33 Apache Spark courses

This course does not require any prior knowledge of Apache Spark or Hadoop. The author explains Spark architecture and fundamental concepts to help you come up to speed and grasp the content of this course. The course will help you understand Spark programming and apply that knowledge to build data engineering solutions.

A carefully structured advanced-level course on Apache Spark 3 to help you clear your job interviews. This course covers advanced topics and concepts that are part of the Databricks Spark certification exam. Boost your skills in Spark 3 architecture and memory management.

Learn the process to design and develop big data engineering projects using Apache Spark. This example-driven advanced-level course will help you understand real-time stream processing using Apache Spark and you can apply that knowledge to build real-time stream processing solutions.

Get to grips with real-time stream processing using PySpark as well as Spark structured streaming and apply that knowledge to build stream processing solutions. This course is example-driven and follows a working session-like approach.

This is a comprehensive and practical Apache Spark course. In this course, you will learn and master the art of framing data analysis problems as Spark problems through 20+ hands-on examples, and then scale them up to run on cloud computing services. Explore Spark 3, IntelliJ, Structured Streaming, and a stronger focus on the DataSet API.

DURATION 1 DAYS 6 CPD HOURS THIS COURSE IS INTENDED FOR THE AUDIENCE FOR THIS COURSE IS INDIVIDUALS WHO WANT TO LEARN THE FUNDAMENTALS OF DATABASE CONCEPTS IN A CLOUD ENVIRONMENT, GET BASIC SKILLING IN CLOUD DATA SERVICES, AND BUILD THEIR FOUNDATIONAL KNOWLEDGE OF CLOUD DATA SERVICES WITHIN MICROSOFT AZURE. OVERVIEW DESCRIBE CORE DATA CONCEPTS IDENTIFY CONSIDERATIONS FOR RELATIONAL DATA ON AZURE DESCRIBE CONSIDERATIONS FOR WORKING WITH NON-RELATIONAL DATA ON AZURE DESCRIBE AN ANALYTICS WORKLOAD ON AZURE DESCRIPTION IN THIS COURSE, STUDENTS WILL GAIN FOUNDATIONAL KNOWLEDGE OF CORE DATA CONCEPTS AND RELATED MICROSOFT AZURE DATA SERVICES. STUDENTS WILL LEARN ABOUT CORE DATA CONCEPTS SUCH AS RELATIONAL, NON-RELATIONAL, BIG DATA, AND ANALYTICS, AND BUILD THEIR FOUNDATIONAL KNOWLEDGE OF CLOUD DATA SERVICES WITHIN MICROSOFT AZURE. STUDENTS WILL EXPLORE FUNDAMENTAL RELATIONAL DATA CONCEPTS AND RELATIONAL DATABASE SERVICES IN AZURE. THEY WILL EXPLORE AZURE STORAGE FOR NON-RELATIONAL DATA AND THE FUNDAMENTALS OF AZURE COSMOS DB. STUDENTS WILL LEARN ABOUT LARGE-SCALE DATA WAREHOUSING, REAL-TIME ANALYTICS, AND DATA VISUALIZATION. 1 - EXPLORE CORE DATA CONCEPTS * Identify data formats * Explore file storage * Explore databases * Explore transactional data processing * Explore analytical data processing 2 - EXPLORE DATA ROLES AND SERVICES * Explore job roles in the world of data * Identify data services 3 - EXPLORE FUNDAMENTAL RELATIONAL DATA CONCEPTS * Understand relational data * Understand normalization * Explore SQL * Describe database objects 4 - EXPLORE RELATIONAL DATABASE SERVICES IN AZURE * Describe Azure SQL services and capabilities * Describe Azure services for open-source databases 5 - EXPLORE AZURE STORAGE FOR NON-RELATIONAL DATA * Explore Azure blob storage * Explore Azure DataLake Storage Gen2 * Explore Azure Files * Explore Azure Tables 6 - EXPLORE FUNDAMENTALS OF AZURE COSMOS DB * Describe Azure Cosmos DB * Identify Azure Cosmos DB APIs 7 - EXPLORE FUNDAMENTALS OF LARGE-SCALE DATA WAREHOUSING * Describe data warehousing architecture * Explore data ingestion pipelines * Explore analytical data stores 8 - EXPLORE FUNDAMENTALS OF REAL-TIME ANALYTICS * Understand batch and stream processing * Explore common elements of stream processing architecture * Explore Azure Stream Analytics * Explore Apache Spark on Microsoft Azure 9 - EXPLORE FUNDAMENTALS OF DATA VISUALIZATION * Describe Power BI tools and workflow * Describe core concepts of data modeling * Describe considerations for data visualization

Advance your data skills by mastering Spark programming in Python. This beginner's level course will help you understand the core concepts related to Apache Spark 3 and provide you with knowledge of applying those concepts to build data engineering solutions.

