Data Analytics & BI

Sub-second exploration across billion-row tables. Agents surface anomalies before you ask. One unified model — product events, revenue, and ops in the same query.

Altertable turns reactive BI into continuous intelligence. Merge product events and business data, explore in sub-seconds, and let agents surface what matters — capturing context along the way so tribal knowledge stops living in Slack threads.

Capabilities

Core features that make BI fast, intelligent, and proactive

Sub Second Exploration

Columnar engine with vectorized execution and adaptive caching delivers sub-second drill-downs across large datasets and high concurrency.

Merge Product and Business Data

Federate product events with revenue, billing, and ops from your databases. One model, one place to answer product-to-profit questions. Connect your data.

Additional Capabilities

More features that make Altertable a complete BI replacement

Drop In BI Alternative

Adopt incrementally and replace legacy dashboards with an AI-native analytics layer. View integrations.

Always On Agents

Agents continuously surface anomalies, correlation shifts, and emerging trends. Insights arrive proactively—no dashboard babysitting. Learn about agents.

Interactive Insights

Every chart is live and conversational. Ask follow-ups, pivot segments, and slice cohorts inline—no context switches or new tabs.

SQL Native AI Assisted

SQL-first for builders, AI-assisted for everyone. Generate queries, validate joins, and add guardrails to keep analyses correct. .

Every language. Every stack.

SDKs and adapters for all major languages, so your team ships without friction.

Lakehouse API

Query and write data directly to your Altertable lakehouse.

from altertable_lakehouse import Client
from altertable_lakehouse.models import QueryRequest

client = Client(
    username="your_username",
    password="your_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"}
)