What are the best ETL design patterns for data mapping?

O padr?o de Pipeline é uma abordagem de design para a implementa??o de processos ETL (Extra??o, Transforma??o e Carga) em que as várias etapas do processo s?o organizadas de forma sequencial, como em uma linha de produ??o, onde os dados fluem de uma etapa para a próxima. Cada etapa do pipeline representa uma fase específica do processo ETL, como extra??o de dados de fontes de origem, transforma??o desses dados conforme necessário e, finalmente, a carga dos dados em um destino.

organizations centralize all their data into a unified repository, ensuring that all relevant information is stored in one place. Standardizing data formats ensures consistency across different datasets, while eliminating duplicates removes redundancy and ensures data accuracy. Establishing robust data governance practices, including rules for usage and access, helps maintain data integrity. Regular updates keep the data current and relevant, while ongoing data quality checks maintain its reliability. Finally, providing access controls ensures that only authorized individuals can access and modify the data, further safeguarding its accuracy and integrity.

The staging area pattern remains one of the oldest and most widely used patterns. Its key benefit lies in preserving raw data for auditing, root-cause analysis, and feed re-runs across different environments. However, having multiple staging areas in a stack can introduce unnecessary complexity. It's often advisable to avoid this practice as the benefits are usually limited, if present at all.

The best ETL design patterns for data mapping involve utilizing Extract-Transform-Load (ETL) tools, employing star schema for dimensional modeling, implementing slowly changing dimensions (SCDs) for handling changing data, utilizing surrogate keys for efficient data integration, leveraging lookup tables for mapping values, employing data profiling for understanding data characteristics, using staging areas for data cleansing and transformation, employing data lineage for tracking data flow, and implementing error handling and logging mechanisms for ensuring data quality and reliability.

The Lookup pattern is handy when creating an ETL job to supply data to systems like Trading, Accounting or Portfolio Management systems. These systems often have many rules, so an external system feeding trades needs an efficient way to access required master and metadata stored within it. The Lookup pattern facilitates this by ensuring that such data is included in the ETL input feed.

The pipeline pattern proves effective in ETL design for data mapping, breaking down transformations into manageable steps. This method orchestrates the process through a defined pipeline, specifying inputs, outputs, and dependencies for each step. Various transformations, such as filtering or aggregating, are applied in individual steps. The pattern enhances readability, making it easier to modify and reuse steps, promoting modularity and reusability. By creating and storing reusable components, efficiency is boosted, allowing parallel execution for improved performance. Overall, the pipeline pattern provides a structured approach to data mapping, optimizing processes and facilitating maintenance in ETL workflows.

Pipelines are popular, especially for Cloud Migrations. To avoid making extra pipelines, tools like Azure Data Factory let you create one main pipeline for basic copying tasks. You can then add different versions for other needs on top of this. This keeps the main copying and organizing process in one place. This helps keep things simple and also makes it easier to track data history and details.

Common ETL design patterns for effective data mapping include the Extract-Load-Transform (ELT) pattern, where data is first loaded into the destination and then transformed, enabling parallel processing and scalability. The Slowly Changing Dimension (SCD) pattern addresses changing data by updating or maintaining historical records. The Hub-and-Spoke pattern centralizes data integration through a central hub while maintaining flexibility in data source connections. Additionally, the Pipeline pattern streamlines data flow by breaking it into manageable stages. Choosing the right design pattern depends on the specific requirements and characteristics of the data mapping process in each ETL scenario.