Eclasess

Course available for lifetime after purchase

Course Duration:
Total Hours: 38 hours (38 sessions x 1 hour each)

This course provides comprehensive training in leveraging Azure Databricks, PySpark, and Spark SQL for processing and analyzing big data efficiently. Participants will gain hands-on experience with the tools and techniques essential for scalable and performant data processing in the cloud.

Course Objectives:
Understand the fundamentals of big data processing and distributed computing.
Gain proficiency in PySpark programming for data manipulation and analysis.
Learn to leverage Spark SQL for querying structured data.
Master advanced techniques for optimizing performance and scalability in Azure Databricks.
Develop practical skills in building end-to-end data pipelines for real-world use cases.
Course Outline:

Session 1-2: Introduction to Big Data Processing

Overview of big data concepts and challenges
Introduction to Apache Spark and Azure Databricks
Setting up Azure Databricks environment
Session 3-4: Getting Started with PySpark

Basics of PySpark programming
RDDs (Resilient Distributed Datasets) and DataFrame APIs
Data ingestion and transformation with PySpark
Session 5-6: Data Manipulation with PySpark

Advanced DataFrame operations in PySpark
Handling missing data and outliers
Exploratory data analysis (EDA) with PySpark
Session 7-8: Introduction to Spark SQL

Overview of Spark SQL and its advantages
Creating and manipulating SQL tables and views
Executing SQL queries in PySpark
Session 9-10: Optimizing Performance in Azure Databricks

Understanding the Spark execution model
Techniques for optimizing performance and resource utilization
Cluster configuration and tuning in Azure Databricks
Session 11-12: Advanced PySpark Concepts

User-defined functions (UDFs) and custom transformations
Window functions for advanced analytics
Handling complex data types in PySpark
Session 13-14: Introduction to Machine Learning with PySpark

Overview of machine learning in Spark MLlib
Building and evaluating machine learning models with PySpark
Integrating PySpark ML with Azure Databricks pipelines
Session 15-16: Real-time Data Processing with Spark Streaming

Introduction to Spark Streaming and Structured Streaming
Processing real-time data streams with PySpark
Building real-time dashboards with Databricks notebooks
Session 17-18: Building End-to-End Data Pipelines

Designing and implementing data pipelines in Azure Databricks
Orchestration and scheduling with Databricks Jobs
Integration with Azure services like Azure Blob Storage and Azure SQL Database
Session 19-38: Capstone Project Work

Guided project work with mentor support
Iterative development and refinement of data pipelines
Regular check-ins and progress updates
Prerequisites:
Basic knowledge of Python programming
Familiarity with SQL and relational databases
Understanding of fundamental concepts in data analysis and processing
Assessment:
Weekly assignments and exercises
Participation in discussions and group activities
Capstone project evaluation

Azure Databricks (Py Spark / Spark SQL) Full Course

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Azure Data Bricks Part - 1

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Hey! I’m learning with Eclasess and you can too. Here’s a course you might like: Azure Databricks (Py Spark / Spark SQL) Full Course by Eclasess
    https://learn.eclasess.com/courses/320830?utm_source=copy-link&utm_medium=social&utm_campaign=web-course-share

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