By the end of this course, learners will be able to:

1. Understand Big Data fundamentals and the need for distributed data processing.
2. Learn the Apache Spark architecture, including driver, executors, DAG, and cluster modes.
3. Master RDDs and DataFrames, and how to manipulate data efficiently in memory.
4. Use PySpark to build scalable data pipelines using Python.
5. Perform data transformations and actions using Spark's core APIs.
6. Work with Spark SQL to query structured data using SQL-like syntax.
7. Integrate Spark with HDFS, Hive, and other data sources for data ingestion and processing.
8. Explore Spark MLlib for scalable machine learning workflows.
9. Build and manage ETL pipelines and understand Spark’s role in modern data engineering.
10. Optimize Spark jobs with caching, partitioning, and tuning techniques.

Apache Spark and PySpark Essentials for Data Engineering - Course Syllabus
This 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 Spark
• Introduction to Big Data and Apache Spark, Overview of Big Data
• Evolution of Spark: From Hadoop to Spark
• Spark Architecture Overview
• Key Components of Spark: RDDs, DataFrames, and Datasets
• Installation and Setup
• Setting Up Spark in Local Mode (Standalone)
• Introduction to the Spark Shell (Scala & Python)
• Basics of PySpark
• Introduction to PySpark: Python API for Spark
• PySpark Installation and Configuration
• Writing and Running Your First PySpark Program
• Understanding RDDs (Resilient Distributed Datasets)
• RDD Concepts: Creation, Transformations, and Actions
• RDD Operations: Map, Filter, Reduce, GroupBy, etc.
• Persisting and Caching RDDs
• Introduction to SparkContext and SparkSession
• SparkContext vs. SparkSession: Roles and Responsibilities
• Creating and Managing SparkSessions in PySpark
• Working with DataFrames and SparkSQL
• Introduction to DataFrames
• Understanding DataFrames: Schema, Rows, and Columns
• Creating DataFrames from Various Data Sources (CSV, JSON, Parquet, etc.)
• Basic DataFrame Operations: Select, Filter, GroupBy, etc.
• Advanced DataFrame Operations
• Joins, Aggregations, and Window Functions
• Handling Missing Data and Data Cleaning in PySpark
• Optimizing DataFrame Operations
• Introduction to SparkSQL
• Basics of SparkSQL: Running SQL Queries on DataFrames
• Using SQL and DataFrame API Together
• Creating and Managing Temporary Views and Global Views
• Data Sources and Formats
• Working with Different File Formats: Parquet, ORC, Avro, etc.
• Reading and Writing Data in Various Formats
• Data Partitioning and Bucketing
• Hands-on Session: Building a Data Pipeline
• Designing and Implementing a Data Ingestion Pipeline
• Performing Data Transformations and Aggregations
• Introduction to Spark Streaming
• Overview of Real-Time Data Processing
• Introduction to Spark Streaming: Architecture and Basics
• Advanced Spark Concepts and Optimization
• Understanding Spark Internals
• Spark Execution Model: Jobs, Stages, and Tasks
• DAG (Directed Acyclic Graph) and Catalyst Optimizer
• Understanding Shuffle Operations
• Performance Tuning and Optimization
• Introduction to Spark Configurations and Parameters
• Memory Management and Garbage Collection in Spark
• Techniques for Performance Tuning: Caching, Partitioning, and Broadcasting
• Working with Datasets
• Introduction to Spark Datasets: Type Safety and Performance
• Converting between RDDs, DataFrames, and Datasets
• Advanced SparkSQL
• Query Optimization Techniques in SparkSQL
• UDFs (User-Defined Functions) and UDAFs (User-Defined Aggregate Functions)
• Using SQL Functions in DataFrames
• Introduction to Spark MLlib
• Overview of Spark MLlib: Machine Learning with Spark
• Working with ML Pipelines: Transformers and Estimators
• Basic Machine Learning Algorithms: Linear Regression, Logistic Regression, etc.
• Hands-on Session: Machine Learning with Spark MLlib
• Implementing a Machine Learning Model in PySpark
• Hyperparameter Tuning and Model Evaluation
• Hands-on Exercises and Project Work
• Optimization Techniques in Practice
• Extending the Mini-Project with MLlib
• Real-Time Data Processing and Advanced Streaming
• Advanced Spark Streaming Concepts
• Structured Streaming: Continuous Processing Model
• Windowed Operations and Stateful Streaming
• Handling Late Data and Event Time Processing
• Integration with Kafka
• Introduction to Apache Kafka: Basics and Use Cases
• Integrating Spark with Kafka for Real-Time Data Ingestion
• Processing Streaming Data from Kafka in PySpark
• Fault Tolerance and Checkpointing
• Ensuring Fault Tolerance in Streaming Applications
• Implementing Checkpointing and State Management
• Handling Failures and Recovering Streaming Applications
• Spark Streaming in Production
• Best Practices for Deploying Spark Streaming Applications
• Monitoring and Troubleshooting Streaming Jobs
• Scaling Spark Streaming Applications
• Hands-on Session: Real-Time Data Processing Pipeline
• Designing and Implementing a Real-Time Data Pipeline
• Working with Streaming Data from Multiple Sources
• Capstone Project - Building an End-to-End Data Pipeline
• Project Introduction
• Overview of Capstone Project: End-to-End Big Data Pipeline
• Defining the Problem Statement and Data Sources
• Data Ingestion and Preprocessing
• Designing Data Ingestion Pipelines for Batch and Streaming Data
• Implementing Data Cleaning and Transformation Workflows
• Data Storage and Management
• Storing Processed Data in HDFS, Hive, or Other Data Stores
• Managing Data Partitions and Buckets for Performance
• Data Analytics and Machine Learning
• Performing Exploratory Data Analysis (EDA) on Processed Data
• Building and Deploying Machine Learning Models
• Real-Time Data Processing
• Implementing Real-Time Data Processing with Structured Streaming
• Integrating Streaming Data with Machine Learning Models
• Performance Tuning and Optimization
• Optimizing the Entire Data Pipeline for Performance
• Ensuring Scalability and Fault Tolerance
• Industry Use Cases and Career Preparation
• Industry Use Cases of Spark and PySpark
• Discussing Real-World Applications of Spark in Various Industries
• Case Studies on Big Data Analytics using Spark
• Interview Preparation and Resume Building
• Preparing for Technical Interviews on Spark and PySpark
• Building a Strong Resume with Big Data Skills
• Final Project Preparation
• Presenting the Capstone Project for Resume and Instructions help

