These are the main options for a clickhouse integration datastudio setup:

1. Google Data Studio → ClickHouse® via Community Connector (Apps Script)
2. Google Data Studio → Tinybird via REST API connector
3. Google Data Studio → ClickHouse® via JDBC bridge proxy

Google Data Studio (now rebranded as Looker Studio) is Google's free BI and real-time data visualization platform, widely used by marketing, product, and analytics teams. ClickHouse® is a columnar OLAP database that handles billions of rows in sub-second queries. Connecting the two enables shareable, interactive dashboards on analytical data without exporting to BigQuery or a data warehouse first.

A clickhouse integration datastudio pipeline lets analysts build reports in Google Data Studio against live ClickHouse® data, share reports publicly or within a Google Workspace org, and embed dashboards in documents or websites.

Before you pick a connector, consider these questions:

• Do you need live data (query runs on each report load) or cached data (refreshed on a schedule)?
• Does your ClickHouse® instance need to be publicly accessible, or do you require a private network path?
• Do you also need to expose the same data as REST APIs for applications beyond Google Data Studio?

Three ways to implement clickhouse integration datastudio

This section covers the three main connector paths, with configuration and code for each.

Option 1: Google Data Studio → ClickHouse® — Community Connector

Google Data Studio supports Community Connectors built with Google Apps Script. The community-maintained ClickHouse® Data Studio connector queries ClickHouse® via the HTTP interface, returns results as Data Studio dimension/metric fields, and enables live report refreshes.

How it works: in Google Data Studio, click Add data → Build your own connector, or use the published ClickHouse® community connector. Provide your ClickHouse® HTTP endpoint, credentials, and the SQL query or table name.

Community Connector configuration (Apps Script config object):

// Google Apps Script: ClickHouse® Data Studio connector config
function getConfig(request) {
  var config = cc.newConfig();
  config.newTextInput()
    .setId('host')
    .setName('ClickHouse® Host')
    .setHelpText('e.g. https://your-clickhouse-host:8443')
    .setPlaceholder('https://your-clickhouse-host:8443');
  config.newTextInput()
    .setId('database')
    .setName('Database')
    .setPlaceholder('default');
  config.newTextInput()
    .setId('username')
    .setName('Username')
    .setPlaceholder('default');
  config.newTextInput()
    .setId('password')
    .setName('Password (stored encrypted)');
  config.newTextInput()
    .setId('query')
    .setName('SQL Query')
    .setHelpText('SELECT query returning dimensions and metrics');
  return config.build();
}

The connector sends the SQL query to ClickHouse® via UrlFetchApp and maps results to Data Studio fields. Example SQL query for a Data Studio report:

SELECT
  toDate(event_time) AS event_date,
  event_type,
  country,
  count() AS total_events,
  uniq(user_id) AS unique_users
FROM events
WHERE event_time >= today() - INTERVAL 30 DAY
GROUP BY event_date, event_type, country
ORDER BY event_date DESC

When this fits:

• You want a direct Data Studio → ClickHouse® connection without intermediate infrastructure
• Your ClickHouse® instance is publicly accessible (or accessible from Google's Apps Script servers)
• Your team is comfortable managing a community-maintained connector

Trade-offs: Community Connectors run on Google Apps Script infrastructure with execution time limits (6 minutes per run). Large result sets or slow queries can time out. ClickHouse® must be reachable from Google's servers — not suitable for private-network ClickHouse® instances without a public endpoint or a reverse proxy.

Prerequisites: ClickHouse® HTTP endpoint accessible from Google Apps Script, Google Data Studio account, community connector deployed or self-hosted in Apps Script.

Option 2: Google Data Studio → Tinybird — REST API connector

Tinybird sits between your data and Google Data Studio. You define SQL Pipes in Tinybird that query ClickHouse®-backed data sources, publish them as REST API endpoints, and consume those endpoints from Data Studio via a community REST API connector or the Data Studio → Google Sheets → Tinybird pattern.

How it works: Tinybird Pipes return JSON over HTTPS. Use a Data Studio Community Connector configured to call the Tinybird endpoint, or export Tinybird data to Google Sheets on a schedule and connect Data Studio to the Sheet.

