Cloud data lakes and data warehouses, often thought of as generationally-competitive analytics repositories, can in fact be complementary, and work together harmoniously, especially in the cloud. The lake is a great place for landing raw data, exploring it, vetting it and performing bespoke data discovery. The warehouse, on the other hand, works well for data that has been vetted, structured and is poised for repeatable, operational analysis.
For all this to work, data can be ingested and moved in raw form, but it must then be refined for analytic use. While the refinement can be performed in the lake, the warehouse, or both, data preparation platforms deeply enhance the cloud lake/warehouse symbiosis. These platforms are purpose-built for production-level data transformation and geared towards the professionals focused on it. Data prep provides value that optimizes exploratory analytics and business intelligence and, increasingly, machine learning as well.
The webinar recording features GigaOm analyst Andrew Brust with Will Davis, Head of Marketing at Trifacta, a leader in self-service data preparation.
You will learn:
- About commodities versus value-adding technologies, in the data pipeline arena
- Why BI platforms’ built-in data prep tools may fall short
- Where hand-coded data prep can introduce inefficiencies