Introduction to Databricks

• Overview of Databricks and its role in unified data analytics.

• Understanding cloud-based data engineering, data science, and machine learning platforms.

• Benefits of Databricks: scalability, collaboration, real-time analytics, and integration with Apache Spark.

• Real-world examples of Databricks in enterprise data pipelines and AI workflows.

Fundamentals of Databricks and Apache Spark

• Introduction to big data analytics and Apache Spark architecture.

• Databricks workspace: clusters, notebooks, libraries, and dashboards.

• Lab: Exploring the Databricks workspace and creating a sample notebook.

Data Engineering with Databricks

• Data ingestion: reading and writing data from various sources (CSV, JSON, Parquet, Delta Lake, cloud storage).

• Transformations and data cleaning using Spark SQL and DataFrames.

• Lab: Ingesting, transforming, and cleaning sample datasets.

Delta Lake and Data Management

• Understanding Delta Lake: ACID transactions, schema enforcement, and time travel.

• Managing and optimizing large-scale datasets.

• Lab: Implementing Delta Lake tables and performing time-travel queries.

Data Exploration and Visualization

• Data profiling, exploratory data analysis (EDA), and statistical summaries.

• Visualizing data with built-in Databricks charts and integration with matplotlib or seaborn.

• Lab: Creating visual dashboards and EDA reports.

Spark SQL and Advanced Querying

• Writing SQL queries in Databricks notebooks.

• Advanced query techniques: joins, aggregations, window functions.

• Lab: Executing complex SQL queries on large datasets.

Performance Optimization

• Understanding Spark execution plans and caching strategies.

• Partitioning, bucketing, and cluster resource management.

• Lab: Optimizing queries and transformations for performance in Databricks.

Machine Learning and AI Integration

• Introduction to MLlib and Databricks Machine Learning Runtime.

• Building predictive models: regression, classification, clustering.

• Lab: Implementing a machine learning model on a sample dataset.

Model Training, Evaluation, and Deployment

• Training and tuning machine learning models in Databricks.

• Evaluating models using metrics like accuracy, RMSE, and F1-score.

• Lab: Deploying a trained model and testing predictions in a notebook.

Streaming Data and Real-Time Analytics

• Introduction to structured streaming in Databricks.

• Processing and analyzing streaming data in real-time.

• Lab: Building a streaming data pipeline and performing real-time analytics.

Databricks Security and Governance

• Configuring user roles, access control, and workspace security.

• Data governance, audit logs, and compliance best practices.

• Lab: Setting up role-based access and secure clusters.

Collaboration and Version Control

• Collaborative development with notebooks, dashboards, and shared projects.

• Integrating Databricks with GitHub, GitLab, and CI/CD pipelines.

• Lab: Version-controlling notebooks and collaborating on a team project.

Advanced Analytics and AI Use Cases

• Natural Language Processing (NLP), recommendation systems, and deep learning integration.

• Leveraging GPUs and MLflow for experiment tracking and model lifecycle management.

• Lab: Implementing an AI use case and tracking experiments with MLflow.

Real-World Project Simulation

• Hands-on project simulating a complete data engineering and analytics workflow in Databricks.

• Tasks include data ingestion, cleaning, transformation, machine learning, visualization, and deployment.

• Trainer-led review, feedback, and discussion of optimization strategies.

Course Wrap-Up & Career Guidance

• Recap of Databricks features, workflows, and best practices.

• Career pathways: Data Engineer, Data Scientist, Machine Learning Engineer, Analytics Specialist.

• Q&A session with trainers, discussion of industry scenarios, and guidance for certifications and advanced learning.