Databricks

Target Audience:

• Data Analysts (beginners)

• Business Analysts

• Anyone interested in learning data analytics with big data tools

Course Objectives:

• Understand the fundamentals of big data, data lakes, and data warehouses.

• Grasp the core functionalities of Apache Spark for distributed data processing.

• Gain hands-on experience setting up a Databricks workspace and working with notebooks.

• Master DataFrames and Datasets, the essential data structures in Spark for data manipulation and analysis.

• Conquer data wrangling techniques using Spark SQL functions for cleaning, transforming, and shaping data for analysis.

• Leverage SQL functionalities within Spark to efficiently query large datasets.

• Explore Delta Lake, the unified storage layer for data lakes on Databricks, offering reliability and time travel capabilities.

• Learn data ingestion strategies to load data from various sources into Databricks.

• Gain experience with basic data visualization techniques to communicate insights from your analysis.

Course Structure:

Length: 3 Days

Big Data Fundamentals and Spark Introduction

• Explore the evolving landscape of big data and its impact on data-driven decision making.

• Demystify data lakes, data warehouses, and their roles in big data ecosystems.

• Understand the core components of Apache Spark for large-scale data processing: Spark Core and Spark SQL.

• Grasp the benefits of distributed processing offered by Spark.

Getting Started with Databricks

• Set up a Databricks workspace and navigate the user interface confidently.

• Explore Databricks clusters, configurations, and working with notebooks for data analysis tasks.

Spark Data Structures: DataFrames and Datasets

• Understand the concept of DataFrames and Datasets as the building blocks for data manipulation in Spark.

• Learn how to create, explore, and manipulate DataFrames and Datasets using Python or Scala (choose one language).

Data Wrangling and Analysis with Spark

• Gain proficiency in data wrangling techniques using Spark SQL functions for filtering, sorting, aggregating, and transforming data.

• Develop basic Spark SQL queries for data exploration and analysis.

Introduction to Delta Lake and Data Ingestion

• Explore Delta Lake, the unified storage layer for data lakes on Databricks, offering reliability and time travel capabilities.

• Understand the advantages of Delta Lake over traditional data lake storage formats.

• Learn data ingestion strategies to load data from various sources (CSV, JSON, databases) into your Databricks workspace.

Data Visualization and Course Wrap-up

• Gain experience with basic data visualization techniques to communicate insights from your analysis using libraries like Matplotlib or Plotly.

• Recap key learnings and discuss real-world applications of data analytics with Databricks.

• Explore resources for continuous learning and professional development in data analytics.