Eclasess

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Module 1: Introduction to Big Data and Data Processing (2 hours)

Big Data Concepts:
Volume, Velocity, Variety
Challenges of Big Data
Importance of Big Data Processing
Data Processing Frameworks:
Overview of Apache Spark
Introduction to Apache Hadoop (brief mention)
Understanding DataBricks (SaaS offering on top of Spark)
Hands-on Lab:
Setting Up a Databricks Account (Community Edition)
Creating Clusters and Notebooks
Exploring the Databricks Workspace
Module 2: Python Basics and Data Structures (2 hours)

Python Fundamentals:
Variables, Data Types (int, float, str, bool, etc.)
Operators (arithmetic, logical, comparison, etc.)
Control Flow (if-else, for loops, while loops)
Functions
Data Structures:
Lists (mutable, ordered collections of items)
Tuples (immutable, ordered collections of items)
Sets (unordered collections of unique elements)
Dictionaries (key-value pairs)
Hands-on Lab:
Working with Python in Databricks Notebooks
Creating and Manipulating Data Structures
Module 3: Apache Spark and Resilient Distributed Datasets (RDDs) (2 hours)

Apache Spark Introduction:
Spark Architecture (driver, executors, workers)
Components (RDD, DataFrame, Dataset)
RDDs (Resilient Distributed Datasets):
Creating RDDs from data sources (lists, files)
Transformations (map, filter, flatMap)
Actions (collect, count, reduce)
Fault Tolerance and Lineage Tracking
Hands-on Lab:
Creating RDDs in Databricks
Applying Transformations and Actions
Module 4: DataFrames and Datasets: Working with Structured Data (4 hours)

DataFrames:
Introduction (tabular data structure)
Creating DataFrames from RDDs, lists, files (CSV, Parquet)
Selecting Columns, Filtering Rows
Joining DataFrames (inner, outer, left, right)
Sorting and Aggregation Operations
Datasets:
Introduction (typed, schema-aware DataFrames)
Understanding Schema (data types, column names)
Benefits of Datasets (optimization, type safety)
Hands-on Lab:
Creating and Working with DataFrames
Reading/Writing CSV and Parquet Files
Joining, Sorting, and Aggregating DataFrames
Creating and Using Datasets
Module 5: Data Lake Storage and Processing with Azure Databricks (3 hours)

Azure Databricks Storage:
Data Lake Storage (ADL) Concepts
Mounting ADL Gen2 Storage to Databricks
Reading and Writing Data to ADL (Parquet, CSV, other formats)
Advanced Data Processing with PySpark:
Filtering Data (show vs. display)
Creating New Columns (concatenation, case-when statements)
Removing Duplicates
Finding Maximum/Minimum Values (overall and group-wise)
Aggregate Functions (sum, avg, count, etc.)
Union and Union All of DataFrames
Hands-on Lab:
Reading/Writing Data from/to ADL
Advanced Data Cleaning and Transformation Techniques
Module 6: Working with Delta Tables (3 hours)

Introduction to Delta Lake:
Benefits (ACID transactions, schema enforcement, time travel)
Creating Delta Tables:
Defining Schema
Writing Data to Delta Tables in Parquet Format
Delta Table Operations:
Reading Data
Schema Enforcement
Partitioning Delta Tables (organizing data by columns)
Time Travel (accessing historical versions)
Merging Data (updates, inserts, deletes)
Analyzing Table History
Rollback to Previous Versions
Hands-on Lab:
Creating and Managing Delta Tables
Performing Updates, Deletes, and Time Travel
Module 7: Advanced Topics and Notebooks (2 hours)

Parallelization and Optimization:
Broadcast Variables (sharing large data efficiently)
Caching (improving performance)
Broadcast Joins (optimizing joins with small

Self learning viedos DATA BRICKS-PYSPARK

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Azure Databricks Introduction

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Apache Spark Architecture

Maharashtra

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Hey! I’m learning with Eclasess and you can too. Here’s a course you might like: Self learning viedos DATA BRICKS-PYSPARK by Eclasess
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