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Python Spark Certification Training using PySpark

CertOcean's Python Spark Certification Training using PySpark training is a live, instructor-led online course. You learn in 19 live sessions (36 hours in total) with an expert instructor, work through 12 curriculum modules and keep lifetime access to the class recordings.

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  • 36 hoursLive training
  • 19 sessionsWith an instructor
  • 12 modules123 topics
  • Sat, Nov 14Next batch
PySpark ApacheLive instructor-led training
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Course overview

About the Python Spark Certification Training using PySpark training

PySpark certification training teaches you how to use Python with Apache Spark to process, analyze and stream large datasets. This live online course is led by industry practitioners and is built for developers, data engineers and analysts who want to work on big data projects.

You start with big data and Hadoop basics, then learn Python for Spark, Spark RDDs, DataFrames and Spark SQL, machine learning with Spark MLlib, and real-time processing with Kafka and Spark Streaming. Every practical runs in CertOcean's CloudLab, so there is nothing to install. You finish with an end-to-end project on real business data, earn a CertOcean certificate, and build the Spark skills tested in the Databricks Certified Associate Developer for Apache Spark exam.

This course includes

  • Instructor-led live sessions36 hours of live online classes. Weekend batch: 12 sessions of 3 hours each. Weekday batch: 18 sessions of 2 hours each.
  • Real-life case studiesBuild a live project on a real business use case using the PySpark concepts you learn in class.
  • Lifetime accessKeep lifetime access to the LMS, including slides, quizzes, installation guides and class recordings.
  • 24/7 expert supportGet help any time from our 24x7 support team through a ticket-based system, during the course and after it.
  • CertificationComplete the final course project to earn a CertOcean certificate as a PySpark (Apache Spark with Python) Developer.
  • Learner communityJoin our learner community to ask questions, share knowledge and learn from your peers.

What you'll learn in this course

  • Understand big data, Hadoop and where Apache Spark fits
  • Use Python functions, OOP and modules to write PySpark programs
  • Work with Spark RDDs, DataFrames and Spark SQL
  • Build machine learning models with Spark MLlib
  • Ingest data with Kafka and Flume and process it with Spark Streaming
  • Complete an end-to-end PySpark project on real business data

What is CertOcean's PySpark certification training?

CertOcean's PySpark certification training is a live online course that teaches you to build big data applications with Python and Apache Spark. You learn how Spark processes data in memory and why it runs much faster than Hadoop MapReduce, then use Spark SQL, RDDs, DataFrames, Spark MLlib and Spark Streaming on real datasets.

The course also covers Hadoop and HDFS basics, data ingestion with Kafka, Flume and Sqoop, and an end-to-end project, so you are ready for Spark developer roles and Spark certification exams.

What does the PySpark training include?

The PySpark training includes live classes, hands-on labs and real projects, all guided by an industry expert. You get:

  • In-depth coverage of the Spark ecosystem: Spark SQL, Spark Streaming, Spark MLlib, Kafka, Flume and Sqoop
  • An overview of big data and Hadoop, including HDFS and YARN
  • Practice loading data into HDFS with Flume and Sqoop and analyzing large datasets
  • Real-time data processing with Kafka, a publish-subscribe messaging system
  • Industry projects from finance, telecom, media, social media and government, run in CertOcean's CloudLab
  • One-on-one guidance from a subject matter expert on industry standards and best practices

Why should you learn PySpark?

You should learn PySpark because Apache Spark is one of the most widely used engines for big data processing, and PySpark is the most common way to use it from Python. Companies in finance, retail, healthcare, media and technology use Spark to process large datasets quickly, so Spark skills are in steady demand for data engineering and analytics roles.

Theory alone will not get you hired. In this course you solve real industry use cases in a live Spark environment, and every question is answered by professionals who work on big data projects today.

What skills will you learn in PySpark training?

In this PySpark training you will learn to:

  • Explain big data, Hadoop 2.x architecture, HDFS and YARN
  • Write Python programs for Spark using functions, OOP and modules
  • Create and transform Spark RDDs and run Spark operations in the PySpark shell
  • Query structured data with DataFrames and Spark SQL
  • Load data with Sqoop and run Spark applications on YARN
  • Build machine learning models, such as clustering, with Spark MLlib
  • Produce and consume messages with Kafka and integrate Flume with Kafka
  • Process live data streams with Spark Streaming
  • Solve real industry use cases in CertOcean's CloudLab

Who should take this PySpark course?

