Google's AI coding assistant integrated in IDEs and Cloud Workbench, providing completions, chat, and autonomous Agent Mode.

AI prediction of the next code tokens as you type, shown as ghost text before you accept the suggestion.

Ghost-text code proposals shown in the editor — press Tab to accept, Escape to dismiss.

Context-menu AI operations available by right-clicking code: explain, fix, generate tests, add docs, and more.

Conversational AI interface inside the IDE for Q&A, code generation, and multi-turn context-aware assistance.

Autonomous multi-step mode where Gemini reads your codebase, proposes a plan, and executes file changes with your approval.

Bronze → Silver → Gold data pipeline pattern: raw ingestion, cleaned/validated, and business-ready aggregated data tiers.

Python API for Apache Spark, used for distributed data processing across large datasets in Dataproc clusters.

Google's serverless, highly scalable cloud data warehouse — query petabytes of data using standard SQL.

Google Cloud managed Spark/Hadoop service that provisions clusters, runs jobs, and auto-scales without manual setup.

Google's managed Apache Airflow service for orchestrating data workflows using Python DAG definitions.

Airflow workflow definition that specifies tasks and their dependencies — no cycles allowed, tasks run in dependency order.

Tracking the flow and transformation of data from its source through each processing stage to its final destination.

Automated checks that incoming data matches expected field names, types, and constraints before processing continues.

AI-agent specification system that defines field-level contracts between teams, consumed by Gemini and Claude as structured context.

Continuous Integration / Continuous Delivery — automated pipeline that builds, tests, and deploys code on every commit.