Amazon Glue is a fully managed Extract, Transform, and Load (ETL) service that makes it easy for customers to prepare and load their data for analytics. It automates the time-consuming steps of data preparation, cleaning, and loading, allowing customers to focus on their analytics. Amazon Glue uses a powerful serverless architecture that can scale up or down to match the workload. It can process data stored in Amazon S3, Amazon RDS, Amazon Redshift, and other databases, making it easy to move and transform data between different sources. Amazon Glue also includes a library of over 1,000 pre-built connectors and transformations, making it easy to quickly build ETL jobs.
Data lineage is a data management concept in which information about the lifecycle of a data element is tracked. It involves capturing and storing metadata as data moves from one system to another, as well as tracking how the data is processed and used. Data lineage allows companies to gain a better understanding of how their data is generated and used, in order to improve security, accuracy, and data governance. In addition, it helps make sure data consistency, integrity and quality, as well as enforce regulatory compliance. Data lineage can also provide insight into areas where data can be used more efficiently and cost effectively. By understanding data lineage and how data moves from one system to another, companies can make changes and improvements that will result in more efficient data management.
Having data lineage for Amazon Glue provides the ability to trace and audit each step of the data transformation that occurs during a data extract, load, and transformation (ETL) job. Analysts and developers can use this level of traceability to quickly identify causes of errors and improve the quality of their data. Additionally, data lineage can be used to provide regulatory compliance, build confidence, and to ensure that all data is handled correctly during each step of the ETL process.
To set up data lineage using Amazon Glue and Secoda, users must first create a data-lake-to-data-warehouse data pipeline. In this pipeline, users will have to use Amazon Glue to extract, transform and load data from the data lake into a data warehouse. Additionally, users will have to use Secoda, which is an automated data lineage tool, in order to track metadata associated with the data elements, such as source, transformation queries, and lineage mapping. With this combination of tools, users will have an effective data lineage set up that can track data movement across the data pipeline.
Secoda is a data discovery tool for the modern data stack. It helps organizations discover, explore, and analyze data from a variety of sources, including databases, data lakes, and cloud services. With Secoda, users can quickly and easily find the data they need to make informed decisions. It provides a unified view of all data sources, allowing users to explore and visualize data in an intuitive, interactive way. Secoda also enables users to collaborate on data exploration and analysis, making it easier to share insights and ideas.