Druid Analytics with Jad Naous

Large companies generate large volumes of data. This data gets dumped into a data lake for long-term storage, then pulled into memory for processing and analysis. Once it is in memory, it is often read into a dashboard, which presents a human with a visualization of the data. 

The end-user who is consuming this data is often a data scientist who is looking at the data to find trends and design new machine learning models. Another kind of user is the operational analyst. An operational analyst is creating complex queries across this data to find latencies in the infrastructure, or perhaps slicing and dicing clickstream data that is coming from online advertisements, in order to figure out how to tweak those advertising algorithms and spend money more effectively.

For an operational analyst, a key use case for a data warehouse is fast, interactive querying. The operational analyst needs to be able to query the data to quickly create a dashboard, make judgments based on that dashboard, and then change the query slightly to look at a slightly different dashboard.

Druid is a high-performance database that is used for these kinds of queries. Druid is used for ad-hoc queries and operational analytics. Imply Data is a company that builds visualization, monitoring, and security around Druid. Jad Naous is vice president of R&D for Imply, and he joins the show to talk about the use case for Druid, the architecture, and the business model of Imply.

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Transcript

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