Apache Spark And Pyspark For Data Engineering And Big Data

Learn Apache Spark and PySpark to build scalable data pipelines, process big data, and implement effective ML workflows.

What you'll learn

Understand Big Data Fundamentals: Explain the key concepts of big data and the evolution from Hadoop to Spark.

Learn Spark Architecture: Describe the core components and architecture of Apache Spark, including RDDs, DataFrames, and Datasets.

Set Up Spark: Install and configure Spark in local and standalone modes for development and testing.

Write PySpark Programs: Create and run PySpark applications using Python, including basic operations on RDDs and DataFrames.

Master RDD Operations: Perform transformations and actions on RDDs, such as map, filter, reduce, and groupBy, while leveraging caching and persistence.

Work with SparkContext and SparkSession: Understand their roles and effectively manage them in PySpark applications.

Work with DataFrames: Create, manipulate, and optimize DataFrames for structured data processing.

Run SQL Queries in SparkSQL: Use SparkSQL to query DataFrames and integrate SQL with DataFrame operations.

Handle Various Data Formats: Read and write data in formats such as CSV, JSON, Parquet, and Avro while optimizing data storage with partitioning and bucketing.

Build Data Pipelines: Design and implement batch and real-time data pipelines for data ingestion, transformation, and aggregation.

Learn Spark Streaming Basics: Process real-time data using Spark Streaming, including working with structured streaming and integrating with Kafka.

Optimize Spark Applications: Tune Spark applications for performance by understanding execution models, DAGs, shuffle operations, and memory management.

Leverage Advanced Spark Features: Utilize advanced DataFrame operations, including joins, aggregations, and window functions, for complex data transformations.

Explore Spark Internals: Gain a deep understanding of Spark’s execution model, Catalyst Optimizer, and techniques like broadcasting and partitioning.

Learn Spark MLlib Basics: Build machine learning pipelines using Spark MLlib, applying algorithms like linear regression and logistic regression.

Develop Real-Time Streaming Applications: Implement stateful streaming, handle late data, and manage fault tolerance with checkpointing in Spark Streaming.

Work on Capstone Projects: Design and implement an end-to-end data pipeline, integrating batch and streaming data processing with machine learning.

Prepare for Industry Roles: Apply Spark to real-world use cases, enhance resumes with Spark skills, prepare for technical interviews in data and ML engineering.

Requirements

Enthusiasm and determination to make your mark on the world!

