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DataBricks Fundamentals

Course Summary

This DataBricks Fundamentals course offers an in-depth, hands-on exploration of the DataBricks platform, guiding participants through its features and advantages for efficient data processing. Explore the DataBricks architecture, from clusters and notebooks to runtime integration with Apache Spark. Gain practical insights into data import, exploration, and transformation using Spark DataFrames and Spark SQL. Master advanced tasks such as machine learning workflows with MLlib and leveraging the power of DataBricks Delta for optimized data storage and operations. Learn about performance optimization techniques, job scheduling, and automation, ensuring streamlined workflows. Collaborative development practices and advanced data pipelines creation are also covered, empowering teams to implement best practices. With a focus on security, governance, and auditing, participants will leave equipped with the skills to unlock the full potential of DataBricks for their data-driven initiatives.

Purpose
Learn the features and benefits of using DataBricks for efficient data processing
Audience
Data Engineers and Data Scientists that rely on big data analytics and machine learning applications
Role
Software Developers | Data Engineers | Data Scientists
Skill level
Intermediate
Style
Lecture | Hands-on Activites | Labs
Duration
4 days
Related technologies
Apache Spark | MLlib | SQL | Machine Learning

 

Learning Objectives
  • Explain the advantages of using DataBricks
  • Describe the architecture and core components of DataBricks
  • Examine DataBricks tools and techniques
  • Apply use of the tools for efficient data processing

What you'll learn:

In this course, you'll learn:

Introduction to DataBricks

  • Overview of DataBricks platform and its features
  • Understanding the advantages of using DataBricks for data processing and analysis
  • Exploring the DataBricks workspace and user interface

DataBricks Architecture and Components

  • Understanding the core components of DataBricks, such as clusters, notebooks, and jobs
  • Exploring the DataBricks runtime and its integration with Apache Spark
  • Overview of DataBricks SQL, DataBricks Delta, and MLflow

DataBricks Notebooks

  • Creating and managing DataBricks notebooks
  • Exploring the notebook interface and features
  • Executing code cells and working with different programming languages (e.g., Python, Scala, SQL)

Data Import and Exploration

  • Importing data into DataBricks from various sources (e.g., CSV, JSON, Parquet)
  • Using DataBricks for data exploration and visualization
  • Leveraging DataBricks SQL for querying and manipulating data

Data Transformations with Apache Spark

  • Understanding the basics of Apache Spark
  • Performing data transformations using Spark DataFrames and Spark SQL
  • Applying common data manipulation operations (e.g., filtering, aggregating, joining)

Advanced Analytics with DataBricks

  • Leveraging DataBricks for advanced analytics tasks
  • Implementing machine learning workflows using DataBricks and MLlib
  • Training, evaluating, and deploying machine learning models with DataBricks

DataBricks Delta

  • Understanding DataBricks Delta and its advantages
  • Managing and optimizing data storage with DataBricks Delta
  • Performing efficient data operations (e.g., merge, upsert) using Delta Lake

Performance Optimization Techniques

  • Identifying performance bottlenecks in DataBricks workloads
  • Applying optimization techniques for faster data processing
  • Utilizing caching, partitioning, and broadcast joins for improved performance

Job Scheduling and Automation

  • Scheduling and managing jobs in DataBricks
  • Configuring automated workflows with DataBricks Jobs
  • Monitoring and troubleshooting job executions

Collaborative Development with DataBricks

  • Enabling collaboration and version control with DataBricks notebooks
  • Implementing team workflows and best practices for notebook development
  • Leveraging DataBricks Repos for notebook organization and sharing

Advanced Data Pipelines with DataBricks

  • Building complex data pipelines using DataBricks and Apache Spark
  • Implementing data ingestion, transformation, and storage solutions
  • Incorporating streaming data processing with Apache Kafka and DataBricks Streaming

Security and Governance

  • Understanding security features and options in DataBricks
  • Managing user access and permissions
  • Implementing data governance practices and auditing

DataBricks Fundamentals

real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
fully customized

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