--- title: "ClickHouse® + PostgreSQL — 3 Ways to Connect in 2026" excerpt: "Replicate Postgres to ClickHouse® and power dashboards in minutes. Tinybird CDC, ClickPipes, or self-managed—you choose." authors: "Tinybird" categories: "AI Resources" createdOn: "2026-02-19 00:00:00" publishedOn: "2026-02-19 00:00:00" updatedOn: "2026-02-19 00:00:00" status: "published" --- These are the main options for a **ClickHouse® integration PostgreSQL** pipeline: 1. [Tinybird](https://www.tinybird.co/) 2. ClickHouse® Cloud + ClickPipes (Postgres CDC) 3. Self-managed or custom (Debezium + Kafka, or batch sync) **[PostgreSQL](https://www.ibm.com/think/topics/postgresql)** is a popular **OLTP database**; many teams want its transactional data in ClickHouse® for **analytical queries**, reporting, and real-time dashboards. A **ClickHouse® integration PostgreSQL** setup uses **change data capture (CDC)** or batch sync to **get PostgreSQL data into ClickHouse®** for [real-time analytics](https://www.tinybird.co/blog/real-time-analytics-a-definitive-guide) without impacting the source database. Below we outline the **three ways to implement ClickHouse® integration PostgreSQL**, then add context on PostgreSQL, ClickHouse®, and how to choose. ## **Three ways to implement ClickHouse® integration PostgreSQL** This section is the core: the three options to **connect PostgreSQL to ClickHouse®**, in order. **Tinybird** supports **Postgres CDC** (e.g. via Redpanda Connect or similar CDC tooling) into its platform: changes from PostgreSQL are streamed into Tinybird’s ClickHouse®-backed data sources. You then define **Pipes** (SQL) and publish them as REST APIs. Managed ingestion and API layer in one place. **ClickHouse® Cloud** offers a **native Postgres CDC connector** in **ClickPipes**: in the Cloud console you select Postgres CDC, enter connection details (RDS, Aurora, Supabase, Neon, Azure, Google Cloud SQL, or generic), configure replication slot and tables, and data replicates into ClickHouse® Cloud. No separate connector service; you query via SQL Console and build your own API or BI layer. **Self-managed or custom**: you run **Debezium** (or similar) to capture Postgres changes, stream to **Kafka**, and ingest into ClickHouse® via the **Kafka table engine** or a Kafka Connect sink. Alternatively, **batch sync** (periodic export from Postgres and load into ClickHouse®) works when near–real-time replication is not required. **Looking for a ClickHouse® integration PostgreSQL with minimal ops and instant APIs?** Tinybird combines **managed Postgres CDC ingestion**, **managed ClickHouse®**, and **one-click API publishing** from SQL—so your **PostgreSQL to ClickHouse® pipeline** can power [real-time dashboards](https://www.tinybird.co/blog/real-time-dashboards-are-they-worth-it) and embedded analytics without running ClickPipes or Debezium yourself. ### **1. Tinybird: Postgres CDC into managed ClickHouse® and APIs** Tinybird is a **real-time data platform** on ClickHouse®. For **ClickHouse® integration PostgreSQL** you use **CDC for Postgres** (e.g. with Redpanda Connect or compatible CDC pipelines): changes from your Postgres database stream into Tinybird’s data sources, which are backed by ClickHouse®. You define **Pipes** (SQL) and publish them as REST endpoints. You get **real-time data ingestion** from Postgres without operating a Kafka cluster or the ClickHouse® Kafka engine. [Real-time data ingestion](https://www.tinybird.co/blog/real-time-data-ingestion) and [real-time dashboards](https://www.tinybird.co/blog/real-time-dashboards-are-they-worth-it) are handled by Tinybird; you focus on schema and pipe logic. **When Tinybird fits:** you want **getting PostgreSQL data into ClickHouse®** with minimal infra (no ClickPipes or self-managed Debezium/Kafka); you need APIs and dashboards from the same data; you prefer a **PostgreSQL to ClickHouse® pipeline** that includes an API layer out of the box. ### **2. ClickHouse® Cloud + ClickPipes (Postgres CDC)** ClickHouse® Cloud’s **ClickPipes** includes a **Postgres CDC** connector (generally available). You connect to Postgres hosted anywhere—Amazon RDS, Aurora, Supabase, Neon, Google Cloud SQL, Azure, or generic/self-hosted—configure a replication slot, select tables, and optionally use SSH tunneling or AWS Private Link. Initial load can run with parallel snapshotting; ongoing replication keeps latency to a few