This PySpark course suits IT professionals who want to work with big data and Spark, as well as graduates starting a data career. It is a strong fit for:

  • Software developers and architects
  • BI, ETL and data warehouse professionals
  • Data scientists and analytics professionals
  • Big data engineers, developers and architects
  • Mainframe and senior IT professionals moving into data roles
  • Recent graduates who want a career in data engineering

How does PySpark certification training help your career?

PySpark certification training helps your career by giving you job-ready skills for data engineer, Spark developer and big data analyst roles. Many large organizations run Spark as part of their data platform, and employers look for people who can write working PySpark code, not just explain it.

By the end of the course you will have hands-on experience with RDDs, DataFrames, Spark SQL, streaming and MLlib, a completed end-to-end project to discuss in interviews, and a CertOcean certificate for your resume and LinkedIn profile.

What are the prerequisites for learning PySpark?

There are no mandatory prerequisites for this PySpark course. Some knowledge of Python programming and SQL helps, but it is not required, because the course starts with the Python basics you need for Spark.

How do the hands-on practicals work in the PySpark course?

You do all practicals, assignments and case studies in CertOcean's CloudLab, a ready-to-use Spark environment that you open in your web browser. If you get stuck, our support team is available 24x7 to help.

What is CertOcean CloudLab?

CloudLab is CertOcean's cloud-based Hadoop and Spark environment for learners in the PySpark course. You use it to run every in-class demo and to work on real Spark case studies.

Because CloudLab runs in your browser, you do not need to install or maintain Python, Spark or a virtual machine, and a basic laptop is enough. It also gives you hands-on experience on a real big data cluster, and our support team is available 24x7 if you get stuck.

What are the system requirements for the PySpark course?

You only need a computer with a modern web browser and a stable internet connection. All practicals run in CertOcean's CloudLab, which comes pre-configured with every tool and service the PySpark course uses, so there is nothing to install.

Which projects will you build in the PySpark training?

At the end of the PySpark training you complete real-world case studies as certification projects that prepare you for Spark developer roles. Examples include:

  • Finance (loan risk): A bank wants to lend to people without bank accounts. Using telecom and transaction data, you build a solution that approves customers who can repay and sets a loan amount, term and repayment schedule that helps them succeed.
  • Transportation (bike sharing demand): You combine past rental patterns with weather data to forecast bicycle rental demand, save the model, and run it in a Spark Streaming job that makes predictions on live requests.
Curriculum

Python Spark Certification Training using PySpark course syllabus

12 modules · 123 topics · 36 hours of live training

Introduction to Big Data, Hadoop and Spark 13 topics

Learning objective: Learning goal: Understand big data, the limits of traditional data systems and how Hadoop solves them. Learn the Hadoop ecosystem and architecture, rack awareness, HDFS and replication, the key configuration files in a Hadoop cluster, and where Spark fits, including the difference between batch and real-time processing.

