIs of other libraries in the PyData ecosystem including: Pandas, scikit-learn and NumPy.