The real-time
data warehouse

Optimized to power data-intensive applications that run on real-time and historical data. With blazing speed and high concurrency.
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What is a real-time data warehouse?

Analytics push traditional data warehouses, lakes, and transactional databases to their limits – not just in terms of performance, but also with skyrocketing costs. A real-time data warehouse is purpose-built for fast, reliable, and cost-effective querying at any scale.
Evolution of data warehouses
30 years ago

Traditional on-prem data warehouse

30 years ago, on-prem data warehouses like IBM, Hadoop, Oracle, and Teradata were the only options available.
  • Data volumnes were small
  • Warehouses were operationally complex
10 years ago

Traditional cloud warehouse

Traditional cloud data warehouses, whose predecessors were built to manage much smaller volumes, began to strain under the increased data load.
  • Performance and concurrency limitations became limiting at scale
  • Retrofitting these for analytics or real-time workloads started to become prohibitively costly
SnowflakeGoogle BigQueryAmazon Redshift

Real-time data warehouse

Built for the next generation of data-intensive workloads.
  • Simplified and cost effective
  • Unified resource for querying streaming and historical data
Offline data warehouses
DatabricksSnowflakeAmazon Athena

Why use a real-time data warehouse?

Companies leverage ClickHouse Cloud as their real-time data warehouse to ensure that analytics shine at any scale.

Traditional cloud data warehouse

  • Performance
    High query latency and concurrency limitations are commonplace
  • Hardware efficiency
    Can create data bloat and inefficient usage of system resources
  • Scale
    Analytics queries scale inadequately as data volumes increase
  • Complexity
    Can lead to growing operational complexity
  • Cost
    Costly for many workloads

Real-time data warehouse

  • Performance
    Engineered to handle highly concurrent workloads that back user-facing applications
  • Hardware efficiency
    Optimized to manage petabytes of data, with best-in-class compression ratios for the most efficient storage usage
  • Scale
    Delivers unparalleled performance for analytical workloads at scale
  • Complexity
    Simplified developer experience that's easy to manage and scale
  • Cost
    Maximizes cost-effectiveness

Impact of the real-time data warehouse

With Snowflake, we were using the standard plan, small compute, which cost nearly six times more than ClickHouse Cloud. We got several seconds query time and no materialized views.

With ClickHouse Cloud's production instance, we are getting sub-second query time along with materialized views. The decision to switch was a no-brainer for us.

AdgreetzAlternative to Snowflake

We were on Redshift for about a year and a half, but found the operational overhead and performance wasn't getting it done. Moving over to ClickHouse we were basically able to cut that (Redshift) bill in half. That 30 second query now takes under a second, and every page loads just faster.

VantageAlternative to Redshift

It [BigQuery] discourages data usage. Instead of encouraging analysts to query the database in any and all ways they can imagine you’ll end up worrying about needing to limit them and come up with processes for controlling the volume of data being used. We simply don’t want the hassle of trying to figure out in advance of how many BQ slots to purchase - what a headache!

HIFIAlternative to BigQuery

Relied on across industries

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Financial Services

Trading and market analytics, fraud detection, risk monitoring, blockchain, and more.
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E-Commerce and Retail

Real-time inventory monitoring and overall tracking for online businesses.
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Marketing and Sales

Data store for Adtech, web analytics, SEO, and much more.
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Including IoT, Energy, Biotech, Manufacturing, and others.
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Media and Entertainment

Assess the performance of videos, assets, and other media in real‑time.
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Understand player behavior, gaming dynamics, and other key insights used to improve overall gameplay.