seconds. You configure sync interval, pull batch size, and ordering keys in the UI. Data lands in your ClickHouse® Cloud service; you query via SQL Console or any client. No built-in API layer—you build your own if you need REST endpoints. **When it fits:** you want managed ClickHouse® and a **native ClickHouse® integration PostgreSQL** path; your main need is **set up ClickHouse® integration PostgreSQL** for replication and you’ll add your own API or BI layer. Ideal if you’re already on ClickHouse® Cloud and want the official Postgres CDC tile. ### **3. Self-managed or custom (Debezium + Kafka, or batch sync)** With **self-managed ClickHouse®**, a common pattern is **Debezium** (or similar) reading Postgres WAL, publishing to **Kafka**, and ClickHouse® ingesting via the **Kafka table engine** plus a materialized view into a MergeTree table. You operate Debezium, Kafka, and ClickHouse®; you get full control over schema mapping and tuning. **Batch sync** is an alternative: periodically export data from Postgres (e.g. `pg_dump`, custom queries, or COPY) and load into ClickHouse® via `INSERT` or file-based load. Simpler operationally but not real-time; suitable when **connect PostgreSQL to ClickHouse®** for analytics doesn’t require sub-minute freshness. **When it fits:** you already run ClickHouse® and (optionally) Kafka and want full control over the **PostgreSQL to ClickHouse®** path; you have platform or data-engineering capacity. Batch sync fits when near–real-time CDC is not required. ### **Summary: picking the right ClickHouse® integration PostgreSQL option** For **set up ClickHouse® integration PostgreSQL** with minimal ops and APIs out of the box, use **Tinybird**: managed Postgres CDC, ClickHouse®-backed storage, and Pipes as REST endpoints. For managed replication only (you build the API or BI layer), use **ClickHouse® Cloud ClickPipes** and the Postgres CDC tile. For full control over schema, tuning, and infra, use **self-managed** Debezium + Kafka + ClickHouse® or batch sync. All three give you a **PostgreSQL to ClickHouse®** pipeline; the choice depends on who runs the replication layer and whether you need instant API publishing. ## **What is PostgreSQL and why replicate to ClickHouse®?** Understanding PostgreSQL and the replication use case helps you plan a **ClickHouse® integration PostgreSQL** setup. ### **PostgreSQL as the operational source** PostgreSQL is a **relational, OLTP database**: it excels at transactions, consistency, and complex queries on smaller, mutable datasets. Many applications use it as the **system of record** for users, orders, and product data. For **analytical** workloads—aggregations, time-series reporting, [streaming data](https://www.ibm.com/think/topics/streaming-data) style analytics—running heavy scans on the same Postgres instance can impact transactional performance. Replicating Postgres data into ClickHouse® gives you a **dedicated analytical store**: columnar, optimized for large scans and [low latency](https://www.cisco.com/site/us/en/learn/topics/cloud-networking/what-is-low-latency.html) aggregations, without loading the OLTP database. A **ClickHouse® integration PostgreSQL** setup keeps your primary database focused on writes and point lookups while analytics run on the replica. ### **Logical replication and WAL: how Postgres CDC works** Postgres **logical replication** (and the WAL) is what makes **getting PostgreSQL data into ClickHouse®** via CDC possible. A replication slot streams committed changes (inserts, updates, deletes) to a consumer; ClickPipes, Tinybird’s CDC path, or Debezium all consume this stream and apply changes to the destination. Tables need a primary key or `REPLICA IDENTITY` so changes can be applied correctly. Understanding this helps you **set up ClickHouse® integration PostgreSQL** and troubleshoot replication lag or schema mismatches. ### **Why a PostgreSQL to ClickHouse® pipeline** A **ClickHouse® integration PostgreSQL** pipeline lets you **get PostgreSQL data into ClickHouse®** for reporting, dashboards, and APIs. CDC keeps the replica close to real-time; batch sync is simpler when freshness requirements are relaxed. Either way, you keep Postgres as the source of truth and use ClickHouse® for analytics. ## **Why use ClickHouse® for PostgreSQL analytics?