> ## Documentation Index
> Fetch the complete documentation index at: https://private-7c7dfe99-fix-nav-issues.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Real-time analytics

> Learn how to build real-time analytics applications with ClickHouse Cloud for instant insights and data-driven decision making

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In analytics, "real-time" usually means the user experience itself feels live.
A customer refreshes a dashboard, opens a leaderboard, or investigates an issue and expects the data to reflect what just happened.
Technically, this means not just delivering low-latency analytical queries but achieving this while data is being continuously inserted at high volume.

<h2 id="system-properties">
  Properties of a real-time analytics system
</h2>

When customers evaluate a real-time analytics platform, they often focus only on query latency. "Can it return an answer within 50ms?" is a reasonable question, and one most analytical engines can answer convincingly if you throw enough compute at a static dataset.

The question that decides what users actually experience is harder. It is whether the system can return a 50ms answer **on data that arrived a second ago**, while ingestion is still running, and while other users are also querying.

Data availability requires thinking about end-to-end time-to-insight, which has three components.

* **Time to ingest**: How long does it take for newly generated data to land in the platform and become durably stored?
* **Time to transform and prepare**: How long does it take to clean, enrich, join, pre-aggregate, or update the serving structures (materialized views, rollups, indexes) that queries actually hit?
* **Time to query**: How long does it take to plan and execute the read once the data is available?

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<h2 id="clickhouse-rta">
  How ClickHouse powers real-time analytics
</h2>

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        With [ClickPipes](/integrations/clickpipes/home), available exclusively in ClickHouse Cloud, you get a turn-key integration engine that makes ingesting large volumes of data effortless. Select an incoming data source and format, tune your schema, and let your pipeline run.

        ClickHouse's extensive library of [table engines](/reference/engines/table-engines) supports ingesting data from Kafka topics, S3 buckets, OLTP databases, and more. Unlike other OLAP databases that require you to batch inserts for high throughput, ClickHouse handles smaller payloads equally well — [asynchronous inserts](/concepts/features/operations/insert/asyncinserts) automatically batch them for optimal write performance.
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        [Materialized views](/concepts/features/materialized-views) make transformations seamless — automatically triggered when new data is inserted, they extract, aggregate, and modify data as it arrives without bespoke pipelines. Chaining them together adds modular flexibility.

        Queries on materialized views are exceptionally fast since results are stored in dedicated tables. ClickHouse Cloud includes the [query cache](/concepts/features/performance/caches/query-cache), [sparse indexes](/concepts/features/performance/skip-indexes/skipping-indexes), and [projections](/concepts/features/projections/projections) at no extra tier.
      </Accordion>

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        ClickHouse powers user-facing analytics across financial services, gaming, e-commerce, and more. It supports a REST interface so web developers can build lightweight applications without complex binary protocols.

        Native connectors exist for BI tools like [Grafana](/integrations/connectors/data-visualization/grafana), [Tableau](/integrations/connectors/data-visualization/tableau/tableau-and-clickhouse), and [Looker](/integrations/connectors/data-visualization/looker-and-clickhouse), plus language clients, SQL clients, and the [MySQL wire protocol](/concepts/features/interfaces/mysql) for tools without a native connector.
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