What is ETL?
- data integration process
- commonly used in Data Engineering/Data Analysis
- collecting data → transforming in usable form → load to destination system for analysis
- ETL Pipelines: essential for making raw data accessible, actionable and valuable for the purpose of decision making.
E: Extract
- collecting data from sources
- essential as data is gathered from multiple sources like databases, applications and files.
- Goal: Bring data in all different formats(structured ,semi-structured, unstructured) together from different systems.
- Important to collect the data without any alteration.
- Examples of Data Sources:
- Relational Databases: MySQL, PostgreSQL, MS SQL Server.
- NoSQL Databases: MongoDB, Cassandra, or DynamoDB.
- Flat Files: CSV, JSON, XML, or Parquet files.
- APIs: Data retrieved from third-party services via RESTful APIs.
- Cloud Services: Data from cloud platforms like AWS, Azure, or Google Cloud Storage.
Extraction Methods
- Full Extraction: all data extracted
- Incremental Extraction: recent/changed data extracted; based in timestamp of CDC.
- Real-Time Extraction: continuos extraction
T: Transform
- essential to making the extracted data useful.
- manipulation/cleaning of raw data to fit the purpose.
- making the data suitable to anlayze
Transformation Steps
- Data Cleaning
- Data Mapping
- Data Aggregation
- Normalization or Denormalization
- Enrichment
- Data Formatting
Transformation Methods
L: Load
- loading data to target systems for analysis
- Examples for target systems
Strategies
- Full Load: Loading the entire transformed dataset into the target system. This can be costly in terms of time and resources for large datasets.
- Incremental Load: Loading only the data that has changed or been added since the last load, which is more efficient for large datasets.
- Real-Time Load: Continuously loading data as it is transformed, typically seen in real-time analytics systems.
Tools and Technologies
Frameworks
- Airflow
- Apache Nifi: Data integration tool for automating data flow between systems.
- AWS Glue: Managed ETL service in AWS that automates much of the data preparation work.
- Azure Data Factory: Cloud-based ETL service for orchestrating and automating data flows.
- Talend: A powerful ETL tool with a graphical interface for building data pipelines.
- Pentaho: Data integration tool that provides ETL capabilities, along with analytics and reporting.
Benefits of ETL
- Data Consolidation: ETL allows you to aggregate data from disparate sources into a single, unified system, making it easier to analyze and derive insights.
- Data Quality: Through the transformation step, ETL can ensure that the data is clean, accurate, and consistent.
- Scalability: ETL systems can scale to handle growing amounts of data efficiently.
- Time Efficiency: Automated ETL processes reduce the time needed for data preparation, freeing up time for analysis.
Challenges of ETL
- Data Quality Issues: If the source data is incomplete or incorrect, it will impact the accuracy of insights derived from the transformed data.
- Performance: Complex transformations and large data volumes can slow down ETL processing. Optimizing ETL pipelines for performance is essential.
- Complexity: As the number of data sources and transformations increases, managing ETL pipelines becomes increasingly complex.
- Real-Time Processing: Implementing real-time ETL pipelines requires more sophisticated infrastructure and technology (e.g., Apache Kafka, stream processing).
ETL vs. ELT (Extract, Load, Transform)
An alternative to ETL is the ELT (Extract, Load, Transform) process, where data is loaded into the destination system first, and then transformed. This approach is more common when working with cloud-based systems and data lakes, where data can be transformed within the storage environment itself.
- ETL: Transformation happens before the data is loaded into the target system.
- ELT: Data is loaded first, and then transformation happens inside the target system.