  • What is big data? Real customer scenarios
  • Limits of traditional analytics architecture: an Uber use case
  • What is Hadoop and how does it solve the big data problem?
  • Hadoop core components and key characteristics
  • Hadoop ecosystem and HDFS
  • Big data analytics with batch and real-time processing
  • Rack awareness and block replication
  • YARN and its advantages
  • Hadoop cluster architecture and cluster modes
  • What is Spark and why is it needed?
  • How Spark differs from other frameworks
  • Spark's place in the Hadoop ecosystem
  • Spark at eBay
Introduction to Python for Apache Spark 14 topics
  • Learning goal: Learn the Python basics you need for Spark: data types, sequence structures and their operations, control flow, and reading and writing files.
  • Overview of Python and where it is used
  • Values, types, variables, operands and expressions
  • Numbers and strings
  • Lists, tuples, sets and dictionaries and their operations
  • Conditional statements and loops
  • Command-line arguments
  • Writing to the screen and Python file I/O
  • Write a “Hello World” program
  • Sets: properties and operations
  • Dictionaries: properties and operations
  • Lists: properties and operations
  • Tuples: properties, operations and comparison with lists
  • Conditional statements and loops
Python Functions, OOP and Modules 12 topics
  • Learning goal: Write reusable Python scripts, handle errors and exceptions, and extract content with regular expressions.
  • Functions and function parameters
  • Global variables, variable scope and return values
  • Lambda functions
  • Object-oriented programming concepts
  • Modules, import statements and the module search path
  • Standard libraries and ways to install packages
  • Functions: syntax, arguments, keyword arguments and return values
  • Lambda: syntax, features and comparison with functions
  • Sorting sequences and dictionaries, and the limits of sorting
  • Packages and modules: import options and sys.path
  • Errors and exceptions: types of issues and how to fix them
Deep Dive into the Apache Spark Framework 11 topics
  • Learning goal: Understand Apache Spark in depth, learn its components, build and run Spark applications, and ingest data with Sqoop.
  • Spark components and architecture
  • Spark deployment modes
  • Introduction to the PySpark shell
  • Submitting a PySpark job
  • Writing your first PySpark job in a Jupyter notebook
  • Spark web UI
  • Data ingestion with Sqoop
  • Build and run a Spark application
  • Explore the Spark application web UI
  • Configure Spark properties
Working with Spark RDDs 14 topics
  • Learning goal: Learn Spark RDDs and the transformations, actions and functions you use on them to implement business logic.
  • Challenges in existing computing methods and how RDDs solve them
  • What an RDD is: operations, transformations and actions
  • Loading and saving data with RDDs
  • Key-value pair RDDs and other pair RDDs
  • RDD lineage and persistence
  • A word count program using RDDs
  • RDD partitioning and how it improves parallelism
  • Passing functions to Spark
  • Load data into RDDs
  • RDD transformations, actions and functions
  • Save data from RDDs
  • Word count with RDDs
  • RDD partitions
DataFrames and Spark SQL 13 topics
  • Learning goal: Process structured data with Spark SQL queries, work with DataFrames and Datasets, run SQL operations on DataFrames, and integrate Spark with Hive.
  • What Spark SQL is and why you need it
  • Spark SQL architecture and SQLContext
  • DataFrames and Datasets
  • Schema RDDs and interoperating with RDDs
  • User-defined functions (UDFs)
  • JSON and Parquet file formats
  • Loading data from different sources
  • Spark and Hive integration
  • Create DataFrames with Spark SQL
  • Load and transform data from different sources
  • Stock market analysis
  • Spark and Hive integration
Machine Learning with Spark MLlib 7 topics
  • Learning goal: Understand why machine learning matters, and implement machine learning algorithms and techniques with Spark MLlib.
  • What machine learning is, why it matters and where it is used
  • Use case: face detection
  • Types of machine learning techniques
  • Introduction to MLlib
  • MLlib features and tools
  • Machine learning algorithms supported by MLlib
Apache Kafka and Apache Flume 13 topics

Learning objective: Learning goal: Understand Kafka and its architecture, configure different types of Kafka clusters, and produce and consume messages with the Kafka Java API. Learn Apache Flume, its architecture and how it works with Kafka for event processing, and ingest streaming data with Flume.

  • What Kafka is, why it is needed and where it is used
  • Kafka core concepts and architecture
  • Kafka cluster components and configuration
  • Kafka producer and consumer Java API
  • What Apache Flume is and why it is needed
  • Flume architecture, sources, sinks and channels
  • Flume configuration
  • Integrating Apache Flume with Apache Kafka
  • Configure a single-node, single-broker cluster
  • Configure a single-node, multi-broker cluster
  • Produce and consume messages with the Kafka Java API
  • Flume commands and setting up a Flume agent
  • Stream Twitter data into HDFS
Apache Spark Streaming: Processing Multiple Batches 10 topics

Learning objective: Learning goal: Build scalable, fault-tolerant streaming applications with Spark Streaming. Learn DStreams, the transformations you can run on streaming data, and common streaming operators such as stateful and sliding window operators.

  • Limits of existing computing methods and why streaming is needed
  • What Spark Streaming is and its features
  • Spark Streaming workflow
  • How Uber uses streaming data
  • StreamingContext and DStreams
  • Transformations on DStreams
  • Windowed operators and why they are useful
  • Slice, window and reduceByWindow operators
  • Stateful operators
  • A word count program with Spark Streaming
Apache Spark Streaming: Data Sources 5 topics
  • Learning goal: Learn the main streaming data sources, such as Apache Flume and Apache Kafka, and build your own Spark Streaming application by the end of the module.
  • Streaming data sources overview
  • Apache Flume and Apache Kafka data sources
  • Example: using a Kafka direct data source
  • Work with different Spark Streaming data sources
End-to-End PySpark Project 4 topics
  • Learning goal: Apply everything you have learned to a complete, real-world PySpark project.
  • Project 1, Finance: A bank wants to lend to people without bank accounts
  • Using telecom and transaction data, build a solution that approves customers who can repay and sets a loan amount, term and repayment schedule that helps them succeed
  • Project 2, Media and entertainment: Rank the best-performing movies from customer feedback and reviews, using both the Spark DataFrame API and the Spark RDD API.
Spark GraphX (Self-Paced) 7 topics
  • Learning goal: Learn Spark GraphX programming concepts and operations, along with common graph algorithms and how to implement them.
  • Introduction to Spark GraphX
  • GraphX basic APIs and operations
  • Getting information about a graph
  • GraphX algorithms: PageRank, personalized PageRank, triangle count, shortest paths, connected components, strongly connected components and label propagation
  • Minimum spanning trees
  • The traveling salesman problem