Google Apps Script: fetch Tinybird endpoint into a Google Sheet (scheduled):

function refreshTinybirdData() {
  var url = "https://api.tinybird.co/v0/pipes/datastudio_events.json?limit=50000";
  var options = {
    headers: { "Authorization": "Bearer YOUR_TINYBIRD_TOKEN" },
    method: "GET",
    muteHttpExceptions: true
  };
  var response = UrlFetchApp.fetch(url, options);
  var data = JSON.parse(response.getContentText()).data;

  var sheet = SpreadsheetApp.openById("YOUR_SHEET_ID").getSheetByName("events");
  sheet.clearContents();
  if (data.length > 0) {
    var headers = Object.keys(data[0]);
    sheet.appendRow(headers);
    data.forEach(function(row) {
      sheet.appendRow(headers.map(function(h) { return row[h]; }));
    });
  }
}

Connect Data Studio to the Google Sheet as a data source. Trigger the Apps Script on a schedule (e.g., every 15 minutes) for near-real-time freshness. This gives you real-time analytics without exposing ClickHouse® to Google infrastructure.

When this fits:

• You already use Tinybird for analytics and want to reuse the same Pipes in Data Studio
• You want a managed analytics API layer — data is ingested into Tinybird and served via REST endpoints to Data Studio
• You need the same data in Data Studio and in application APIs from a single source

Trade-offs: the Google Sheets intermediate step adds latency (refresh cadence). For true live queries, use a Data Studio community connector that calls the Tinybird endpoint directly. The Sheets approach works well for scheduled-refresh dashboards.

Prerequisites: Tinybird account with published Pipes, Google Workspace account, Google Sheets and Apps Script access.

Option 3: Google Data Studio → ClickHouse® — JDBC bridge proxy

For teams with private ClickHouse® infrastructure, a JDBC-to-REST bridge (such as a lightweight proxy or a Cloud Run service) translates Data Studio's HTTP connector calls into ClickHouse® JDBC queries. This keeps ClickHouse® off the public internet while enabling Data Studio access.

How it works: deploy a small HTTP proxy service (Node.js, Python, or a prebuilt JDBC bridge) in your cloud infrastructure. The proxy accepts Data Studio connector requests, forwards them to ClickHouse® via JDBC or HTTP interface, and returns JSON results. Data Studio's community connector calls the proxy URL.

Conceptual proxy pattern (illustrative only — not production-ready):

# Illustrative only. Production requires: named-query allowlisting,
# timeouts, rate limiting, and proper secrets management.
from fastapi import FastAPI, Header, HTTPException
import clickhouse_connect

ALLOWED_QUERIES = {
    "daily_events": "SELECT toDate(event_time) AS date, count() AS total FROM events WHERE event_time >= today() - INTERVAL 30 DAY GROUP BY date ORDER BY date"
}
app = FastAPI()
client = clickhouse_connect.get_client(host="private-ch-host", port=8443, secure=True,
                                        username="datastudio_reader", password="secret")

@app.get("/query/{query_name}")
def run_named_query(query_name: str, authorization: str = Header(None)):
    if authorization != "Bearer YOUR_PROXY_SECRET":
        raise HTTPException(status_code=401)
    if query_name not in ALLOWED_QUERIES:
        raise HTTPException(status_code=400, detail="Unknown query")
    return {"data": client.query(ALLOWED_QUERIES[query_name]).named_results()}

Never accept raw SQL from URL parameters. A production bridge needs query allowlisting, connection pooling, request timeouts, and secrets management.

When this fits:

• Your ClickHouse® instance is on a private network and cannot be exposed publicly
• You need fine-grained access control on which queries Data Studio can run
• Your team can deploy and maintain a lightweight proxy service

Trade-offs: requires deploying and operating an additional service. Adds a network hop and potential latency. The proxy becomes a new failure surface — monitor it alongside ClickHouse®.

Prerequisites: ClickHouse® instance (self-managed or Cloud on private network), cloud infrastructure to run the proxy (Cloud Run, AWS Lambda, or a VM), Google Data Studio account.

Summary: picking the right option

Decision framework: what to choose for clickhouse integration datastudio

Pick based on your network access, freshness requirements, and data usage:

• Community Connector if your ClickHouse® is publicly accessible and you need a simple direct connection with live query support.
• Tinybird API if your ClickHouse® is on a private network, you need the same data in Data Studio and in APIs, or you want to avoid Google Apps Script execution limits. Best when you serve user-facing analytics and Data Studio from the same Tinybird Pipe.
• JDBC bridge if your ClickHouse® is private and you need direct live queries rather than the Sheets/API intermediate step.

