Databricks Learning Path

Databricks is a powerful platform for data engineering, analytics, and machine learning. Here are some important things to learn to get the most out of Databricks:

1. Understanding the Basics Databricks Workspace: Learn how to navigate the Databricks workspace, which provides a centralized environment for collaboration.

Notebooks: Get familiar with Databricks notebooks, which are similar to Jupyter notebooks but designed for collaboration and flexibility.

2. Apache Spark Integration Spark Basics: Understand the basics of Apache Spark, the distributed computing framework that powers Databricks.

Spark Configuration: Learn how Databricks handles Spark configuration automatically, allowing you to focus on building data solutions.

3. Data Engineering Data Ingestion: Learn how to ingest data from various sources into Databricks.

Data Transformation: Understand how to transform and process data using Spark and Databricks.

Delta Lake: Get to know Delta Lake, which provides ACID transactions, schema enforcement, and real-time data consistency.

4. Data Science and Machine Learning

Model Training: Learn how to train machine learning models using Databricks.
Model Deployment: Understand how to deploy machine learning models in Databricks.
MLflow: Get familiar with MLflow, an open-source platform for managing the end-to-end machine learning lifecycle.

5. SQL Analytics

SQL Queries: Learn how to run SQL queries in Databricks.
Visualization: Understand how to create visualizations and dashboards using Databricks SQL Analytics.

6. Automation and Orchestration Jobs: Learn how to create and manage jobs in Databricks to automate workflows. Workflows: Understand how to orchestrate complex workflows using Databricks.

7. Security and Governance Access Control: Learn how to manage access control and permissions in Databricks.
Data Governance: Understand how to implement data governance practices in Databricks.

8. Cloud Integration Cloud Providers: Get familiar with integrating Databricks with major cloud providers like AWS, Azure, and Google Cloud.

9. Performance Optimization Optimization Techniques: Learn techniques to optimize the performance of your Databricks workloads.

10. Certification and Training Databricks Academy: Explore training and certification programs offered by Databricks to validate your skills and knowledge.