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PricingDocs
Bars

Data Platform

Managed ClickHouse
Production-ready with Tinybird's DX
Streaming ingestion
High-throughput streaming ingest
Schema iteration
Safe migrations with zero downtime
Connectors
Plug and play Kafka, S3, and GCS

Developer Experience

Instant SQL APIs
Turn SQL into an endpoint
BI & Tool Connections
Connect your BI tools and ORMs
Tinybird Code
Ingest and query from your terminal

Enterprise

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High availability
Fault-tolerance and auto failovers
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Product []

Data Platform

Managed ClickHouse
Production-ready with Tinybird's DX
Streaming ingestion
High-throughput streaming ingest
Schema iteration
Safe migrations with zero downtime
Connectors
Plug and play Kafka, S3, and GCS

Developer Experience

Instant SQL APIs
Turn SQL into an endpoint
BI & Tool Connections
Connect your BI tools and ORMs
Tinybird Code
Ingest and query from your terminal

Enterprise

Tinybird AI
AI resources for LLMs and agents
High availability
Fault-tolerance and auto failovers
Security and compliance
Certified SOC 2 Type II for enterprise
PricingDocs
Resources []

Learn

Blog
Musings on transformations, tables and everything in between
Customer Stories
We help software teams ship features with massive data sets
Videos
Learn how to use Tinybird with our videos
ClickHouse for Developers
Understand ClickHouse with our video series

Build

Templates
Explore our collection of templates
Tinybird Builds
We build stuff live with Tinybird and our partners
Changelog
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Community

Slack Community
Join our Slack community to get help and share your ideas
Open Source Program
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Back to columns

Scalable Analytics Architecture

Scalable Analytics Architecture
Oct 21, 2025
Flink's 95% problem
Flink might sound like the holy grail of streaming data processing, but for 95% of us, it's just a complex headache we don't need.
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How we protect your primary workloads with Compute-Compute Separation for Populates
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Optimizing Apache Iceberg tables for real-time analytics
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Real-Time Analytics on Apache Iceberg with Tinybird
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dbt in real-time
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How to count 100B events: Comparing architectures
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The simplest way to count 100B unique IDs: Part 2
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The perfect data ingestion API design
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Application Architecture: Combining DynamoDB and Tinybird
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Alasdair BrownAlasdair BrownDeveloper Advocate
1Application Architecture: Combining DynamoDB and Tinybird

Skip the infra work. Deploy your first ClickHouse
project now

Get started for freeRead the docs
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Product /

ProductWatch the demoPricingSecurityRequest a demo

Company /

About UsPartnersShopCareers

Features /

Managed ClickHouseStreaming IngestionSchema IterationConnectorsInstant SQL APIsBI & Tool ConnectionsTinybird CodeTinybird AIHigh AvailabilitySecurity & Compliance

Support /

DocsSupportTroubleshootingCommunityChangelog

Resources /

ObservabilityBlogCustomer StoriesTemplatesTinybird BuildsTinybird for StartupsRSS FeedNewsletter

Integrations /

Apache KafkaConfluent CloudRedpandaGoogle BigQuerySnowflakePostgres Table FunctionAmazon DynamoDBAmazon S3

Use Cases /

User-facing dashboardsReal-time Change Data Capture (CDC)Gaming analyticsWeb analyticsReal-time personalizationUser-generated content (UGC) analyticsContent recommendation systemsVector search
All systems operational

Copyright © 2025 Tinybird. All rights reserved

|

Terms & conditionsCookiesTrust CenterCompliance Helpline