Data Bricks
Master in Data Bricks
Elevate your data engineering and analytics expertise with Skill Elevate’s DataBricks training. Our courses are crafted to cover every stage, from data ingestion and processing to machine learning and visualization, ensuring you gain the most valuable skills in the field.
Course Curriculum
Introduction to DataBricks
- What is DataBricks?
- Key Features and Benefits
- DataBricks Architecture
- Setting Up a DataBricks Workspace
Data Bricks Essentials
- Navigating the DataBricks User Interface
- Working with Notebooks
- DataBricks CLI and REST API
- Integrating with Data Storage (Azure, AWS, GCP)
Apache Spark Basics
- Introduction to Apache Spark
- Spark Architecture and Components
- Spark SQL and DataFrames
- Spark RDDs (Resilient Distributed Datasets)
Data Ingestion and Preparation
- Loading Data into DataBricks
- Data Cleaning and Transformation
- Using DataBricks Delta Lake
- Handling Streaming Data with Spark Streaming
Data Analysis with Spark SQL
- Writing SQL Queries in DataBricks
- Performing Aggregations and Joins
- Working with Window Functions
- Using UDFs (User-Defined Functions)
Machine Learning with Data Bricks
- Introduction to MLlib (Spark Machine Learning Library)
- Building and Evaluating Machine Learning Models
- Hyperparameter Tuning and Model Selection
- Using AutoML in DataBricks
Advanced Machine Learning
- Distributed Machine Learning with Spark
- Integrating with MLFlow for Experiment Tracking
- Model Deployment in DataBricks
- Time Series Analysis and Forecasting
DataBricks Delta Lake
- Introduction to Delta Lake
- ACID Transactions and Schema Enforcement
- Time Travel and Data Versioning
- Optimizing Data Lakes with Delta Lake
Data Bricks Runtime
- Understanding DataBricks Runtime Versions
- Configuring and Managing Clusters
- Cluster Performance Tuning
- Using DataBricks Runtime for Genomics
Data Engineering with DataBricks
- Building Data Pipelines
- Orchestrating Workflows with DataBricks Jobs
- Using DataBricks Connect
- Integrating with Apache Airflow
DataBricks for Data Science
- Collaborative Data Science in DataBricks
- Visualizing Data with Matplotlib, Seaborn, and Plotly
- Using SQL Analytics
- Exploratory Data Analysis (EDA) in DataBricks
Security and Best Practices
- DataBricks Security Model
- Access Control and Permissions
- Managing Secrets with DataBricks
- Best Practices for Secure DataBricks Deployments
DataBricks and Cloud Integration
- Integrating DataBricks with Azure
- Integrating DataBricks with AWS
- Integrating DataBricks with GCP
- Hybrid and Multi-Cloud Deployments
Case Studies and Real-World Projects
- Real-World Use Cases
- Hands-on Projects
- Industry-Specific Solutions with DataBricks
Monitoring and Optimization
- Monitoring Workloads in DataBricks
- Performance Optimization Techniques
- Troubleshooting Common Issues
- Cost Management and Optimization
Conclusion and Future Trends
- Summary of Key Concepts
- Future Trends in Data Engineering and Analytics
- Resources for Further Learning
