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Building Data Pipelines

A Data Pipeline is a set of processes that allows data to be collected, processed, and moved from one or more places to another. It involves the systematic flow of data through a series of automated steps to ensure that data is ingested, transformed, and made available for stakeholders. At CRUK these stakeholders can include: supporter management, marketing and data analysts.

They are a critical component of any data engineering infrastructure, facilitating the seamless flow of data from source to destination and must handle varying data volumes efficiently. To ensure the effectiveness and reliability of your data pipelines, there are some key considerations:

1. Data Observability​

  • Implement monitoring and logging mechanisms with alerts for real-time visibility into data, facilitating quick responses to bottlenecks, anomalies, or discrepancies.
  • Trace the lineage and transformation history of data for transparency and accountability. For example, in AWS, store the data state after each processing step in S3, enabling querying with Athena.

2. Data Consistency​

  • Ensure data consistency across different stages of the pipeline by defining and enforcing data schemas.
  • Implement proper version control for schema changes and maintain documentation for better traceability.

3. Data Quality​

  • Establish data quality checks to identify and rectify anomalies, emphasizing completeness and accuracy.
  • Document data validation and cleaning standards at share them across the organisation. At CRUK we adhere to a common Master Data Quality Standard.

4. Idempotency​

  • Design pipelines to be idempotent, allowing for repeated execution without adverse effects on data integrity.
  • Use mechanisms such as delta detection to handle duplicate data and prevent unintended side effects. Delta detection involves identifying changes in data since the last pipeline execution, often achieved by comparing timestamps or using unique identifiers.
  • Consider implementing snapshot resets to manage and maintain the pipeline state, ensuring consistency and reliability even in error scenarios. It's essential to think beyond the ideal scenario and plan for edge cases and errors in your pipeline execution.

5. Scalability​

  • Build pipelines that can scale horizontally to accommodate changing data volumes.
  • Leverage cloud services and parallel processing techniques to ensure scalability and optimal performance.
  • Use frameworks built for scalability and efficiently processing big data, like Apache Spark.

6. Efficiency​

  • Optimize data processing tasks for speed, resource utilization, and efficient storage.
  • Utilize caching mechanisms, compression techniques, and parallel processing to enhance pipeline efficiency.
  • Consider access patterns for data and using appropriate storage formats. Consider the downsides of using CSV as a storage format, such as increased storage space and slower query performance compared to more optimized formats like Parquet. Choose storage formats based on efficiency for both writing and retrieval, considering factors like compression and columnar storage benefits.

7. Flexibility/Extendability​

  • Design pipelines to be flexible and adaptable to evolving data requirements.
  • Use modular components and standardized interfaces, allowing for easy integration of new data sources or changes in data structures.

8. Follow patterns​

  • Follow established design patterns, such as the builder pattern in code or using abstract classes to create modular, reusable, and maintainable components within your data pipelines.

9. Complexity​

  • Keep pipeline designs simple and modular, facilitating maintainability.
  • Document and regularly review complex transformations and logic. Conduct code reviews and knowledge-sharing sessions.

By incorporating these best practices into your data engineering processes, you can ensure the reliability, efficiency, and scalability of your data pipelines. Regularly review and update your pipeline architecture to accommodate changing business needs and technological advancements. Remember, a well-designed data pipeline is the foundation for robust and effective data-driven decision-making.