Description

A warm welcome to the Apache Spark and PySpark for Data Engineering and Big Data course by Uplatz.Apache Spark is like a super-efficient engine for processing massive amounts of data. Imagine it as a powerful tool that can handle information that's way too big for a single computer to deal with. It does this by distributing the work across a cluster of computers, making the entire process much faster.Spark and PySpark provide a powerful and efficient way to process and analyze large datasets, making them essential tools for data scientists, engineers, and anyone working with big data.Key features of Spark that make it special:Speed: Spark can process data incredibly fast, even petabytes of it, because it distributes the workload and does a lot of the processing in memory.Ease of Use: Spark provides simple APIs in languages like Python, Java, Scala, and R, making it accessible to a wide range of developers.Versatility: Spark can handle various types of data processing tasks, including:Batch processing: Analyzing large datasets in bulk.Real-time streaming: Processing data as it arrives, like social media feeds or sensor data.Machine learning: Building and training AI models.Graph processing: Analyzing relationships between data points, like in social networks.PySpark is specifically designed for Python users who want to harness the power of Spark. It's essentially a Python API for Spark, allowing you to write Spark applications using familiar Python code.How PySpark brings value to the table:Pythonic Interface: PySpark lets you interact with Spark using Python's syntax and libraries, making it easier for Python developers to work with big data.Integration with Python Ecosystem: You can seamlessly integrate PySpark with other Python tools and libraries, such as Pandas and NumPy, for data manipulation and analysis.Community Support: PySpark has a large and active community, providing ample resources, tutorials, and support for users.Apache Spark and PySpark for Data Engineering and Big Data - Course CurriculumThis course is designed to provide a comprehensive understanding of Spark and PySpark, from basic concepts to advanced implementations, to ensure you well-prepared to handle large-scale data analytics in the real world. The course includes a balance of theory, hands-on practice including project work.Introduction to Apache SparkIntroduction to Big Data and Apache Spark, Overview of Big DataEvolution of Spark: From Hadoop to SparkSpark Architecture OverviewKey Components of Spark: RDDs, DataFrames, and DatasetsInstallation and SetupSetting Up Spark in Local Mode (Standalone)Introduction to the Spark Shell (Scala & Python)Basics of PySparkIntroduction to PySpark: Python API for SparkPySpark Installation and ConfigurationWriting and Running Your First PySpark ProgramUnderstanding RDDs (Resilient Distributed Datasets)RDD Concepts: Creation, Transformations, and ActionsRDD Operations: Map, Filter, Reduce, GroupBy, etc.Persisting and Caching RDDsIntroduction to SparkContext and SparkSessionSparkContext vs. SparkSession: Roles and ResponsibilitiesCreating and Managing SparkSessions in PySparkWorking with DataFrames and SparkSQLIntroduction to DataFramesUnderstanding DataFrames: Schema, Rows, and ColumnsCreating DataFrames from Various Data Sources (CSV, JSON, Parquet, etc.)Basic DataFrame Operations: Select, Filter, GroupBy, etc.Advanced DataFrame OperationsJoins, Aggregations, and Window FunctionsHandling Missing Data and Data Cleaning in PySparkOptimizing DataFrame OperationsIntroduction to SparkSQLBasics of SparkSQL: Running SQL Queries on DataFramesUsing SQL and DataFrame API TogetherCreating and Managing Temporary Views and Global ViewsData Sources and FormatsWorking with Different File Formats: Parquet, ORC, Avro, etc.Reading and Writing Data in Various FormatsData Partitioning and BucketingHands-on Session: Building a Data PipelineDesigning and Implementing a Data Ingestion PipelinePerforming Data Transformations and AggregationsIntroduction to Spark StreamingOverview of Real-Time Data ProcessingIntroduction to Spark Streaming: Architecture and BasicsAdvanced Spark Concepts and OptimizationUnderstanding Spark InternalsSpark Execution Model: Jobs, Stages, and TasksDAG (Directed Acyclic Graph) and Catalyst OptimizerUnderstanding Shuffle OperationsPerformance Tuning and OptimizationIntroduction to Spark Configurations and ParametersMemory Management and Garbage Collection in SparkTechniques for Performance Tuning: Caching, Partitioning, and BroadcastingWorking with DatasetsIntroduction to Spark Datasets: Type Safety and PerformanceConverting between RDDs, DataFrames, and DatasetsAdvanced SparkSQLQuery Optimization Techniques in SparkSQLUDFs (User-Defined Functions) and UDAFs (User-Defined Aggregate Functions)Using SQL Functions in DataFramesIntroduction to Spark MLlibOverview of Spark MLlib: Machine Learning with SparkWorking with ML Pipelines: Transformers and EstimatorsBasic Machine Learning Algorithms: Linear Regression, Logistic Regression, etc.Hands-on Session: Machine Learning with Spark MLlibImplementing a Machine Learning