It is a type of data transformation that involves manipulating data to generate new data values.Īt a technical level, data derivation involves using programming logic or mathematical formulas to create new data values based on one or more source data elements.Ī simple example of data derivation is calculating a new data element based on existing data elements in a database. Derivationĭata derivation is the process of creating new data elements or modifying existing data elements from one or more source data elements using a defined set of rules or formulas. The goal of data deduplication is to reduce data storage space and improve data management efficiency by eliminating redundant data. This process can be done using a variety of techniques, such as checksums, hashes, or content-aware algorithms. These steps can happen within a data pipeline automated by an ETL tool, but they often require a mix of SQL and Python scripting to build out an end-to-end workflow: Deduplicationĭata deduplication is a technique used in data management to identify and eliminate duplicate data entries within a data set.ĭata deduplication involves analyzing the content of data blocks within a data set and comparing them to identify duplicates. There are some additional steps that are often required to shape data into a dataset that’s easy to extract business insights from. Initial data transformation steps require standardizing the data and choosing the ETL tools that appropriately fit the type and format of the data. They may be structured, semi-structured, or unstructured. Ingested data may exist in different formats like JSON, XML, or CSV. It’s important for both data engineers and analysts to be aware of and understand data transformations, regardless of where they occur in the data pipeline. This data can be used to analyze trends and patterns across different stores and regions, identify opportunities for growth and optimization, and make data-driven decisions to improve business performance. Since most data warehouse tools like Redshift and Snowflake support massive parallel processing of large volumes of data and are now more accessible due to affordable pricing, ELT has become more popular.īy using ETL(or ELT) to centralize data from its various data sources, the retail store from the example above can create a single source of truth for its sales, inventory, and customer data. This process is called ELT (Extract Load Transform) and enables users to take advantage of the massive processing capabilities of modern data warehouses to run more efficient queries. They could also happen after the data is loaded into the target system. This involves mapping the data and matching it appropriately to the current schema then ensuring data load happens in the target system.ĭata transformations don’t always happen after data extraction. The final step is to load the transformed data into the data warehouse. The next step is to transform the extracted data into a format that is suitable for the data warehouse. These sources could be relational databases, a Customer Relationship Management (CRM) tool, or a Point of Sale (POS) system. The first step is to extract the data from different sources. The business collects data on daily sales, inventory, and customer demographics on a daily basis and wants to integrate this data into a data warehouseor data lake for data analysis and reporting. A good example of this is a retail business that operates multiple stores across different regions. What is ETL transformation?ĮTL transformation is the process of converting raw data from source systems into a format that is suitable for the target system. This is where data transformationwith ETL (Extract, Transform, Load) comes in. This data can be transformed into a useful format and integrated into a single repository, such as a data warehouse, to enable data-driven decision-making. However, the data collected is often incomplete, inconsistent, and spread across different data sources. Today, businesses and organizations generate and collect massive amounts of data from a variety of sources, including social media, IoT devices, and legacy systems.
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