Welcome to the tag category page for Data integration!
Data pipelines are a series of tools and processes designed to automate the flow and transformation of data from a source to a destination. These destinations may include data warehouses, data lakes, analytics databases, and other repositories. The process of data pipeline involves ingesting the raw data from various sources and then transforming, validating, and loading it into a target system. ETL (Extract, Transform, Load) is a type of data pipeline that involves the process of extracting data from various sources, transforming it in some way to make it suitable for analysis, and then loading it into a destination system. AWS Data Pipeline is a popular web service that automates the movement and transformation of data. There are many other types of data pipelines that can be used depending on the specific needs of an organization. Overall, data pipelines are essential for organizations that need to move and transform large volumes of data quickly and efficiently. They allow businesses to gain valuable insights from their data in a timely manner, ultimately helping them make better decisions based on that information.
A data catalog is an organized inventory and detailed list of all data assets in an organization that helps manage and discover data. It uses metadata management to enable data analysts, scientists, stewards, and other data consumers to find and understand datasets for extracting business value. It includes data from the World Bank's microdata, and open-source data catalog tools. Some examples of data catalog tools are Amundsen by Lyft and LinkedIn DataHub. The difference between a data catalog and a data warehouse is that the former helps find, understand, trust, and use data, while the latter stores structured data.
