Various data sources with purposes and relevance

Building data architecture that automates your business involves designing a comprehensive system that integrates data from various sources and automates repetitive tasks, resulting in improved efficiency and streamlined operations. This approach empowers your business to leverage data-driven insights for informed decision-making and stay agile in a rapidly evolving market.

Data requires the ability to extract it. Is the process of retrieving specific data from various sources for further processing, analysis, or storage. This involves identifying and isolating relevant data from databases, documents, web pages, or other data repositories. The extracted data is often transformed and loaded into a different system or data warehouse, making it accessible for analysis, reporting, or integration with other datasets. Data extraction is a critical step in data integration, enabling organizations to consolidate and utilize data from diverse sources effectively.

When it come to raw data transformation, this is the process of converting one format or structure to another to make it suitable for analysis, integration, or storage. This process involves various operations such as cleaning, filtering, aggregating, and enriching the data to ensure it is accurate, consistent, and usable. Data transformation is often a key step in the ETL (Extract, Transform, Load) process, where data is extracted from its source, transformed to fit the needs of the target system, and then loaded into a data warehouse or other storage solutions. Effective data transformation improves data quality and enables more meaningful insights and decision-making.