They are a handy data lineage tool for these reasons: Streaming platforms send off events (messages with data) to connected services in real time. Regardless of the collection method chosen, the next step in mapping data lineage is to push the metadata to tools that process it before it can be taken by the database. Finally, manual lineage entry may be time-consuming and painstaking, but the ability to do fixes by hand can be indispensable. ![]() To deal with that, specialized software parses the data transformation code, identifies and extracts lineage information. These solutions provide robust and accurate information, but it’s rare that the entire data landscape is compatible with them. Alternatively, OpenLineage seeks to standardize metadata so that data lineage can easily be pieced together. Each of them has its pros and cons, and the ultimate choice depends on your use case.įor example, modern BI/data visualization tools like Tableau and Looker keep track of their data lineages and expose it via APIs. There’s a variety of approaches for gathering the information needed for a system’s data lineage. ![]() You can use streaming to build ETL (extract, transform, load) processes that automatically ingest and transform collected data and maintain separation of ETL concerns. Besides that, compliance with business standards like BCBS 239 and the GDPR means that companies must be able to make data-driven reports on a largely automated basis.Įven if you brush aside the above, it’s indisputable that graph DBs are the best choice for data lineage, but Memgraph raises the bar by making end-to-end data lineage automation feasible with built-in support for streaming. Automating data lineage mapping saves a lot of resources. technical) and even more by the particularities of your organization’s data landscape. ![]() The specifics vary by the type of data lineage (business vs. A challenge all data lineage projects face is figuring out the best way to get lineage information in a database and keep it up to date.
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