![]() ![]() (We’ll see how Nemo improves on this in the next section.) For user browsing behavior, do we search for “click”, “page views”, or “browse”? A common solution is free-text search on table names and even columns. Where can I find data about _? If we don’t know the right terms, this is especially challenging. ![]() Questions we ask in the data discovery processīefore discussing platform features, let’s briefly go over some common questions in the data discovery process. They help answer “Where can I find the data?” and other questions that users will have. Lyft found that 25% of time is spent on data discovery ( source)ĭata discovery platforms catalog data entities (e.g., tables, ETL jobs, dashboards), metadata (e.g., ownership, lineage), and make searching them easy. Similarly, 80% of Shopify’s data team felt that the discovery process hindered their ability to deliver results. Before Lyft implemented their data discovery platform, 25% of the time in the data science workflow was spent on data discovery. How would you find the right tables and columns to use? How would you quickly assess their suitability?įinding the right data can take a lot of time. You need data for analysis, or to build a machine learning system. Imagine yourself as a new joiner in the organization. Data discovery platforms help us find data faster.
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