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Apache Spark Training

  • Hands-on Learning for Practical Experience.
  • Beginner and Advanced Level Classes in Apache Spark.
  • Best Practice for Interview Preparation in Apache Spark.
  • Certified Apache Spark Expert With 9+ Years of Experience.
  • Trained 14,452+ Students and Worked With 380+ Recruiting Clients.
  • Next Apache Spark Batch to Begin This Week –  Enroll Your Name Now!
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This is How I.T. Ridge Students Prepare for Better Jobs

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Learn Our Resoureful Apache Spark Course

  • Our Apache Spark course offers comprehensive training in big data processing and analytics using a powerful open-source framework.
  • Participants will learn to harness the potential of Spark for distributed data processing and machine learning tasks.
  • Hands-on labs and projects are an integral part of the course, ensuring practical skill development.
  • We cover topics like Spark architecture, data manipulation, and optimizing performance for real-world applications.
  • Students gain insights into Spark's ecosystem, including Spark SQL, streaming, and MLlib.
  • By the end of the course, participants will be equipped to build scalable, high-performance data pipelines.
  • Enroll in our Apache Spark course to unlock the potential for data-driven insights and career opportunities.
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Syllabus of Apache Spark Training

Module 1: Introduction to Spark
  • Introduction to Spark
  • Spark overcomes the drawbacks of working on MapReduce
  • Understanding in-memory MapReduce
  • Interactive operations on MapReduce
  • Spark stack, fine vs. coarse-grained update, Spark Hadoop YARN, HDFS Revision, and YARN Revision
  • The overview of Spark and how it is better than Hadoop
  • Deploying Spark without Hadoop
  • Spark history server and Cloudera distribution
Module 2: Spark Basics
  • Spark installation guide
  • Spark configuration
  • Memory management
  • Executor memory vs. driver memory
  • Working with Spark Shell
  • The concept of resilient distributed datasets (RDD)
  • Learning to do functional programming in Spark
  • The architecture of Spark
Module 3: Working with RDDs in Spark
  • Spark RDD
  • Creating RDDs
  • RDD partitioning
  • Operations and transformation in RDD
  • Deep dive into Spark RDDs
  • The RDD general operations
  • Read-only partitioned collection of records
  • Using the concept of RDD for faster and efficient data processing
  • RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions
Module 4: Aggregating Data with Pair RDDs
  • Understanding the concept of key-value pair in RDDs
  • Learning how Spark makes MapReduce operations faster
  • Various operations of RDD
  • MapReduce interactive operations
  • Fine and coarse-grained update
  • Spark stack
Module 5: Writing and Deploying Spark Applications
  • Comparing the Spark applications with Spark Shell
  • Creating a Spark application using Scala or Java
  • Deploying a Spark application
  • Scala built application
  • Creation of the mutable list, set and set operations, list, tuple, and concatenating list
  • Creating an application using SBT
  • Deploying an application using Maven
  • The web user interface of Spark application
  • A real-world example of Spark
  • Configuring of Spark
Module 6: Parallel Processing
  • Learning about Spark parallel processing
  • Deploying on a cluster
  • Introduction to Spark partitions
  • File-based partitioning of RDDs
  • Understanding of HDFS and data locality
  • Mastering the technique of parallel operations
  • Comparing repartition and coalesce
  • RDD actions
Module 7: Spark RDD Persistence
  • The execution flow in Spark
  • Understanding the RDD persistence overview
  • Spark execution flow, and Spark terminology
  • Distribution shared memory vs RDD
  • RDD limitations
  • Spark shell arguments
  • Distributed persistence
  • RDD lineage
  • Key-value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey, and AggregateByKey
Module 8: Spark MLlib
  • Introduction to Machine Learning
  • Types of Machine Learning
  • Introduction to MLlib
  • Various ML algorithms supported by MLlib
  • Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques
Module 9: Integrating Apache Flume and Apache Kafka
  • Why Kafka and what is Kafka?
  • Kafka architecture
  • Kafka workflow
  • Configuring Kafka cluster
  • Operations
  • Kafka monitoring tools
  • Integrating Apache Flume and Apache Kafka
Module 10: Spark Streaming
  • Introduction to Spark Streaming
  • Features of Spark Streaming
  • Spark Streaming workflow
  • Initializing StreamingContext, discretized Streams (DStreams), input DStreams and Receivers
  • Transformations on DStreams, output operations on DStreams, windowed operators and why it is useful
  • Important windowed operators and stateful operators
Module 11: Improving Spark Performance
  • Introduction to various variables in Spark like shared variables and broadcast variables
  • Learning about accumulators
  • The common performance issues
  • Troubleshooting the performance problems
Module 12: Spark SQL and Data Frames
  • Learning about Spark SQL
  • The context of SQL in Spark for providing structured data processing
  • JSON support in Spark SQL
  • Working with XML data
  • Parquet files
  • Creating Hive context
  • Writing data frame to Hive
  • Reading JDBC files
  • Understanding the data frames in Spark
  • Creating Data Frames
  • Manual inferring of schema
  • Working with CSV files
  • Reading JDBC tables
  • Data frame to JDBC
  • User-defined functions in Spark SQL
  • Shared variables and accumulators
  • Learning to query and transform data in data frames
  • Data frame provides the benefit of both Spark RDD and Spark SQL
  • Deploying Hive on Spark as the execution engine
Module 13: Scheduling/Partitioning
  • Learning about the scheduling and partitioning in Spark
  • Hash partition
  • Range partition
  • Scheduling within and around applications
  • Static partitioning, dynamic sharing, and fair scheduling
  • Map partition with index, the Zip, and GroupByKey
  • Spark master high availability, standby masters with ZooKeeper
  • Single-node recovery with the local file system and high order functions
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Upcoming Batches