After successfully completing the Apache Spark & PySpark Essentials for Data Engineering course, learners will receive a Course Completion Certificate from Uplatz, validating their expertise in distributed data processing and Python-based big data development.
This certification highlights your skills in high-demand technologies used in data engineering, ETL development, and real-time analytics, and prepares you for further certifications in Spark or cloud-based data engineering tracks.

Proficiency in Spark and PySpark is a must-have for modern data engineering roles. This course unlocks several career paths, including:

1. Data Engineer
2. Big Data Developer
3. Spark Developer
4. ETL Engineer
5. Data Analyst (Big Data)
6. Machine Learning Engineer (Spark MLlib)

Industries such as finance, retail, healthcare, tech, media, and telecom use Spark for large-scale data analysis, streaming, and batch processing—creating continuous demand for Spark-skilled professionals.

1. What is Apache Spark and how does it differ from Hadoop?
Apache Spark is a distributed data processing engine that offers in-memory computation for faster analytics, unlike Hadoop’s MapReduce, which relies on disk I/O.

2. What are RDDs in Spark?
RDDs (Resilient Distributed Datasets) are the fundamental data structure in Spark, representing immutable distributed collections of objects that can be processed in parallel.

3. What is PySpark?
PySpark is the Python API for Apache Spark, allowing users to write Spark applications using Python.

4. What is the difference between RDD and DataFrame?
RDD is low-level and gives full control, whereas DataFrame is a higher-level abstraction optimized for performance and is easier to use with SQL-like syntax.

5. How does Spark achieve fault tolerance?
Spark achieves fault tolerance through lineage information in RDDs, which allows it to recompute lost data without relying on data replication.

6. What is Spark SQL?
Spark SQL allows querying structured data using SQL-like commands. It integrates seamlessly with DataFrames and datasets.

7. How is data partitioned in Spark?
Spark partitions data based on keys, hash functions, or custom logic, allowing parallel processing across nodes in a cluster.

8. What is lazy evaluation in Spark?
Spark delays execution until an action is called. This optimization strategy allows Spark to build efficient execution plans.

9. How do you optimize a PySpark job?
Use techniques like broadcasting small datasets, caching/persisting data, tuning partition sizes, and minimizing data shuffles.

10. Can Spark handle streaming data?
Yes, using Spark Structured Streaming, Spark can process real-time data streams with fault tolerance and scalability.

