TransformĪs the name suggests, the transformation stage is when the raw data collected in the extraction stage is processed for operational use. However, if the system cannot determine changes in data in the staging area, then full extraction methods are suitable for this stage. If data is continually pulled from remote sources such that records are being changed, then comparisons between old and new data objects can provide opportunities for optimization through partial extraction. Data mustn’t be directly extracted into a data warehouse infrastructure to avoid undermining that warehouse’s data structures and the reliability of analytics conducted on them. ExtractĮxtraction is the process of taking data from various heterogeneous sources and moving them to a staging area (such as a data lake) in preparation for cleaning and processing. This process is called Extract Transform Load, or ETL. Regardless of where data is traveling to, an organization must have a process to ensure that it gets there exactly as it should. In contrast, data lakes can serve as either a landing space for raw data or a space to power dynamic data structuring and analytics. Outside of architectural differences, data warehouses will often serve as resources to quickly draw reports and analytics. Data Warehouses: If a data lake is a pool of raw data collected and stored at scale, data warehouses are more focused locations to store cleaned and structured data for use.ĭata lakes and warehouses will use different hardware infrastructures and file management systems to optimize data storage.The strength of a data lake is that it can store massive quantities of information at scale. This stopping point typically serves as a staging area, where data lands before being organized, processed, or analyzed. Data Lakes: Data lakes are large and centralized collections of structured and unstructured data.Two common forms of data storage in cloud environments will impact extraction and transformation processes. To support these operations reliably and predictably, data and cloud engineers use different approaches to structuring these pipelines and, in most cases, structuring how data is stored. Data must be centralized, structured, organized, and finally stored during this process. Cloud Data Storage and Structureĭue to the complexity of cloud data storage and processing, there are equally complex pipelines of automated workflows that take information from the point of collection through its lifecycle. What is extract transform load? ETL, which stands for extract, transform and load, is a process that combines data from multiple sources of data into a single, consistent data store.
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