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Analytics aren’t just for internal stakeholders anymore. If you’re building an analytics application for customers, then you’re probably wondering…what’s the right database backend?

Every year industry pundits predict data and analytics becoming more valuable the following year. But this doesn’t take a crystal ball to predict. There’s instead something much more interesting happening that’s going to change everything in the analytics world

Traditional RDBMSs simply aren’t designed to handle massive datasets or high concurrency. They are natively single-server, single-threaded, and are designed to only scale vertically (by increasing the size of the server) rather than horizontally (by increasing the number of servers).

Recently, I joined Imply. I’ll further discuss this on a different post, but what really amazed me during these first few days, is how much this company and the team believe in eating our own dog food.

Snowflake's IPO today is a testament to the importance of data analytics and the cloud in business today. This post describes how Imply complements cloud data warehouses for a growing set of workloads called interactive data applications, which require sub-second query response at high user concurrency.

One of the most important considerations when selecting an analytics platform is its suitability to conduct the required analyses over specific types of data within a given performance threshold. A helpful way to think about this is to utilize the concept of temperature-tiered analytics to align analytics needs with data architectures.

A new benchmark test indicates that Apache Druid, the engine that drives the Imply real-time data platform, delivers 3 times the speed and 12 times the price-performance of Google BigQuery.

This short post describes how Druid compares against enterprise data warehouses. Druid is not a data warehouse. It's a real-time database for user-facing analytics application needing sub-second query response at high concurrency.