ETL comprises the extract, transform, and load phases that move data from source systems into a destination database or data warehouse. The extract step collects data from sources. Transform applies operations like cleansing, validation, aggregation. Load inserts the processed data into the target storage.
ETL enables consolidating data from disparate sources into a unified structure optimized for downstream uses like reporting and analytics. ETL tools and scripts automate and manage these data integration workflows.
Modern data platforms like Apache Arrow DataFusion accelerate ETL pipelines using SQL over DataFrames, optimized query execution engines, and interchangeable storage formats. Pushdown optimizations and incremental reprocessing further improve ETL efficiency at scale.
ETL is key for preparing raw data into an analysis-ready state. Cloud-native ETL simplifies building and maintaining scalable data lakes and warehouses.
The extract step acquires data from sources via APIs, queries, files. Transformations filter, validate, deduplicate, merge data. Load inserts data into the destination using SQL, APIs.
Steps may run sequentially or in parallel. Batch or streaming modes are supported. ETL manages data lineage tracking, recovery, integrity checks, logging, and operationalization of the overall workflow.
ETL is key for building analytics datasets, data warehouses, and data lakes. It collects and processes raw data into an analysis-ready form. ETL workflows integrate data from across departments and systems, standardize formats, enforce data quality, and populate central repositories.
ETL enables deriving business value from data. It is used extensively within data engineering to efficiently deliver clean, integrated data.
A DataFrame is a two-dimensional tabular data structure with labeled columns and rows, used for data manipulation and analysis in data science and machine learning workflows.
Read more ->Query execution is the process of carrying out the actual steps to retrieve results for a database query as per the generated execution plan.
Read more ->Apache DataFusion is an extensible, high-performance data processing framework in Rust, designed to efficiently execute analytical queries on large datasets. It utilizes the Apache Arrow in-memory data format.
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