DURATION 5 DAYS 30 CPD HOURS THIS COURSE IS INTENDED FOR THIS INTERMEDIATE AND BEYOND LEVEL COURSE IS GEARED FOR EXPERIENCED TECHNICAL PROFESSIONALS IN VARIOUS ROLES, SUCH AS DEVELOPERS, DATA ANALYSTS, DATA ENGINEERS, SOFTWARE ENGINEERS, AND MACHINE LEARNING ENGINEERS WHO WANT TO LEVERAGE SCALA AND SPARK TO TACKLE COMPLEX DATA CHALLENGES AND DEVELOP SCALABLE, HIGH-PERFORMANCE APPLICATIONS ACROSS DIVERSE DOMAINS. PRACTICAL PROGRAMMING EXPERIENCE IS REQUIRED TO PARTICIPATE IN THE HANDS-ON LABS. OVERVIEW WORKING IN A HANDS-ON LEARNING ENVIRONMENT LED BY OUR EXPERT INSTRUCTOR YOU'LL: DEVELOP A BASIC UNDERSTANDING OF SCALA AND APACHE SPARK FUNDAMENTALS, ENABLING YOU TO CONFIDENTLY CREATE SCALABLE AND HIGH-PERFORMANCE APPLICATIONS. LEARN HOW TO PROCESS LARGE DATASETS EFFICIENTLY, HELPING YOU HANDLE COMPLEX DATA CHALLENGES AND MAKE DATA-DRIVEN DECISIONS. GAIN HANDS-ON EXPERIENCE WITH REAL-TIME DATA STREAMING, ALLOWING YOU TO MANAGE AND ANALYZE DATA AS IT FLOWS INTO YOUR APPLICATIONS. ACQUIRE PRACTICAL KNOWLEDGE OF MACHINE LEARNING ALGORITHMS USING SPARK MLLIB, EMPOWERING YOU TO CREATE INTELLIGENT APPLICATIONS AND UNCOVER HIDDEN INSIGHTS. MASTER GRAPH PROCESSING WITH GRAPHX, ENABLING YOU TO ANALYZE AND VISUALIZE COMPLEX RELATIONSHIPS IN YOUR DATA. DISCOVER GENERATIVE AI TECHNOLOGIES USING GPT WITH SPARK AND SCALA, OPENING UP NEW POSSIBILITIES FOR AUTOMATING CONTENT GENERATION AND ENHANCING DATA ANALYSIS. DESCRIPTION EMBARK ON A JOURNEY TO MASTER THE WORLD OF BIG DATA WITH OUR IMMERSIVE COURSE ON SCALA AND SPARK! MASTERING SCALA WITH APACHE SPARK FOR THE MODERN DATA ENTERPRISE IS A FIVE DAY HANDS ON COURSE DESIGNED TO PROVIDE YOU WITH THE ESSENTIAL SKILLS AND TOOLS TO TACKLE COMPLEX DATA PROJECTS USING SCALA PROGRAMMING LANGUAGE AND APACHE SPARK, A HIGH-PERFORMANCE DATA PROCESSING ENGINE. MASTERING THESE TECHNOLOGIES WILL ENABLE YOU TO PERFORM A WIDE RANGE OF TASKS, FROM DATA WRANGLING AND ANALYTICS TO MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE, ACROSS VARIOUS INDUSTRIES AND APPLICATIONS.GUIDED BY OUR EXPERT INSTRUCTOR, YOU?LL EXPLORE THE FUNDAMENTALS OF SCALA PROGRAMMING AND APACHE SPARK WHILE GAINING VALUABLE HANDS-ON EXPERIENCE WITH SPARK PROGRAMMING, RDDS, DATAFRAMES, SPARK SQL, AND DATA SOURCES. YOU?LL ALSO EXPLORE SPARK STREAMING, PERFORMANCE OPTIMIZATION TECHNIQUES, AND THE INTEGRATION OF POPULAR EXTERNAL LIBRARIES, TOOLS, AND CLOUD PLATFORMS LIKE AWS, AZURE, AND GCP. MACHINE LEARNING ENTHUSIASTS WILL DELVE INTO SPARK MLLIB, COVERING BASICS OF MACHINE LEARNING ALGORITHMS, DATA PREPARATION, FEATURE EXTRACTION, AND VARIOUS TECHNIQUES SUCH AS REGRESSION, CLASSIFICATION, CLUSTERING, AND RECOMMENDATION SYSTEMS. INTRODUCTION TO SCALA * Brief history and motivation * Differences between Scala and Java * Basic Scala syntax and constructs * Scala's