Databricks in Data & Analytics

Authenticate, configure, and explore data with Databricks CLI commands.

Create, configure, and deploy Databricks Declarative Automation Bundles for dashboards, jobs, and pipelines.

Create and deploy data engineering jobs on Databricks using notebooks, Python, SQL, or pipelines.

Build batch or streaming data pipelines on Databricks with Python or SQL.

Build dashboards and data apps on the Databricks platform.

Manage serverless Postgres databases on Databricks with autoscaling and synced tables.

Create and manage scalable REST API endpoints for LLMs and ML models.

Create and deploy Databricks dashboards with validated SQL queries.

Run Python, Scala, SQL, or R code on Databricks clusters and manage compute infrastructure.

Build vector search indexes and semantic search applications on Databricks.

Define and manage governed business metrics in YAML for consistent KPIs across teams.

Build real-time data ingestion clients that write directly to Databricks Delta tables.

Migrate Databricks workloads from classic compute to serverless with compatibility checks and fixes.

Manage Apache Iceberg tables and enable cross-platform data sharing on Databricks.

Look up official Databricks documentation for APIs, features, and platform guidance.

Master advanced SQL features, stored procedures, and warehouse optimization on Databricks.

Build production streaming pipelines with Kafka, joins, and real-time processing.

Build custom Python connectors to read and write data between Spark and external systems.

Query system tables and manage file volumes in Databricks Unity Catalog.

Build with Databricks SDK, Connect, CLI, and REST API.

Build web apps, dashboards, and APIs for Databricks with Python or TypeScript.

Set up and run AI evaluation pipelines with MLflow scorers and automated prompt optimization.

Generate realistic test datasets in Parquet, JSON, CSV, or Delta format at any scale.

Generate synthetic PDFs with test questions for document retrieval evaluation on Databricks.

Add AI tasks like classification, extraction, and summarization directly to SQL and PySpark pipelines.

Create knowledge assistants and supervisor agents for document Q&A and multi-agent workflows.

Design production-ready Databricks projects using best practices and the medallion pattern.

Optimize Databricks cluster configurations and query performance for faster jobs.

Optimize Databricks spending through cluster policies, spot instances, and cost monitoring.

Apply production-ready patterns for Databricks Python SDK integrations and best practices.

Create and query Databricks Genie Spaces for natural language SQL exploration.