Databricks Workflows


             Databricks Workflows

Databricks Workflows is a fully managed orchestration service integrated with the Databricks Lakehouse Platform. It allows you to easily define, manage, and monitor complex data, analytics, and AI pipelines.

Key Features and Benefits:

  • Unified Orchestration: Orchestrate diverse tasks, including notebooks, Python scripts, JARs, SQL queries, Delta Live Tables (DLT) pipelines, and even debt jobs.
  • Deep Lakehouse Integration: Leverage the full power of the Databricks Lakehouse Platform, including Delta Lake, for reliable data storage and MLflow for experiment tracking.
  • Simplified Workflow Definition: Create workflows using a visual editor or through code (JSON), making it easy for both technical and non-technical users.
  • Advanced Observability: Gain a comprehensive understanding of your workflows with detailed logs, metrics, alerts, and a lineage graph for seamless troubleshooting and optimization, instilling confidence in your operations.High Reliability: Ensure your pipelines run reliably with features like automatic retries, error handling, and task-level monitoring.
  • Scalability: Seamlessly scale your workflows to handle massive amounts of data and complex computations.

Use Cases:

Databricks Workflows can be used to automate and orchestrate a wide range of data and AI pipelines, including:

  • ETL Pipelines: Extract, transform, and load data from various sources into your data lakehouse.
  • Data Analytics Pipelines: Run complex data analysis and reporting tasks on a schedule or triggered by events.
  • Machine Learning Pipelines: Train, validate, and deploy machine learning models at scale.

Databricks Training Demo Day 1 Video:

You can find more information about Databricks Training in this Dtabricks Docs Link



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