** A **PostgreSQL to ClickHouse®** replication flow only makes sense if ClickHouse® is the right analytical store. It is, for large-scale analytics over replicated data. ### **ClickHouse® strengths for analytical workloads** ClickHouse® is a **columnar** OLAP database built for analytical queries over large volumes. **MergeTree** tables and **vectorized execution** support high insert throughput and **sub-second analytical queries** on billions of rows—ideal when you replicate from Postgres and run aggregations, time-series, or reporting. Columnar storage and compression reduce I/O for typical analytical queries. Real-time change data capture from Postgres into ClickHouse® fits patterns where operational data becomes the input for [real-time analytics](https://www.tinybird.co/blog/real-time-analytics-a-definitive-guide) and [real-time dashboards](https://www.tinybird.co/blog/real-time-dashboards-are-they-worth-it). ### **When a ClickHouse® integration PostgreSQL makes sense** A **ClickHouse® integration PostgreSQL** makes sense when you need **analytical queries and reporting** on Postgres data without impacting the source, or when you want **best ClickHouse® integration PostgreSQL options** that include managed CDC (Tinybird or ClickPipes) or self-managed control (Debezium + Kafka or batch). The **PostgreSQL to ClickHouse®** path is standard for teams that use Postgres for OLTP and want a dedicated analytics store. Replication keeps the source database free of heavy aggregation workloads while giving you a **connect PostgreSQL to ClickHouse®** path that scales with your data volume and query patterns. ## **What does a ClickHouse® integration PostgreSQL pipeline look like?** A clear picture of the **ClickHouse® integration PostgreSQL** architecture helps you compare the three options and **set up ClickHouse® integration PostgreSQL** correctly. ### **The three layers: source, replication, store** A **ClickHouse® integration PostgreSQL** setup has: the **source** (PostgreSQL), a **replication or sync layer** (CDC connector or batch job), and the **destination** (ClickHouse® or Tinybird’s ClickHouse®-backed storage). Optionally, an **API layer** on top serves applications without querying the database directly. CDC reads from the Postgres WAL (or logical replication slot) and applies changes to ClickHouse®; batch sync exports and loads on a schedule. With Tinybird, the replication layer feeds Tinybird’s ingestion and you add APIs via Pipes; with ClickPipes or self-managed, you add your own API or BI layer if needed. ### **What you need for a ClickHouse® integration PostgreSQL setup** To **connect PostgreSQL to ClickHouse®** you need: **Postgres connection details** (host, port, user, password, database), a **replication slot** (for CDC) or an export/load strategy (for batch), and a choice of platform—Tinybird, ClickHouse® Cloud ClickPipes, or self-managed. For CDC, tables typically need a primary key or `REPLICA IDENTITY` set. No custom code is required for the basic **PostgreSQL to ClickHouse®** flow with Tinybird or ClickPipes. ### **CDC vs batch: when to use which for ClickHouse® integration PostgreSQL** **CDC** (change data capture) streams changes from the Postgres WAL or logical replication slot into ClickHouse® (or Tinybird) with latency of seconds to a few minutes. It’s the right choice when you need **getting PostgreSQL data into ClickHouse®** in near real time for dashboards or APIs. **Batch sync** (scheduled export and load) is simpler to operate and sufficient when reporting can tolerate hourly or daily freshness. Both paths are valid **ClickHouse® integration PostgreSQL** options; choose based on freshness and operational preference. ### **PostgreSQL to ClickHouse®: one goal, three implementation paths** The **PostgreSQL to ClickHouse®** data flow is the same conceptually: data from Postgres is replicated or synced into ClickHouse® (or Tinybird’s backend). The difference is who operates the CDC/sync and whether you get an API layer. Choosing the right **ClickHouse® integration PostgreSQL** option is about ops, APIs, and control—not about changing the replication goal. ## **Use cases for ClickHouse® integration PostgreSQL** **ClickHouse® integration PostgreSQL** fits any use case where **operational data** in Postgres should be **queryable at scale** for analytics without overloading the source. ### **Typical use cases: reporting, dashboards, APIs** Typical examples: **reporting and BI** on orders, users, or