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Learn how to achieve sub-second responses with Apache Druid
Learn how to achieve sub-second responses with Apache Druid. This article is an in-depth look at how Druid resolves queries and describes data modeling techniques that improve performance.
Apache Druid – Recovering Dropped Segments
Apache Druid uses load rules to manage the ageing of segments from one historical tier to another and finally to purge old segments from the cluster. In this article, we’ll show what happens when you make a mistake on the load rules which results in the premature removal of segments from the cluster. We’ll also show how to correct the mistake and recover the segments before they are permanently deleted.
Real-Time Analytics: Building Blocks and Architecture
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.
Transactions Come and Go, but Events are Forever
For decades, analytics has focused on Transactions. While Transactions are still important, the future of analytics is understanding Events.
What’s new in Imply Polaris – Our Real-Time Analytics DBaaS
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
Elasticsearch and Druid
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
Wow, that was easy – Up and running with Apache Druid
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.
Top 7 Questions about Kafka and Druid
Read on to learn more about common questions and answers about using Kafka with Druid.
Real-time Analytics Database uses partitioning and pruning to achieve its legendary performance
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.