Operational lakehouse

Bring product, business, and operational data into one governed lakehouse, then serve analysts, dashboards, applications, and agents from the same live context.

How teams adopt the operational lakehouse

Start with storage and catalogs, query live data, then expose the same governed lakehouse to applications, BI, and agents — without bolting another warehouse onto the stack.

Diagram: managed Altertable catalogs and external catalogs unified as one lakehouse

Bring every source into one live workspace

Keep the systems that already run your business in place, then make them queryable as one governed lakehouse. Product data, warehouse tables, operational replicas, and open files can finally answer the same question together.

Start with the data you already have
Use built-in storage, bring your own object storage, or query existing databases and warehouses without starting a copy-and-sync project first.
Give every source a stable home
Register files, tables, databases, and warehouses as named catalogs so teams stop hunting through disconnected systems before every analysis.
Leave ownership where it belongs
Production systems stay authoritative. Altertable becomes the shared analytical layer that reaches them when teams, apps, or agents need live context.

What platform teams get

Federation without copy-out, predictable pricing, governance applied once, and enough interfaces that you do not have to rewrite the rest of the stack.

Federated by default

Connect databases, warehouses, Iceberg, and Bucket Tables as catalogs so everything is queryable from one SQL workspace.

Predictable pricing

Flat monthly pricing — unmetered queries, unlimited seats, no per-GB scan charges. Run benchmarks and agent traffic without surprise invoices. See pricing.

Controls every surface inherits

Set permissions, credential access, residency, and auditability once at the lakehouse layer, then let BI, applications, and agents inherit the same guardrails.

No migration cliff

Keep the tools and workflows your team already trusts while you move more workloads onto one shared lakehouse, one surface at a time.

Core lakehouse capabilities

Federation, open table formats, client interfaces, and discovery operators in one governed SQL layer.

Federated SQL across catalogs

Join managed Altertable catalogs with sources like Postgres, MySQL, ClickHouse, BigQuery, Snowflake, Redshift, Supabase, or any S3-compatible bucket and Iceberg tables in one SQL workspace.

Your data stays portable

Build on open lakehouse formats instead of trapping analytics in another proprietary warehouse. Altertable can work with the data you already store, without forcing a lock-in migration first.

Interfaces for every caller

Connect through HTTP, Arrow Flight SQL, the Postgres-compatible adapter, or MCP so applications, BI tools, and agents use the same lakehouse without rewrites.

Discovery operators in SQL

Full-text search with relevance scoring, regex predicates, and vector similarity (beta) all run as SQL — combine them with aggregations and federated joins in one query.

Your language, your stack

SDKs and adapters in the languages your team already uses,
so you ship without changing how you work.

HTTP API

from altertable_lakehouse import Client
from altertable_lakehouse.models import QueryRequest

client = Client(
    username="your_lakehouse_username",
    password="your_lakehouse_password",
)

# Run a SQL query
req = QueryRequest(statement="""
    SELECT
        date_trunc('day', timestamp) AS day,
        COUNT(DISTINCT user_id)      AS dau
    FROM   events
    WHERE  event = 'Event Name'
    AND    timestamp >= NOW() - INTERVAL '30 days'
    GROUP  BY 1
    ORDER  BY 1 DESC
""")

# Accumulate all rows in memory
result = client.query_all(req)
for row in result.rows:
    print(row)

# Append data to a table
client.append(
    catalog="my_catalog",
    schema="my_schema",
    table="users",
    data={"user_id": 1, "plan": "pro"}
)

Explore more use cases

Other workloads that share the same engine and governance.

Product Analytics

Capture product data through Altertable's APIs, then analyze funnels, segmentation, and behavior in the same system as revenue, operations, and warehouse data.

Self-Serve BI

Build dashboards and visualizations on the same live engine used for SQL, then expose trusted metrics and insights to AI agents and collaboration tools through MCP.