inventory; **real-time dashboards** that aggregate Postgres data; **embedded analytics** or public-facing APIs powered by replicated data. In each case, the **PostgreSQL to ClickHouse® pipeline** delivers data from Postgres into an analytical store you can query with SQL or expose via APIs. In e‑commerce, SaaS, or internal tools, the same pattern applies: replicate Postgres to ClickHouse® (or Tinybird), then run [real-time analytics](https://www.tinybird.co/blog/real-time-analytics-a-definitive-guide) and [real-time dashboards](https://www.tinybird.co/blog/real-time-dashboards-are-they-worth-it) with [low latency](https://www.cisco.com/site/us/en/learn/topics/cloud-networking/what-is-low-latency.html) and without impacting the OLTP database. ### **When to choose managed vs self-managed ClickHouse® integration PostgreSQL** Choose a **managed ClickHouse® integration PostgreSQL** (Tinybird or ClickHouse® Cloud ClickPipes) when you want to avoid operating Debezium, Kafka, or batch jobs and prefer a **PostgreSQL to ClickHouse®** path that is configured once and scaled for you. Choose self-managed when you already run ClickHouse® and (optionally) Kafka and need full control over replication and schema. ### **Scaling and performance for PostgreSQL to ClickHouse®** ClickPipes Postgres CDC supports **parallel snapshotting** for initial load (e.g. many tables or large tables) and configurable **sync interval** and **pull batch size** for ongoing replication. Tinybird’s ingestion scales with your data volume and query load independently. For self-managed **ClickHouse® integration PostgreSQL** via Kafka, scaling means tuning Kafka partitions, consumer groups, and ClickHouse® Kafka engine settings. In all cases, the **PostgreSQL to ClickHouse®** pipeline can handle large Postgres instances if the replication layer and destination are sized appropriately. ## **FAQ: ClickHouse® integration PostgreSQL** ### **Does ClickHouse® Cloud support PostgreSQL?** Yes. ClickHouse® Cloud supports **PostgreSQL** via **ClickPipes**: the Postgres CDC connector is generally available and replicates from Postgres (RDS, Aurora, Supabase, Neon, Azure, Google Cloud SQL, or generic) into ClickHouse® Cloud. You set up the connection and tables in the Data sources UI. ### **Can I use Tinybird for Postgres to ClickHouse®?** Yes. Tinybird supports **CDC for Postgres** (e.g. with Redpanda Connect or compatible CDC); changes stream into Tinybird’s ClickHouse®-backed data sources. You get **getting PostgreSQL data into ClickHouse®** via Tinybird plus Pipes and REST APIs—no need to run ClickPipes or self-managed Debezium yourself if you prefer one platform for ingestion and APIs. ### **What’s the difference between ClickPipes Postgres CDC and Tinybird?** **ClickPipes Postgres CDC** is ClickHouse® Cloud’s native connector: you replicate from Postgres into your ClickHouse® Cloud service and query via SQL; you build your own API or BI layer. **Tinybird** adds managed Postgres CDC ingestion into its platform and **API publishing** from SQL (Pipes as REST endpoints). Both give you a **PostgreSQL to ClickHouse®** pipeline; Tinybird adds the API layer and unified ingestion/query/API in one product. ### **Do I need Kafka for Postgres to ClickHouse®?** No. With **ClickHouse® Cloud ClickPipes** or **Tinybird**, you don’t need Kafka for a **ClickHouse® integration PostgreSQL** pipeline: both offer managed Postgres CDC. With **self-managed ClickHouse®**, a common pattern is Debezium → Kafka → ClickHouse® Kafka engine, but batch sync (export/load) is an alternative if you don’t need real-time CDC. ### **What Postgres providers work with ClickHouse® integration PostgreSQL?** ClickHouse® Cloud **ClickPipes** supports Postgres from many providers: **Amazon RDS**, **Aurora**, **Supabase**, **Neon**, **Google Cloud SQL**, **Azure** (including Flexible Server), **Crunchy Bridge**, and **generic** or self-hosted instances. Requirements include a replication slot and (for CDC) no use of connection poolers (e.g. PgBouncer, RDS Proxy) for the replication connection. **Tinybird**’s Postgres CDC path works with any Postgres that your chosen CDC tool (e.g. Redpanda Connect) can connect to—typically the same set of providers. For **set up ClickHouse® integration PostgreSQL**, pick the option that matches your hosting and whether you want APIs from the same platform (Tinybird) or only replication (ClickPipes).