Bottom line: for simple dashboards on a public ClickHouse® instance, Option 1 is the fastest path. For private networks or multi-consumer analytics, Option 2 (Tinybird) gives you both Data Studio and API access without complex infrastructure.

What does clickhouse integration datastudio mean (and when should you care)?

A clickhouse integration datastudio setup connects Google Data Studio's report layer to ClickHouse®'s analytical query engine. Data Studio sends requests to a connector — Apps Script, REST API, or JDBC bridge — which queries ClickHouse® and returns dimension/metric data for charts, scorecards, and pivot tables.

You should care when your data outgrows what Google Analytics, BigQuery, or a spreadsheet can query interactively. ClickHouse® handles large volumes of event and business data with fast aggregations. A live connector reflects new data on each report load.

If your workload is primarily GA4 or Google Ads data, Data Studio's native connectors are simpler. ClickHouse® is the right fit when you need custom event data, product analytics, or large-volume metrics that live outside the Google ecosystem.

Schema and pipeline design

Practical schema rules for Data Studio queries

Data Studio maps result set columns to dimensions (strings, dates) and metrics (numbers). Designing your ClickHouse® schema to produce clean dimension/metric results avoids field-mapping issues.

Rule 1: use explicit date columns. Data Studio's date range filter works best with a Date or DateTime column returned explicitly in the SELECT. Avoid ambiguous timestamp formats.

Rule 2: use LowCardinality(String) for dimension columns. Data Studio frequently groups by categorical dimensions. LowCardinality keeps GROUP BY fast for bounded-cardinality columns.

Rule 3: pre-aggregate with materialized views. Data Studio queries run on every report load. A materialized view at the right granularity reduces ClickHouse® load and improves report refresh speed.

Rule 4: return clean column names. Data Studio uses column names as field labels. Use AS aliases in the SELECT to produce human-readable names (event_date, total_events, unique_users).

Example: analytics-friendly schema

CREATE TABLE events (
  event_id UInt64,
  user_id UInt64,
  event_type LowCardinality(String),
  event_time DateTime,
  country LowCardinality(String),
  campaign LowCardinality(String),
  revenue Float64,
  updated_at DateTime
)
ENGINE = ReplacingMergeTree(updated_at)
PARTITION BY toYYYYMM(event_time)
ORDER BY (user_id, event_id)

A pre-aggregation materialized view for Data Studio reports:

CREATE MATERIALIZED VIEW events_daily_mv
ENGINE = AggregatingMergeTree()
PARTITION BY toYYYYMM(event_date)
ORDER BY (event_type, country, campaign, event_date)
AS SELECT
  toDate(event_time) AS event_date,
  event_type,
  country,
  campaign,
  count() AS total_events,
  sumState(revenue) AS total_revenue_state,
  uniqState(user_id) AS unique_users_state
FROM events
GROUP BY event_date, event_type, country, campaign

Query with: SELECT event_date, event_type, sumMerge(total_revenue_state) AS revenue, uniqMerge(unique_users_state) AS unique_users FROM events_daily_mv GROUP BY event_date, event_type.

Point Data Studio connectors at events_daily_mv for daily-granularity dashboards.

Failure modes

1. Apps Script execution timeout. Community Connectors have a 6-minute execution limit. Large result sets or slow ClickHouse® queries fail silently. Mitigation: use LIMIT in connector queries and pre-aggregate in materialized views.

2. ClickHouse® not publicly reachable from Google. Apps Script runs on Google servers. If ClickHouse® is behind a VPC, the connector cannot reach it. Mitigation: use the Tinybird API path (Option 2) or the JDBC bridge proxy (Option 3).

3. Schema mismatch breaking Data Studio field mapping. Renaming connector query columns breaks existing reports. Mitigation: treat connector SQL as a versioned contract; add columns additively and test in a new report before updating shared ones.

4. Apps Script refresh failure (Option 2). If the scheduled Apps Script refresh fails silently, Data Studio shows stale data. Mitigation: add error handling in the script and send alert emails on failure.

5. Data Studio data freshness gap. Data Studio caches data for up to 12 hours by default. Mitigation: configure data freshness to 15 minutes in report settings and communicate the refresh cadence to report consumers.

Why ClickHouse® for Data Studio analytics

ClickHouse® is a columnar OLAP database built for the analytical query patterns Data Studio reports generate. Vectorized execution and columnar compression deliver fast aggregations on large datasets — reports load quickly even on months of high-frequency events.

