![]() ![]() Leveraging ETL for collaborating and merging can be time-consuming and complex, as the raw data has to be transformed first. Data sources can range from traditional relational databases to NoSQL databases hosted in on-premises data centers or by various cloud providers. It is challenging to integrate data when different teams and organizations all use different systems, structures, and processes. ( Read all about big data analytics.) Collaborating or merging data with other teams Furthermore, their inherent scalability allows for dynamically scaling up and down the resources according to demand. Leveraging ELT processes enables organizations to gain several advantages in such scenarios.Ĭloud-based data warehouses used in ELT can handle a massive amount of data and consist of processing power to process it efficiently. Handling massive amounts of data is often a challenge. ELT extracts and loads data from various sources into a single data warehouse or data lake, enabling users to access and query data from a centralized location.ĮLT leverages the computational power of modern data warehouses such as Amazon Redshift and Google BigQuery, enabling real-time or near-real-time reporting. Use cases: when & why to use ELT Reporting and analysisĮLT facilitates reporting and analysis by preparing and organizing data for efficient querying and analysis. For example, data normalization processes like removing data duplications, missing values, summarization, aggregation, and mapping processes.įurther transformation steps such as data partitioning, validation, and encryption may be required, depending on the organizational requirement and the underlying technology used for data analytics. Several transformation processes happen during this phase. The final phase involves converting the data into the required format before it is used for further analysis. After that, the raw data will be loaded into the data warehouse in bulk or incrementally, depending on the underlying infrastructure. This is similar to offline data extraction in ETL, where raw data is stored temporarily. ![]() LoadĮxtracted data is loaded directly into the destination, often a staging area within the data warehouse. Examples include web pages, email repositories, customer relationship management (CRM) systems, Enterprise Resource Planning (ERP) systems, APIs, etc.ĭata can be in unstructured, semi-structured, and structured formats such as JSON, XML, or data tables. Extractĭata from different sources is extracted as it is.
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