source / Databricks
    D

    Governed Databricks Answers

    Connect Databricks, Delta Lake, and Unity Catalog as governed lakehouse sources for AI answers. Answerplane understands Spark SQL and catalog lineage so teams can inspect plans before publishing outputs.

    14-day trial / no credit card / 150 governed questions included

    How It Works

    Scope Databricks, inspect the plan, and publish governed answers

    1

    Connect Your Databricks Workspace

    Provide Databricks connection details (workspace URL, access token, SQL warehouse ID or cluster ID). Supports personal access tokens and OAuth.

    2

    AI Learns Your Unity Catalog

    Answerplane introspects your catalogs, schemas, tables, views, and Delta tables. Understands Unity Catalog structure and metadata.

    3

    Inspect the Spark SQL Plan

    Ask 'What's the revenue trend by product?' or 'Show me recent data changes'. Then inspect the optimized Spark SQL and Unity Catalog context before reuse.

    Databricks-Specific Features

    Optimized for Databricks's unique capabilities

    01

    Spark SQL & Delta Lake Native

    Builds Spark SQL plans optimized for Databricks and Delta Lake. Understands Delta-specific features like time travel, MERGE, and schema evolution.

    02

    Unity Catalog Aware

    Works with Unity Catalog's three-level namespace (catalog.schema.table). Respects data governance, lineage, and access controls.

    03

    Lakehouse Optimization

    Builds plans optimized for lakehouse architecture with proper partitioning, predicate pushdown, and Z-ordering considerations.

    04

    Databricks SQL Support

    Compatible with Databricks SQL endpoints for fast analytics queries. Understands Photon acceleration and serverless compute.

    Example Queries

    See how governed questions become inspectable Databricks plans

    1

    "Show top products by sales volume"

    SELECT
      product_name,
      SUM(quantity) AS total_quantity,
      SUM(revenue) AS total_revenue
    FROM main.sales.orders
    GROUP BY product_name
    ORDER BY total_revenue DESC
    LIMIT 10;

    Explanation: Unity Catalog three-level namespace (catalog.schema.table)

    2

    "Query Delta table as of yesterday using time travel"

    SELECT
      customer_id,
      order_total,
      order_date
    FROM main.sales.orders VERSION AS OF 'yesterday'
    WHERE order_total > 1000;

    Explanation: Delta Lake time travel with version as of timestamp

    3

    "Get incremental changes using Delta change data feed"

    SELECT
      customer_id,
      order_id,
      _change_type,
      _commit_version
    FROM table_changes('main.sales.orders', 0)
    WHERE _change_type IN ('insert', 'update_postimage');

    Explanation: Delta Lake change data feed for incremental processing

    4

    "Calculate rolling 7-day average revenue"

    SELECT
      order_date,
      daily_revenue,
      AVG(daily_revenue) OVER (
        ORDER BY order_date
        ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
      ) AS rolling_avg_7day
    FROM (
      SELECT
        DATE(order_timestamp) AS order_date,
        SUM(order_total) AS daily_revenue
      FROM main.sales.orders
      GROUP BY order_date
    )
    ORDER BY order_date DESC;

    Explanation: Window function for moving averages in Spark SQL

    5

    "Find customers who made purchases in multiple categories"

    SELECT
      c.customer_name,
      COUNT(DISTINCT p.category) AS category_count,
      COLLECT_SET(p.category) AS categories_purchased
    FROM main.crm.customers c
    JOIN main.sales.orders o ON c.customer_id = o.customer_id
    JOIN main.inventory.products p ON o.product_id = p.product_id
    GROUP BY c.customer_id, c.customer_name
    HAVING COUNT(DISTINCT p.category) > 1
    ORDER BY category_count DESC;

    Explanation: Uses Spark SQL COLLECT_SET for array aggregation

    6

    "Parse nested JSON in struct columns"

    SELECT
      event_id,
      event_data.timestamp AS event_time,
      event_data.user.id AS user_id,
      event_data.user.name AS user_name,
      event_data.action AS action_type
    FROM main.analytics.events
    WHERE event_data.action = 'purchase';

    Explanation: Dot notation for querying nested struct columns

    These are just a few examples. Answerplane keeps the plan, query, and provenance visible before teams save or embed an answer.

    sec

    Security & Performance

    Your Databricks data is protected with enterprise-grade security

    Read-only access enforced via Databricks workspace permissions

    Personal access tokens encrypted at rest with AES-256

    Supports Unity Catalog's fine-grained access controls

    Respects row filters and column masks defined in Unity Catalog

    Query execution on isolated SQL warehouses or clusters

    Audit logs track all queries for compliance

    Multi-tenant organization-level isolation

    No wholesale source-data replication; saved artifacts retained only when features require it

    Frequently Asked Questions

    Everything you need to know about using Answerplane with Databricks

    How do I connect my Databricks workspace?+

    Go to Settings > Databases > Add Database, select Databricks, and provide your workspace URL (e.g., https://dbc-12345.cloud.databricks.com), personal access token, and SQL warehouse ID or cluster ID.

    Does Answerplane support Unity Catalog?+

    Yes! Answerplane fully supports Unity Catalog's three-level namespace (catalog.schema.table). It respects your data governance policies, access controls, and lineage tracking.

    Can I query Delta Lake tables?+

    Yes. Answerplane builds reviewable Spark SQL plans for Delta Lake tables and supports Delta-specific analytics features like time travel and change data feed. Write-oriented MERGE operations should stay outside read-only answer flows.

    Does it work with Databricks SQL?+

    Yes! Answerplane works with Databricks SQL endpoints (SQL warehouses) for fast analytics. It's optimized for Photon-accelerated queries and serverless compute.

    Can Answerplane query data in S3/ADLS/GCS via Databricks?+

    Yes. If Databricks exposes external tables or governed cloud-storage access, Answerplane can plan answers over that data using Spark SQL while preserving catalog and source context.

    How does Answerplane handle Databricks notebooks?+

    Answerplane focuses on reviewable SQL plans via Databricks SQL or clusters. It does not directly execute notebook cells, but approved plans can be copied into notebooks when your workflow requires it.

    What about complex Spark DataFrame operations?+

    Answerplane builds Spark SQL plans. Complex DataFrame transformations that require PySpark or Scala should remain in your engineering workflow, with Answerplane used for governed answer planning around the SQL-accessible layer.

    Is my Databricks data secure?+

    Yes. We enforce read-only access, encrypt credentials at rest, and rely on your source permissions as the system of record. We do not ingest or replicate source contents wholesale; saved chats, dashboards, exports, uploads, and materialized artifacts are retained only when you use those features.

    Can I use Answerplane with Databricks on AWS, Azure, and GCP?+

    Yes! Answerplane works with Databricks on all cloud platforms: AWS, Azure (Microsoft Azure), and Google Cloud Platform.

    Does it support Delta Lake time travel?+

    Yes. You can ask questions like 'Show me data from yesterday' and Answerplane will draft reviewable plans using Delta's VERSION AS OF or TIMESTAMP AS OF syntax.

    Still have questions?

    Contact our team
    Launch path

    Connect Databricks to governed AI answers with source control

    Let teams ask questions, inspect plans, and publish trusted Databricks answers with provenance attached.

    14-day trial / no credit card / 150 governed questions included