Tag - apache druid

Druid Kafka Supervisor
13Oct

Understanding Apache Druid Supervisor and its specification for real-time data ingestion from Apache Kafka

Although both Apache Druid and Apache Kafka are potent open-source data processing tools, they have diverse uses. While Druid is a high-performance, column-store, real-time analytical database, Kafka is a distributed platform for event streaming. However, they can work together in a typical data pipeline scenario where Kafka is used as a messaging system to ingest and store data/events, and Druid is used to perform real-time analytics on that data. In short, the indexing is the process of loading data in Druid...

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06Jul

The significance of deep storage in Apache Druid

The phrase “deep storage” refers to the long-term storage system used by Apache Druid, where past data segments are preserved for durability and retrieval in the future. Druid stores data in files called segments and deep storage is the place where segments are stored. Even though Druid’s native integration with Apache Kafka (can read here how to integrate Druid with Kafka) and Amazon Kinesis, which allows query-on-arrival at millions of events per second, low latency ingestion, etc., and eventually enables us to...

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Forging Apache Druid with Apache Kafka for real-time streaming analytics
16Jun

Forging Apache Druid with Apache Kafka for real-time streaming analytics

A real-time analytics database called Apache Druid is developed for quick slice-and-dice analysis on massive data volumes. The best data for Apache Druid is event-oriented and frequently utilized as the database backend for analytical application GUIs and for highly concurrent APIs that require quick aggregations. Druid can be leveraged very effectively where real-time ingestion, fast query performance, and high uptime are crucial. At the other end, Apache Kafka is gaining outstanding momentum as a distributed event streaming platform with excellent...

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08Jun

Apache Druid

is database built for data in motion Sub-second queries at any scale Execute OLAP queries on high-dimensional, high-cardinality data sets with billions to trillions of rows in milliseconds without pre-defining or caching queries. Maximum concurrency at the cheapest cost Build real-time analytics apps with constant performance that can handle 100–100,000 queries per second using a highly effective architecture that requires less infrastructure than other databases. Real-time and historical insights Druid's native integration with Apache Kafka and Amazon Kinesis, which allows query-on-arrival at millions of events...

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