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This blog identifies the key technical considerations for real-time analytics. It answers what is the right data architecture and why. It spotlights the technologies used at Confluent, Reddit, Target and 1000s of others.

For decades, analytics has focused on Transactions. While Transactions are still important, the future of analytics is understanding Events.

This blog explains some of the new features, functionality and connectivity added to Imply Polaris over the last two months. We've expanded ingestion capabilities, simplified operations and increased reliability by running across multiple availability zones

This blog will help you understand what Elasticsearch and Druid do well and will help you decide whether you need one or both to reach your goals

The objective of this blog is to provide a step-by-step guide on setting up Druid locally, including the use of SQL ingestion for importing data and executing analytical queries.

Read on to learn more about common questions and answers about using Kafka with Druid.

Tales at Scale cracks open the world of analytics projects and shares stories from developers and engineers who are building analytics applications or working within the real-time data space. One of the key areas of focus for the show is Apache Druid.

Apache Druid uses partitioning (splitting data) and pruning (selecting subset of data) to achieve its legendary performance. Learn how to use the CLUSTERED BY clause during ingestion for performance and high concurrency.

This blog explains how developers can leverage Imply Polaris to embed robust visualization options directly into their own applications without them having to build a UI. This is super important because consuming analytics is an expected part of each app experience. For example, we get our health stats from our fitness apps and we analyze traffic insights for the fastest commute.