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New in Apache Druid 27: Querying Deep Storage
In realtime analytics, a common scenario is that you want to retain a lot of (years of) historical data in order to run analytics over a longer period of time. But these analytical queries occur infrequently and their performance is usually not critical. The bulk of everyday queries, however, accesses only a limited set of relatively fresh data, typically 1 or 2 weeks worth.
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Using Druid with MinIO
With on premise setups, compute/storage separation is often implemented using a NAS or similar storage unit that exposes an S3 API endpoint.
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Druid Sneak Peek: Graphical Data Exploration
Jul 30, 2023 • blog, apache, druid, imply, visualization, tutorial
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Merging Realtime Segments in Apache Druid
So, you want your realtime analytical queries to be really fast, and that’s why you selected Apache Druid! Today, let’s have a look at another aspect of how Druid achieves its amazing performance.
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Analyzing GitHub Stars with Imply Polaris
Jul 12, 2023 • blog, druid, imply, polaris, sql, datamodeling, tutorial
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New in Apache Druid 27: Querying Deep Storage
In realtime analytics, a common scenario is that you want to retain a lot of (years of) historical data in order to run analytics over a longer period of time. But these analytical queries occur infrequently and their performance is usually not critical. The bulk of everyday queries, however, accesses only a limited set of relatively fresh data, typically 1 or 2 weeks worth.
-
Using Druid with MinIO
With on premise setups, compute/storage separation is often implemented using a NAS or similar storage unit that exposes an S3 API endpoint.
-
Druid Sneak Peek: Graphical Data Exploration
-
Merging Realtime Segments in Apache Druid
So, you want your realtime analytical queries to be really fast, and that’s why you selected Apache Druid! Today, let’s have a look at another aspect of how Druid achieves its amazing performance.
-
Analyzing GitHub Stars with Imply Polaris