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Altertable AI-Native Data Platform

website·Indexed 32 days ago

https://altertable.ai/

An AI-native data platform featuring always-on agents that model, monitor, and analyze data to surface insights and reduce costs. The platform integrates with lakehouses and utilizes DuckDB for high-performance analytics, offering specialized capabilities like Altertable Workers for running the data plane in private clouds.

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What's inside Altertable

  1. Overview of Altertable AI-native data platform

    Altertable is an AI-native data platform designed to shift data professionals from task execution to outcome ownership. It replaces static dashboards and manual pipeline management with continuous intelligence, utilizing always-on AI agents that ingest, model, and analyze data 24/7 to surface insights automatically. The platform features a persistent semantic layer to capture tribal knowledge and is built on open standards (DuckDB, Parquet, Arrow) to support both structured and unstructured data without vendor lock-in.

  2. Overview of MATCH_RECOGNIZE

    MATCH_RECOGNIZE is used to find sequences of rows that match a regular-expression-like pattern. It is ideal for analyzing ordered event streams, such as customer journeys, funnels, state transitions, and anomaly detection, which are typically difficult to express using standard self-joins or window functions.

  3. Overview of Altertable Workers

    Altertable Workers enable a split-plane architecture where altertable.ai serves as the control plane (handling catalog, authentication, connection configuration, UI, billing, audit, metadata, and orchestration) and the Workers serve as the data plane. The data plane handles source connectivity, query execution, Parquet reads/writes, object storage access, local cache, and result streaming. This allows raw data to remain within the customer's infrastructure without round-tripping through the control plane.

  4. Overview of the Altertable Data Operating System

    Altertable is a data operating system designed to replace over-engineered, reactive data stacks with a unified system. It integrates AI-native infrastructure, real-time context, and autonomous intelligence to provide continuous insights. The system is built on four pillars: native warehousing, an AI-native runtime, autonomous agents with institutional memory, and a natural language dialogue interface grounded in semantic models.

  5. Overview of Altertable data integrations

    Altertable provides several integrations to load data from pipelines and event platforms into a lakehouse. Supported integrations include:

    • Airbyte: Sync data from hundreds of sources using a custom Altertable destination connector with Docker.
    • Segment: Ingest Segment events to combine product behavior data with business and operational data.
    • PostHog: Forward events from PostHog JavaScript integrations into Altertable Product Analytics without modifying tracking calls.
    • dbt: Use the dbt-altertable adapter to transform lakehouse data using DuckDB SQL and maintain persisted documentation.

  6. Overview of Altertable data ingestion methods

    Altertable provides multiple ways to ingest records, files, and scheduled object-storage syncs into its lakehouse catalogs to make them queryable via SQL, dashboards, and AI workflows. Depending on the use case, developers can choose from the following methods:

    • HTTP API: Use append, upload, and upsert endpoints for record and file ingestion.
    • Altertable CLI: Load files, query data, and automate workflows from the terminal.
    • DuckDB: Use the community extension to ingest local files, tables, and SQL workflows.
    • Object Storage: Use bucket synchronizers for scheduled imports of files from object storage.
    • Python DataFrames: Load Pandas, Polars, and Ibis tables via the DuckDB Python client and Altertable community extension.
    • Product Analytics: Track events, identify users, and alias identities into the product_analytics catalog.
    • OpenTelemetry: Send logs, traces, and telemetry signals using OpenTelemetry exporters for SQL analysis.
    • Third-party Integrations: Connect ETL tools and event platforms to load records.

  7. Overview of AlterTable Dashboards

    Dashboards are unified views that collect multiple Insights (visualizations, metrics, and tables) to monitor KPIs and metrics. They consist of three primary components: Widgets (which hold insights, text, or grouped sections), a Grid layout for arranging and resizing elements, and Variables for applying shared filters across all widgets.

  8. Overview of Bucket Tables in Altertable

    Bucket Tables allow you to expose files stored in a connected bucket (object storage) as queryable external tables. This enables querying Parquet, CSV, or JSON files directly in their original location without copying the data into an Altertable catalog. Each table maps a logical table name to a specific path or prefix in the bucket, which the lakehouse SQL engine then reads.

  9. Overview of Altertable Product Analytics

    Altertable Product Analytics allows you to send product events and identity updates via SDKs or direct HTTP requests. All data is written to the built-in product_analytics catalog, enabling unified analysis of client-side, server-side, mobile, and batch data sources.

  10. Overview of Funnel Insights

    Funnel Insights allow you to track user progression through sequential events to measure conversion rates and identify where users drop off. This is primarily used for analyzing signup and onboarding flows, checkout and purchase processes, feature adoption journeys, and multi-step workflows.

  11. Overview of AlterTable Visualizations

    Visualizations in AlterTable allow users to transform lakehouse data into saved analyses. The system is built on two primary components: Insights and Dashboards. Use Insights for persistent, individual analyses (semantic or SQL views) that can be shared, embedded, or monitored. Use Dashboards to aggregate multiple insights into a single layout for KPI monitoring and automated follow-up analysis via scheduled tasks.

  12. Overview of MCP Apps for interactive AI interfaces

    MCP Apps is an extension to the Model Context Protocol (SEP-1865) that allows MCP tools to render interactive user interfaces (UIs) inside a host's chat interface (e.g., Claude). Unlike standard MCP tools that return text or JSON, MCP Apps provide a way to embed live, interactive components—such as hoverable charts or configurable forms—within a sandboxed iframe, enabling users to interact with data without leaving the chat environment.