Petabyte-Scale Data in Real-Time: ClickHouse®, S3 Object Storage, and Data Lakes | ClickHouse Guide
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These days new ClickHouse applications start with petabyte-sized datasets and scale up from there. Fortunately, ClickHouse gives you open-source tools for real-time analytics on big data: MergeTree backed by object storage as well as reading on data lakes. We start by showing you popular design patterns for ingest, aggregation, and queries on source data. We then dig into specific best practices for defining S3 storage policies, reading from Parquet data, backing up, monitoring, and setting up high-performance clusters in the cloud. It’s all open source and works in any cloud. Join us!
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CHAPTERS
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Introduction - 00:00
Challenges of Real-time Big Data - 03:06
Overview of ClickHouse and Its Features - 05:10
Designing analytics on real-time big data - 08:24
Implementing Flexible Compute - 14:18
Scaling MergeTree Tables with S3 Storage - 25:24
Use Parquet in Read-only Data Lakes - 42:12
Cheatsheet For Petabyte-Scale ClickHouse Clusters - 54:06
Q&A and Closing Remarks - 57:06
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PRESENTATION SLIDES
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MANAGED CLOUD FOR CLICKHOUSE
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* https://altinity.com/managed-clickhouse/
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RESOURCES
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* Altinity Knowledge Base for ClickHouse: https://kb.altinity.com/
* Altinity Documentation for ClickHouse: https://docs.altinity.com
* Altinity Stable Builds for ClickHouse: https://altinity.com/altinity-stable/
* Training for ClickHouse: https://altinity.com/clickhouse-train...
* Altinity vs other ClickHouse vendors: https://altinity.com/altinity-cloud-v...
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