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Databricks

Databricks is the data and AI platform teams use to build data pipelines, run analytics, train models, and query large-scale datasets using Spark and SQL. It often serves as the backbone for data engineering, analytics, and machine learning across the organization.

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Overview

PixieBrix connects to Databricks and turns it into a browser-wide data layer, making trusted insights available wherever work happens. Databricks remains the system of record; PixieBrix brings its outputs into the flow of work.

Actions

Query Databricks Data From Any Tool

PixieBrix lets teams run approved Databricks SQL queries or parameterized lookups directly from Zendesk, Salesforce, Slack, or internal web apps - without opening Databricks.

Surface Analytics and Metrics in Context

Key metrics, aggregates, and query results from Databricks can appear automatically alongside tickets, deals, incidents, or product discussions.

Expose Data Safely to Non-Technical Teams

PixieBrix enables buttons, sidebars, or panels that trigger predefined Databricks queries, giving business users access to trusted data without raw SQL or notebook access.

Bring Model Outputs Into Workflows

When Databricks is used for ML or forecasting, PixieBrix can surface predictions, classifications, or scores directly inside operational tools where decisions are made.

Unify Data Across Cross-Functional Workflows

Support, product, sales, and operations teams all reference the same Databricks-backed insights inside their daily tools.

Benefits

Databricks Insights Where Decisions Happen

Teams no longer jump to notebooks or dashboards to answer simple questions. PixieBrix delivers insights directly in context.

Faster Investigation and Resolution

Support and product teams validate usage, anomalies, or customer impact immediately - shortening feedback loops.

Consistent, Trusted Data

By surfacing Databricks as the single source of truth, PixieBrix prevents metric drift and conflicting interpretations.

Reduced Context Switching

PixieBrix eliminates tab-hopping between Databricks and other systems, keeping teams focused.

Broader Data Access Without Compromising Governance

Non-technical users gain access to approved data and model outputs without risking data security or platform sprawl.