Model in PySparkHyperparameter Tuning and Model EvaluationHands-on Exercises and Project WorkOptimization Techniques in PracticeExtending the Mini-Project with MLlibReal-Time Data Processing and Advanced StreamingAdvanced Spark Streaming ConceptsStructured Streaming: Continuous Processing ModelWindowed Operations and Stateful StreamingHandling Late Data and Event Time ProcessingIntegration with KafkaIntroduction to Apache Kafka: Basics and Use CasesIntegrating Spark with Kafka for Real-Time Data IngestionProcessing Streaming Data from Kafka in PySparkFault Tolerance and CheckpointingEnsuring Fault Tolerance in Streaming ApplicationsImplementing Checkpointing and State ManagementHandling Failures and Recovering Streaming ApplicationsSpark Streaming in ProductionBest Practices for Deploying Spark Streaming ApplicationsMonitoring and Troubleshooting Streaming JobsScaling Spark Streaming ApplicationsHands-on Session: Real-Time Data Processing PipelineDesigning and Implementing a Real-Time Data PipelineWorking with Streaming Data from Multiple SourcesCapstone Project - Building an End-to-End Data PipelineProject IntroductionOverview of Capstone Project: End-to-End Big Data PipelineDefining the Problem Statement and Data SourcesData Ingestion and PreprocessingDesigning Data Ingestion Pipelines for Batch and Streaming DataImplementing Data Cleaning and Transformation WorkflowsData Storage and ManagementStoring Processed Data in HDFS, Hive, or Other Data StoresManaging Data Partitions and Buckets for PerformanceData Analytics and Machine LearningPerforming Exploratory Data Analysis (EDA) on Processed DataBuilding and Deploying Machine Learning ModelsReal-Time Data ProcessingImplementing Real-Time Data Processing with Structured StreamingIntegrating Streaming Data with Machine Learning ModelsPerformance Tuning and OptimizationOptimizing the Entire Data Pipeline for PerformanceEnsuring Scalability and Fault ToleranceIndustry Use Cases and Career PreparationIndustry Use Cases of Spark and PySparkDiscussing Real-World Applications of Spark in Various IndustriesCase Studies on Big Data Analytics using SparkInterview Preparation and Resume BuildingPreparing for Technical Interviews on Spark and PySparkBuilding a Strong Resume with Big Data SkillsFinal Project PreparationPresenting the Capstone Project for Resume and Instructions helpLearning Spark and PySpark offers numerous benefits, both for your skillset and your career prospects. By learning Spark and PySpark, you gain valuable skills that are in high demand across various industries. This knowledge can lead to exciting career opportunities, increased earning potential, and the ability to tackle challenging data problems in today's data-driven world.Benefits of Learning Spark and PySparkHigh Demand Skill: Spark and PySpark are among the most sought-after skills in the big data industry. Companies across various sectors rely on these technologies to process and analyze their data, creating a strong demand for professionals with expertise in this area.Increased Earning Potential: Due to the high demand and specialized nature of Spark and PySpark skills, professionals proficient in these technologies often command higher salaries compared to those working with traditional data processing tools.Career Advancement: Mastering Spark and PySpark can open doors to various career advancement opportunities, such as becoming a Data Engineer, Big Data Developer, Data Scientist, or Machine Learning Engineer.Enhanced Data Processing Capabilities: Spark and PySpark allow you to process massive datasets efficiently, enabling you to tackle complex data challenges and extract valuable insights that would be impossible with traditional tools.Improved Efficiency and Productivity: Spark's in-memory processing and optimized execution engine significantly speed up data processing tasks, leading to improved efficiency and productivity in your work.Versatility and Flexibility: Spark and PySpark can handle various data processing tasks, including batch processing, real-time streaming, machine learning, and graph processing, making you a versatile data professional.Strong Community Support: Spark and PySpark have large and active communities, providing ample resources, tutorials, and support to help you learn and grow.Career ScopeData Engineer: Design, build, and maintain the infrastructure for collecting, storing, and processing large datasets using Spark and PySpark.Big Data Developer: Develop and deploy Spark applications to process and analyze data for various business needs.Data Scientist: Utilize PySpark to perform data analysis, machine learning, and statistical modeling on large datasets.Machine Learning Engineer: Build and deploy machine learning models using PySpark for tasks like classification, prediction, and recommendation.Data Analyst: Analyze large datasets using PySpark to identify trends, patterns, and insights that can drive business decisions.Business Intelligence Analyst: Use Spark and PySpark to extract and analyze data from various sources to generate reports and dashboards for business intelligence.