functional programming features INTRODUCTION TO APACHE SPARK * Overview and history * Spark components and architecture * Spark ecosystem * Comparing Spark with other big data frameworks BASICS OF SPARK PROGRAMMING SPARKCONTEXT AND SPARKSESSION * Resilient Distributed Datasets (RDDs) * Transformations and Actions * Working with DataFrames SPARK SQL AND DATA SOURCES * Spark SQL library and its advantages * Structured and semi-structured data sources * Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) * Data manipulation using SQL queries BASIC RDD OPERATIONS * Creating and manipulating RDDs * Common transformations and actions on RDDs * Working with key-value data BASIC DATAFRAME AND DATASET OPERATIONS * Creating and manipulating DataFrames and Datasets * Column operations and functions * Filtering, sorting, and aggregating data INTRODUCTION TO SPARK STREAMING * Overview of Spark Streaming * Discretized Stream (DStream) operations * Windowed operations and stateful processing PERFORMANCE OPTIMIZATION BASICS * Best practices for efficient Spark code * Broadcast variables and accumulators * Monitoring Spark applications INTEGRATING EXTERNAL LIBRARIES AND TOOLS, SPARK STREAMING * Using popular external libraries, such as Hadoop and HBase * Integrating with cloud platforms: AWS, Azure, GCP * Connecting to data storage systems: HDFS, S3, Cassandra, etc. INTRODUCTION TO MACHINE LEARNING BASICS * Overview of machine learning * Supervised and unsupervised learning * Common algorithms and use cases INTRODUCTION TO SPARK MLLIB * Overview of Spark MLlib * MLlib's algorithms and utilities * Data preparation and feature extraction LINEAR REGRESSION AND CLASSIFICATION * Linear regression algorithm * Logistic regression for classification * Model evaluation and performance metrics CLUSTERING ALGORITHMS * Overview of clustering algorithms * K-means clustering * Model evaluation and performance metrics COLLABORATIVE FILTERING AND RECOMMENDATION SYSTEMS * Overview of recommendation systems * Collaborative filtering techniques * Implementing recommendations with Spark MLlib INTRODUCTION TO GRAPH PROCESSING * Overview of graph processing * Use cases and applications of graph processing * Graph representations and operations * Introduction to Spark GraphX * Overview of GraphX * Creating and transforming graphs * Graph algorithms in GraphX BIG DATA INNOVATION! USING GPT AND GENERATIVE AI TECHNOLOGIES WITH SPARK AND SCALA * Overview of generative AI technologies * Integrating GPT with Spark and Scala * Practical applications and use cases Bonus Topics / Time Permitting INTRODUCTION TO SPARK NLP * Overview of Spark NLP Preprocessing text data * Text classification and sentiment analysis PUTTING IT ALL TOGETHER * Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.