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Upcoming batches

Next Python Spark Certification Training using PySpark batches

Live online classes with an instructor. Pick the weekend or weekday batch that suits you.

Batch details
  • FormatLive online
  • Sessions19 live sessions
  • Total36 hours
  • BatchesWeekend and weekday
Weekend batch

Starts Saturday, November 14

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Weekday batch

Starts Monday, November 16

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  • Certificate of completionIssued by CertOcean when you finish the training and assignments.
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Learner reviews

Why learners choose CertOcean

Rated Excellent · 4.9 out of 5 on Trustpilot
The training curriculum was great. CertOcean provided me everything required to obtain my CISM. They're excellent!
Laxmi Haya Vadan KaramcheduUnited States · CISMVerified
Very resourceful company, I passed my PMP exam at first attempt, highly recommended.
IjeomaCanada · PMPVerified
Great training support! Helped me pass several certifications.
Cozumel TyUnited StatesVerified
  • US, Canada and UK hoursClasses set around your working day
  • Expert instructorsWorking professionals in your field
  • Career supportResume and interview help
FAQ

Python Spark Certification Training using PySpark course FAQs

Can't find your answer? A course advisor can help with the syllabus, batch dates and exam questions.

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How much does the Python Spark Certification Training using PySpark course cost?

The Python Spark Certification Training using PySpark training costs $439, down from the regular $499 (you save 12%). It is a one-time payment in US dollars, and you can pay securely by card or PayPal.

What happens if I miss a PySpark class?

You will not miss any content. You can either:

  • Watch the recording of the class in the LMS, or
  • Join the same session in another live batch.
Can I get help after the PySpark course ends?

Yes. Our support team is available 24x7 and answers your questions during the PySpark course and after it ends.

Does CertOcean offer job assistance after the PySpark course?

Yes. The LMS includes a resume builder that lets you create a professional resume in three simple steps, with unlimited templates for different job titles and roles. Log in to the LMS and click Create your resume to start. If you want one-on-one help, you can also book CertOcean's resume writing and interview preparation services.

Can I access the PySpark course material after the training ends?

Yes. You keep lifetime access to all PySpark course material in the LMS, including class recordings, slides and quizzes.

Can I attend a free demo before enrolling in the PySpark course?

Yes. Click Book a Free Demo on this page and an advisor will walk you through the course, the schedule and the CloudLab, and answer your questions. To keep live classes small and interactive, learners who have not enrolled cannot sit in on a running batch, but you can watch a sample class recording.

How long is the PySpark course?

The PySpark course has 36 hours of live online classes. You can choose a weekend batch with 12 sessions of 3 hours each, or a weekday batch with 18 sessions of 2 hours each, and you keep lifetime access to the class recordings.

Who teaches the PySpark course at CertOcean?

The PySpark course is taught by industry practitioners with at least 10 years of IT experience. Every instructor is a subject matter expert and is trained by CertOcean to run interactive, hands-on classes.

What is PySpark?

PySpark is the Python API for Apache Spark. It lets you write Spark applications in Python, combining Python's simplicity with Spark's speed for processing big data.

Apache Spark is an open-source engine that processes data in memory across a cluster, for both batch and real-time workloads. Teams use PySpark for data pipelines, streaming analytics, recommendation systems, fraud detection and machine learning.

What is an RDD in PySpark?

An RDD (Resilient Distributed Dataset) is Spark's core data structure: an immutable, distributed collection of objects. Spark splits each RDD into logical partitions that are processed in parallel on different nodes of the cluster, and it can rebuild lost partitions automatically, which makes RDDs fault-tolerant.

Is PySpark a programming language?

No, PySpark is not a programming language. It is the Python API for Apache Spark, which lets Python developers use Spark's in-memory, distributed processing without switching to Scala or Java.

Will I get a certificate after the Python Spark Certification Training using PySpark training?

Yes. You receive a CertOcean certificate of completion when you finish the training.

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