Data Pipelines
ETL vs ELT.
ETL Process
ETL (Extract, Transform, Load) processes and data warehousing.
- ETL is used to move data from operational databases to data warehouses
- The process involves multiple stages to ensure data quality and performance
Steps:
- Operational Database vs Data Warehouse:
- Operational databases are designed for CRUD (Create, Read, Update, Delete) transactions
- Data warehouses are optimized for analytics and historical data tracking
- Extraction Step:
- Data is first extracted into flat files (e.g., CSV, TSV, XML, JSON)
- Flat files serve as snapshots and allow for easier troubleshooting
- Raw Database:
- Data is loaded into a raw database without complex transformations
- This step helps isolate data issues from ingestion problems
- Staging Database:
- Used for data mapping, deduplication, and initial transformations
- Prepares data for the final data warehouse
- Quality Assurance (QA):
- QA checks are performed at multiple stages of the ETL process
- Ensures data integrity and accuracy before entering the data warehouse
- Logging and Error Tracking:
- Critical for monitoring ETL performance and troubleshooting
- Tracks file loads, procedure execution, and data transformations
- Data Warehouse Structure:
- Uses fact and dimension tables (e.g., F_Transactions for fact tables)
- May involve multiple levels of aggregation and transformation
- Naming Conventions:
- Consistent abbreviations and prefixes are used (e.g., F* for fact tables, D* for dimension tables)
- Helps in understanding table types and purposes
- Iterative Transformations:
- Data may undergo multiple transformations after entering the data warehouse
- Each level of transformation is often tracked (e.g., T1, T2, etc.)
- Importance of Clean Data:
- Emphasizes the need for thorough data validation and cleaning
- Incorrect data can lead to poor decision-making
Pipelines vs ETL
ETL can be considered a specific type of data pipeline, but not all data pipelines are ETL. The choice between ETL and other types of data pipelines depends on the specific data processing requirements, the nature of the data sources and destinations, and the desired outcomes of the data integration process.
- Scope and Purpose:
- ETL is a specific type of data pipeline focused on extracting data from sources, transforming it, and loading it into a destination, typically a data warehouse.
- Data pipelines are a broader concept that encompasses various data movement and processing techniques, including but not limited to ETL.
- Processing Method:
- ETL typically operates in batches, processing data in discrete chunks at scheduled intervals.
- Data pipelines can handle both batch and real-time streaming data, offering more flexibility in data processing.
- Transformation:
- In ETL, transformation is a mandatory step that occurs before loading data into the destination.
- Data pipelines may or may not include transformation steps. Some pipelines use ELT (Extract, Load, Transform) where transformation occurs after loading.
- Continuity:
- ETL processes usually have a defined end point after loading data into the destination.
- Data pipelines are often continuous, supporting ongoing data streams and potentially triggering additional processes or workflows after data is loaded.
- Flexibility:
- ETL is more structured and follows a specific sequence of operations.
- Data pipelines can be more flexible, accommodating various data processing needs and architectures.
- Use Cases:
- ETL is commonly used for data warehousing and business intelligence applications.
- Data pipelines have broader applications, including real-time analytics, machine learning, and supporting modern data lake architectures.
- Data Volume and Velocity:
- ETL is often better suited for handling large volumes of data in scheduled batches.
- Data pipelines can manage both high-volume batch processing and high-velocity real-time data streams.