This course primarily focuses on explaining the concepts of Python and PySpark. It will help you enhance your data analysis skills using structured Spark DataFrames APIs.

DURATION 1 DAYS 6 CPD HOURS THIS COURSE IS INTENDED FOR THE PRIMARY AUDIENCE FOR THIS COURSE IS DATA PROFESSIONALS WHO ARE FAMILIAR WITH DATA MODELING, EXTRACTION, AND ANALYTICS. IT IS DESIGNED FOR PROFESSIONALS WHO ARE INTERESTED IN GAINING KNOWLEDGE ABOUT LAKEHOUSE ARCHITECTURE, THE MICROSOFT FABRIC PLATFORM, AND HOW TO ENABLE END-TO-END ANALYTICS USING THESE TECHNOLOGIES. JOB ROLE: DATA ANALYST, DATA ENGINEER, DATA SCIENTIST OVERVIEW DESCRIBE END-TO-END ANALYTICS IN MICROSOFT FABRIC DESCRIBE CORE FEATURES AND CAPABILITIES OF LAKEHOUSES IN MICROSOFT FABRIC CREATE A LAKEHOUSE INGEST DATA INTO FILES AND TABLES IN A LAKEHOUSE QUERY LAKEHOUSE TABLES WITH SQL CONFIGURE SPARK IN A MICROSOFT FABRIC WORKSPACE IDENTIFY SUITABLE SCENARIOS FOR SPARK NOTEBOOKS AND SPARK JOBS USE SPARK DATAFRAMES TO ANALYZE AND TRANSFORM DATA USE SPARK SQL TO QUERY DATA IN TABLES AND VIEWS VISUALIZE DATA IN A SPARK NOTEBOOK UNDERSTAND DELTA LAKE AND DELTA TABLES IN MICROSOFT FABRIC CREATE AND MANAGE DELTA TABLES USING SPARK USE SPARK TO QUERY AND TRANSFORM DATA IN DELTA TABLES USE DELTA TABLES WITH SPARK STRUCTURED STREAMING DESCRIBE DATAFLOW (GEN2) CAPABILITIES IN MICROSOFT FABRIC CREATE DATAFLOW (GEN2) SOLUTIONS TO INGEST AND TRANSFORM DATA INCLUDE A DATAFLOW (GEN2) IN A PIPELINE DESCRIPTION THIS COURSE IS DESIGNED TO BUILD YOUR FOUNDATIONAL SKILLS IN DATA ENGINEERING ON MICROSOFT FABRIC, FOCUSING ON THE LAKEHOUSE CONCEPT. THIS COURSE WILL EXPLORE THE POWERFUL CAPABILITIES OF APACHE SPARK FOR DISTRIBUTED DATA PROCESSING AND THE ESSENTIAL TECHNIQUES FOR EFFICIENT DATA MANAGEMENT, VERSIONING, AND RELIABILITY BY WORKING WITH DELTA LAKE TABLES. THIS COURSE WILL ALSO EXPLORE DATA INGESTION AND ORCHESTRATION USING DATAFLOWS GEN2 AND DATA FACTORY PIPELINES. THIS COURSE INCLUDES A COMBINATION OF LECTURES AND HANDS-ON EXERCISES THAT WILL PREPARE YOU TO WORK WITH LAKEHOUSES IN MICROSOFT FABRIC. INTRODUCTION TO END-TO-END ANALYTICS USING MICROSOFT FABRIC * Explore end-to-end analytics with Microsoft Fabric * Data teams and Microsoft Fabric * Enable and use Microsoft Fabric * Knowledge Check GET STARTED WITH LAKEHOUSES IN MICROSOFT FABRIC * Explore the Microsoft Fabric Lakehouse * Work with Microsoft Fabric Lakehouses * Exercise - Create and ingest data with a Microsoft Fabric Lakehouse USE APACHE SPARK IN MICROSOFT FABRIC * Prepare to use Apache Spark * Run Spark code * Work with data in a Spark dataframe * Work with data using Spark SQL * Visualize data in a Spark notebook * Exercise - Analyze data with Apache Spark WORK WITH DELTA LAKE TABLES IN MICROSOFT FABRIC * Understand Delta Lake * Create delta tables * Work with delta tables in Spark * Use delta tables with streaming data * Exercise - Use delta tables in Apache Spark INGEST DATA WITH DATAFLOWS GEN2 IN MICROSOFT FABRIC * Understand Dataflows (Gen2) in Microsoft Fabric * Explore Dataflows (Gen2) in Microsoft Fabric * Integrate Dataflows (Gen2) and Pipelines in Microsoft Fabric * Exercise - Create and use a Dataflow (Gen2) in Microsoft Fabric