04-Nov-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

11-Nov-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

18-Nov-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

25-Nov-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

Key Features

  • 40 Hours Course Duration
  • Industry Expert Faculties
  • Completed 500+ Batches
  • 100% Job Oriented Training

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  • Free Demo Class Available
  • Have a portfolio of various data analysis projects.
  • Certification Guidance

Learn Apache Spark: Comprehensive Overview

The open-source data processing platform Apache Spark has revolutionized the field of big data analytics. It is made to handle and analyze large datasets quickly, effectively, and with minimal effort. Learn Apache Spark, which offers a potent in-memory processing engine that enables analysts, engineers, and data scientists to interact with data in previously impossible ways. Apache Spark stands out for its adaptability. It provides a wide variety of libraries and APIs within a single platform for jobs including data processing, machine learning, graph analysis, and more.

Excite Your Career With Apache Spark Job Opportunities

The Advanced Placement Assistance program from I.T. Ridge is available to students in order to assist them in locating employment and internship possibilities after completing the course.

  • Our Apache Spark job program connects learners with industry opportunities to apply their newfound skills effectively.
  • Graduates of the Apache Spark placement program are highly sought-after by organizations seeking big data expertise.
  • Participants in our placement training can leverage their Apache Spark expertise to secure positions in data engineering and data analysis roles.
  • We work with over 100 different software development firms, including HCL, Wipro, Dell, Accenture, Google, CTS, TCS, and IBM.
  • We offer valuable networking opportunities, allowing individuals to connect with professionals in the data industry.
  • On completion of a successful placement program graduates find themselves well-prepared for fulfilling and high-paying data-related positions.
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Obtain Our Industry Recognized Apache Spark Certification

For experts in big data and analytics, obtaining an Apache Spark Certification is a significant accomplishment. It provides as confirmation of your proficiency with Apache Spark, a potent distributed data processing framework. With this certification, you can effectively demonstrate your proficiency working with massive amounts of data, applying advanced analytics, and creating data-driven applications. The industry's acceptance of Apache Spark certification is among its most alluring features. Because qualified professionals can use Apache Spark to its maximum capacity, which leads to improved job options and higher earning potential, employers and organizations respect them highly.
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FAQ's

Call now: +91 8328526484 and know the exciting offers available for you!

I.T. Ridge is the Legend in offering placement to the students. Please visit our Placed Students List on our website

We have strong relationship with over 700+ Top MNCs like SAP, Oracle, Amazon, HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc.

More than 3500+ students placed in last year in India & Globally

I.T. Ridge conducts development sessions including mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.

85% percent placement record

Our Placement Cell support you till you get placed in better MNC

I.T. Ridge is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of AWS and National Institute of Education (NIE) Singapore

No worries. I.T. Ridge assure that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.

All the instructors at I.T. Ridge are practitioners from the Industry with minimum 9-12 yrs of relevant IT experience. They are subject matter experts and are trained by I.T. Ridge for providing an awesome learning experience.

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