ETL stands for Extract, Transform, Load. It involves extracting data from source systems, transforming it into the desired format, and loading it into a target system.
ETL transforms data before loading it into the data warehouse, while ELT loads data first and performs transformations within the data warehouse.
Common tools include Informatica, Talend, SSIS, Pentaho, and Apache Nifi.
Data transformation involves cleaning, filtering, merging, splitting, and converting data into the desired format for analysis.
Data staging is the process of temporarily storing data between extraction and loading. It ensures data integrity and smooth transitions between ETL phases.
Full load transfers all data every time, while incremental load only updates changes since the last load.
By implementing data validation checks, error handling, and logging mechanisms to catch inconsistencies.
SCD refers to how historical data is stored and managed in a data warehouse. There are various types: Type 1 (overwrite), Type 2 (add new record), Type 3 (keep a version of the change).
Schema changes can be managed by updating the ETL process, adjusting transformation logic, and modifying target data structures.
ETL is the process of data extraction, transformation, and loading, whereas ETL testing verifies data accuracy, completeness, and performance of the ETL pipelines.
Surrogate keys are unique identifiers assigned to rows in the target table, often used in place of natural keys to ensure uniqueness and performance.
Data mapping involves defining how source fields correspond to target fields during the transformation process.
Lookup transformation is used to join data from different sources, typically to reference data from a secondary dataset or validate information.
By implementing error-handling strategies like logging, exception handling, retry mechanisms, and alerting to track and address failures.
Challenges include performance bottlenecks, memory management, and handling real-time data or streaming data pipelines.
In real-time ETL, tools like Apache Kafka, AWS Kinesis, or Apache Flink are used to process streaming data with minimal latency.
By encrypting sensitive data, using secure connections, implementing access controls, and adhering to compliance standards.
Metadata describes the structure, definitions, and rules for data, helping guide the ETL process and providing context for transformations.
Data aggregation involves compiling and summarizing detailed data into a more simplified form, which can help in reporting and analysis. For example, daily sales data can be aggregated to show monthly sales trends.
Techniques include using partitioning, indexing, parallel processing, bulk loading methods, and optimizing transformations to process data in chunks rather than in a single pass.
