ELTkäytäntöjä

ELTkäytäntöjä refers to Extract, Load, Transform practices in data management and business intelligence. This process involves moving data from various sources into a central repository, such as a data warehouse, and then transforming it to suit analytical needs. The extraction phase pulls data from operational systems, databases, and files. The loading phase then places this raw data into the target data warehouse. Finally, the transformation step cleans, validates, standardizes, and aggregates the data, making it ready for reporting and analysis.

ELT differs from the traditional ETL (Extract, Transform, Load) process primarily in the order of operations. In ELT, the transformation occurs *after* the data is loaded into the target system. This approach is particularly advantageous with modern cloud-based data warehouses and big data technologies that possess significant processing power. These platforms can handle large volumes of raw data and perform complex transformations efficiently. ELT allows for greater flexibility, as raw data can be stored and transformed in multiple ways as business requirements evolve, without needing to re-extract data from the source. It also enables faster loading of data, as the computationally intensive transformation step is deferred. Common tools used for ELT include cloud data warehouses like Snowflake, Amazon Redshift, and Google BigQuery, often combined with data integration platforms.