Q1. What are the payment options? A1. We have multiple payment options: 1) Book your course on our webiste by clicking on Buy this course button on top right of this course page 2) Pay via Invoice using any credit or debit card 3) Pay to our UK or India bank account 4) If your HR or employer is making the payment, then we can send them an invoice to pay. Q2. Will I get certificate? A2. Yes, you will receive course completion certificate from Uplatz confirming that you have completed this course with Uplatz. Once you complete your learning please submit this for to request for your certificate https://training.uplatz.com/certificate-request.php Q3. How long is the course access? A3. All our video courses comes with lifetime access. Once you purchase a video course with Uplatz you have lifetime access to the course i.e. forever. You can access your course any time via our website and/or mobile app and learn at your own convenience. Q4. Are the videos downloadable? A4. Video courses cannot be downloaded, but you have lifetime access to any video course you purchase on our website. You will be able to play the videos on our our website and mobile app. Q5. Do you take exam? Do I need to pass exam? How to book exam? A5. We do not take exam as part of the our training programs whether it is video course or live online class. These courses are professional courses and are offered to upskill and move on in the career ladder. However if there is an associated exam to the subject you are learning with us then you need to contact the relevant examination authority for booking your exam. Q6. Can I get study material with the course? A6. The study material might or might not be available for this course. Please note that though we strive to provide you the best materials but we cannot guarantee the exact study material that is mentioned anywhere within the lecture videos. Please submit study material request using the form https://training.uplatz.com/study-material-request.php Q7. What is your refund policy? A7. Please refer to our Refund policy mentioned on our website, here is the link to Uplatz refund policy https://training.uplatz.com/refund-and-cancellation-policy.php Q8. Do you provide any discounts? A8. We run promotions and discounts from time to time, we suggest you to register on our website so you can receive our emails related to promotions and offers. Q9. What are overview courses? A9. Overview courses are 1-2 hours short to help you decide if you want to go for the full course on that particular subject. Uplatz overview courses are either free or minimally charged such as GBP 1 / USD 2 / EUR 2 / INR 100 Q10. What are individual courses? A10. Individual courses are simply our video courses available on Uplatz website and app across more than 300 technologies. Each course varies in duration from 5 hours uptop 150 hours. Check all our courses here https://training.uplatz.com/online-it-courses.php?search=individual Q11. What are bundle courses? A11. Bundle courses offered by Uplatz are combo of 2 or more video courses. We have Bundle up the similar technologies together in Bundles so offer you better value in pricing and give you an enhaced learning experience. Check all Bundle courses here https://training.uplatz.com/online-it-courses.php?search=bundle Q12. What are Career Path programs? A12. Career Path programs are our comprehensive learning package of video course. These are combined in a way by keeping in mind the career you would like to aim after doing career path program. Career path programs ranges from 100 hours to 600 hours and covers wide variety of courses for you to become an expert on those technologies. Check all Career Path Programs here https://training.uplatz.com/online-it-courses.php?career_path_courses=done Q13. What are Learning Path programs? A13. Learning Path programs are dedicated courses designed by SAP professionals to start and enhance their career in an SAP domain. It covers from basic to advance level of all courses across each business function. These programs are available across SAP finance, SAP Logistics, SAP HR, SAP succcessfactors, SAP Technical, SAP Sales, SAP S/4HANA and many more Check all Learning path here https://training.uplatz.com/online-it-courses.php?learning_path_courses=done Q14. What are Premium Career tracks? A14. Premium Career tracks are programs consisting of video courses that lead to skills required by C-suite executives such as CEO, CTO, CFO, and so on. These programs will help you gain knowledge and acumen to become a senior management executive. Q15. How unlimited subscription works? A15. Uplatz offers 2 types of unlimited subscription, Monthly and Yearly. Our monthly subscription give you unlimited access to our more than 300 video courses with 6000 hours of learning content. The plan renews each month. Minimum committment is for 1 year, you can cancel anytime after 1 year of enrolment. Our yearly subscription gives you unlimited access to our more than 300 video courses with 6000 hours of learning content. The plan renews every year. Minimum committment is for 1 year, you can cancel the plan anytime after 1 year. Check our monthly and yearly subscription here https://training.uplatz.com/online-it-courses.php?search=subscription Q16. Do you provide software access with video course? A16. Software access can be purchased seperately at an additional cost. The cost varies from course to course but is generally in between GBP 20 to GBP 40 per month. Q17. Does your course guarantee a job? A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience. Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey! Q18. Do you provide placement services? A18. While our course is designed to provide you with a comprehensive understanding of the subject, we currently do not offer placement services as part of the course package. Our main focus is on delivering high-quality education and equipping you with essential skills in this field. However, we understand that finding job opportunities is a crucial aspect of your career journey. We recommend exploring various avenues to enhance your job search: a) Career Counseling: Seek guidance from career counselors who can provide personalized advice and help you tailor your job search strategy. b) Networking: Attend industry events, workshops, and conferences to build connections with professionals in your field. Networking can often lead to job referrals and valuable insights. c) Online Professional Network: Leverage platforms like LinkedIn, a reputable online professional network, to explore job opportunities that resonate with your skills and interests. d) Online Job Platforms: Investigate prominent online job platforms in your region and submit applications for suitable positions considering both your prior experience and the newly acquired knowledge. e.g in UK the major job platforms are Reed, Indeed, CV library, Total Jobs, Linkedin. While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities. Q19. How do I enrol in Uplatz video courses? A19. To enroll, click on "Buy This Course," You will see this option at the top of the page. a) Choose your payment method. b) Stripe for any Credit or debit card from anywhere in the world. c) PayPal for payments via PayPal account. d) Choose PayUmoney if you are based in India. e) Start learning: After payment, your course will be added to your profile in the student dashboard under "Video Courses". Q20. How do I access my course after payment? A20. Once you have made the payment on our website, you can access your course by clicking on the "My Courses" option in the main menu or by navigating to your profile, then the student dashboard, and finally selecting "Video Courses". Q21. Can I get help from a tutor if I have doubts while learning from a video course? A21. Tutor support is not available for our video course. If you believe you require assistance from a tutor, we recommend considering our live class option. Please contact our team for the most up-to-date availability. The pricing for live classes typically begins at USD 999 and may vary.