ClickHouse®'s MergeTree engine supports time-partitioned data, pre-aggregation, and deduplication — patterns that translate directly to Data Studio's dimension/metric data model. For teams that need both shareable dashboards and internal product analytics from the same source, ClickHouse® is among the fastest database for analytics backends available.

Security and operational monitoring

• Authentication: dedicated read-only ClickHouse® user with GRANT SELECT. Never embed admin credentials in a connector or Apps Script.
• TLS: enforce HTTPS on all connections. Port 8443 for Cloud; https_port for self-managed.
• Proxy access control: named-query allowlisting in the JDBC bridge — never pass raw SQL from the connector URL parameter.
• Token hygiene: scoped Tinybird tokens with READ-only access per Pipe; rotate on schedule.
• Connector sharing: Data Studio connectors shared broadly grant all viewers query access. Restrict sharing and use row-level controls in the query or Pipe logic.

Latency, caching, and freshness considerations

Community Connectors run a query on each Data Studio report load — latency is Apps Script execution plus ClickHouse® query time. For pre-aggregated queries, expect a few seconds total. Raw event queries can be slower; pre-aggregate in a materialized view.

Data Studio caches report data for up to 12 hours by default. Configure data freshness per report (minimum 15 minutes in Data Studio Free) to reduce staleness.

Tinybird Pipe caching provides configurable TTL freshness. Data Studio loads within the cache window receive fast responses without a ClickHouse® query. The Sheets intermediate path (Option 2) refreshes on the Apps Script trigger schedule, independently of Data Studio's own cache.

Why Tinybird is a strong fit for clickhouse integration datastudio

Most teams using Google Data Studio eventually need the same data in multiple places — a product feature, an embedded chart, or a customer-facing report. Managing separate pipelines means duplicated query logic and more infrastructure.

Tinybird solves this by combining a ClickHouse®-powered analytics platform, SQL-based Pipes, and instant REST API publishing. Ingest once, define a Pipe, and serve Data Studio (via Google Sheets or a REST connector) and your product (via HTTP) from the same query. This is the real-time data ingestion pattern for teams that outgrow single-consumer BI setups.

Next step: identify your most-used Data Studio report, model its key query as a Tinybird Pipe, and validate freshness in staging before switching production.

Frequently Asked Questions (FAQs)

How do I connect ClickHouse® to Google Data Studio?

Use one of three paths: a Community Connector (Apps Script) for direct queries to a public ClickHouse® endpoint; Tinybird REST API via a scheduled Google Sheets refresh; or a JDBC bridge proxy for private ClickHouse® instances. The Community Connector is the simplest for publicly accessible ClickHouse®.

Does clickhouse integration datastudio support live data?

The Community Connector runs a query on each Data Studio report load — effectively live, within Apps Script execution limits. The Tinybird API path via Google Sheets refreshes on a schedule (minimum ~15 minutes). Data Studio itself caches data for up to 12 hours by default; configure data freshness in report settings to reduce the cache TTL.

Can I connect Google Data Studio to a private ClickHouse® instance?

Use the Tinybird API path (Option 2) — ingest your data into Tinybird and expose it via REST endpoints that Data Studio can reach publicly. Note that this means data lives in Tinybird's platform, not that Tinybird proxies your existing ClickHouse® cluster directly. Alternatively, deploy a JDBC bridge proxy (Option 3) that sits between Google's infrastructure and your private ClickHouse® instance.

What SQL query format does the ClickHouse® Data Studio connector use?

Use standard ClickHouse® SQL — SELECT with explicit column aliases for dimension and metric names. Return a Date or DateTime column for Data Studio's date range filter to work correctly. Avoid SELECT * — specify only the columns Data Studio needs. Pre-aggregate in materialized views for better performance.

Does clickhouse integration datastudio work with Google Workspace permissions?

Yes. Data Studio reports can be shared within Google Workspace. Community Connectors are shared separately — authorize the connector for all users who need it. The Tinybird API path (via Google Sheets) inherits Google Sheets sharing permissions.

What are the main limitations of clickhouse integration datastudio?

Apps Script execution limits (6 minutes) restrict Community Connector query size. ClickHouse® must be publicly accessible for the Community Connector. The 12-hour default cache shows stale data without configuration. The Sheets path adds refresh lag. Data Studio's dimension/metric model requires clean, aggregated result sets — raw nested ClickHouse® types are not supported.