Overview

Section 1: Spark Framework and PySpark Introduction

Lecture 1 Spark Framework and PySpark Introduction

Section 2: Spark and its Components

Lecture 2 Part 1 - Spark and its Components

Lecture 3 Part 2 - Spark and its Components

Section 3: Python Concepts for Big Data - Data Types & Data Structures

Lecture 4 Part 1 - Python Concepts for Big Data - Data Types & Data Structures

Lecture 5 Part 2 - Python Concepts for Big Data - Data Types & Data Structures

Lecture 6 Part 3 - Python Concepts for Big Data - Data Types & Data Structures

Section 4: Conditional Control Structure, Loops, Statement, Comprehensions

Lecture 7 Part 1 - Conditional Control Structure, Loops, Statement, Comprehensions

Lecture 8 Part 2 - Conditional Control Structure, Loops, Statement, Comprehensions

Section 5: Functions, Maps, Filters, Reduce, Lambda Expressions

Lecture 9 Part 1 - Functions, Maps, Filters, Reduce, Lambda Expressions

Lecture 10 Part 2 - Functions, Maps, Filters, Reduce, Lambda Expressions

Lecture 11 Part 3 - Functions, Maps, Filters, Reduce, Lambda Expressions

Section 6: Modules and Packages, their Methods and Attributes

Lecture 12 Part 1 - Modules and Packages, their Methods and Attributes

Lecture 13 Part 2 - Modules and Packages, their Methods and Attributes

Lecture 14 Part 3 - Modules and Packages, their Methods and Attributes

Section 7: Data Analysis with NumPy and Pandas

Lecture 15 Part 1 - Data Analysis with NumPy and Pandas

Lecture 16 Part 2 - Data Analysis with NumPy and Pandas

Lecture 17 Part 3 - Data Analysis with NumPy and Pandas

Lecture 18 Part 4 - Data Analysis with NumPy and Pandas

Lecture 19 Part 5 - Data Analysis with NumPy and Pandas

Section 8: Data Cleaning and Pre-processing

Lecture 20 Data Cleaning and Pre-processing

Section 9: Visualizations with Matplotlib and Seaborn

Lecture 21 Part 1 - Visualizations with Matplotlib and Seaborn

Lecture 22 Part 2 - Visualizations with Matplotlib and Seaborn

Lecture 23 Part 3 - Visualizations with Matplotlib and Seaborn

Section 10: Machine Learning and Build ML Models

Lecture 24 Part 1 - Machine Learning and Build ML Models

Lecture 25 Part 2 - Machine Learning and Build ML Models

Lecture 26 Part 3 - Machine Learning and Build ML Models

Lecture 27 Part 4 - Machine Learning and Build ML Models

Section 11: Case Study in Education Domain

Lecture 28 Case Study in Education Domain

Section 12: PySpark Architecture, Framework, and Processing Workflow

Lecture 29 PySpark Architecture, Framework, and Processing Workflow

Section 13: PySpark Data Frames with various Operations

Lecture 30 Part 1 - PySpark Data Frames with various Operations

Lecture 31 Part 2 - PySpark Data Frames with various Operations

Lecture 32 Part 3 - PySpark Data Frames with various Operations

Lecture 33 Part 4 - PySpark Data Frames with various Operations

Lecture 34 Part 5 - PySpark Data Frames with various Operations

Lecture 35 Part 6 - PySpark Data Frames with various Operations

Section 14: PySpark RDD and SQL Data Frames

Lecture 36 PySpark RDD and SQL Data Frames

Section 15: Data Cleaning and Data Profiling with PySpark

Lecture 37 Data Cleaning and Data Profiling with PySpark

Section 16: Pyspark Data Handling with Domain Specific

Lecture 38 Pyspark Data Handling with Domain Specific

Section 17: Human Resources Data Operations

Lecture 39 Human Resources Data Operations - JSON Mapping

Lecture 40 Human Resources Data Operations - Conditional Statements

Section 18: Data Visualizations with PySpark

Lecture 41 Data Visualizations with PySpark

Section 19: Machine Learning with PySpark MLlib

Lecture 42 Machine Learning with PySpark MLlib

Section 20: PySpark MLlib with Public Data Sources

Lecture 43 PySpark MLlib with Public Data Sources

Section 21: PySpark MLlib with Supervised ML and Unsupervised ML

Lecture 44 PySpark MLlib with Supervised ML and Unsupervised ML

Section 22: GraphX Component with GraphFrames Operations in PySpark

Lecture 45 GraphX Component with GraphFrames Operations in PySpark

Section 23: Spark Streaming Component and its Functionalities in PySpark

Lecture 46 Part 1 - Spark Streaming Component and its Functionalities in PySpark

Lecture 47 Part 2 - Spark Streaming Component and its Functionalities in PySpark

Section 24: Spark Streamimg Component with Multiple Files in Directory

Lecture 48 Spark Streamimg Component with Multiple Files in Directory

Section 25: End-to-end Project of PySpark with the Components of Spark in Big Data

Lecture 49 End-to-end Project of PySpark with the Components of Spark in Big Data

Data Engineers: Professionals seeking to build scalable big data pipelines using Apache Spark and PySpark.,Machine Learning Engineers: Engineers aiming to integrate big data frameworks into machine learning workflows for distributed model training and prediction.,Anyone aspiring for a career in Data Engineering, Big Data, Data Science, and Machine Learning.,Data Scientists: Those looking to process and analyze large datasets efficiently using Spark's advanced capabilities.,Newbies and beginners interested in data engineering, machine learning, AI research, and data science.,ETL Developers: Developers interested in transitioning from traditional ETL tools to modern, distributed big data processing systems.,Solution Architects: Professionals who design enterprise-level solutions and need expertise in scalable big data frameworks.,Data Architects: Experts responsible for designing data systems who want to incorporate Spark into their architecture for performance and scalability.,Software Engineers: Developers moving into data-intensive applications or big data engineering roles.,IT Professionals: Generalists looking to expand their knowledge of distributed computing and big data frameworks.,Students and Fresh Graduates: Aspiring data engineers, scientists, or analysts with foundational programming knowledge, eager to enter the big data space.,Database Administrators: DBAs aiming to understand modern big data processing to complement their database expertise.,Technical Managers and Architects: Leaders who need a foundational understanding of Spark and PySpark to manage teams and projects effectively.,Cloud Engineers: Engineers developing data workflows on cloud platforms like AWS, Azure, or Google Cloud.