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Apache Spark Training in Chennai

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  • Hands-on Learning for Practical Experience.
  • Beginner and Advanced Level Classes in Apache Spark.
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Weekdays Regular

08:00 AM & 10:00 AM Batches

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


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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.
  • Classroom Batch Training
  • One To One Training
  • Online Training
  • Customized Training
  • Enroll Now

Course Objectives

  • Enhanced big data processing skills
  • Increased career opportunities
  • Improved data processing performance
  • Ability to handle large datasets
  • Access to a supportive community
  • Stay up-to-dated
  • Skill Enhancement
  • Career Advancement
  • Practical Experience
  • Networking
  • Expert Guidance
  • Job Opportunities
  • In-Depth Knowledge
  • Hands-On Experience
  • Structured Learning
  • Data Engineers
  • Data Scientists
  • Business Analysts
  • Big Data Developers

Yes, Apache Spark classes frequently contain practical activities. Practical activities are a crucial component of learning Apache Spark because they let students put the theoretical information they learn in class to use in actual situations.

  • Spark SQL
  • Spark Core
  • Spark MLlib
  • Spark GraphX
  • Spark Streaming

A fundamental understanding of programming and large data ideas, familiarity with SQL, and proficiency with a programming language like Java, Scala, or Python are all standard qualifications, though specifications can vary.

Is Apache Spark training a good choice for career?

In particular, if you are interested in big data, data engineering, data analytics, or machine learning, Apache Spark training is a great career choice. Numerous sectors place a high value on spark skills.

Why should I take Apache Spark course?

Studying Apache Spark can be helpful for experts in industries including data science, data engineering, software development, and business analysis.

Name the key topics covered in Apache Spark Course.

  • Spark architecture and components
  • Spark DataFrames and SQL
  • Spark Streaming
  • Spark MLlib
  • Spark GraphX
  • Cluster management and deployment

Mention some learning resources provided during Apache Spark training.

  • Textbooks
  • Documentation
  • Access to Tools/IDE's
  • Video tutorials
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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.


Additional Info

Benefits of Apache Spark

  • Speed: Apache Spark is renowned for its incredible speed because of its ability to process data in memory. In comparison to conventional batch processing systems like Hadoop MapReduce, it can process data up to 100 times quicker. For iterative machine learning algorithms and real-time data analytics, this speed gain is essential.
  • Ease of Use: A wide spectrum of developers and data scientists can use Spark since it offers high-level APIs in languages like Scala, Java, Python, and R. Tasks involving complicated data processing are made simpler by its readable APIs and libraries.
  • Platform Unified: Apache Spark provides a platform that may be used for batch processing, interactive querying, streaming, and machine learning. By doing away with the necessity for numerous specialized tools, complexity and management burden are decreased.
  • Scalability: Scalability is ensured by Spark's capacity to divide data processing duties among a cluster of computers. Large datasets are no problem for it, and it can expand to meet your data processing demands by adding more nodes to the cluster.
  • Flexibility: Spark contains libraries for a variety of data processing workloads, including Spark Streaming for real-time data processing, MLlib for machine learning, GraphX for processing graphs, and Spark SQL for structured data. With this flexibility, you may approach a variety of data difficulties using a single framework.
  • Advanced Analytics: Spark is suited for creating predictive models, recommendation engines, and other data-driven applications since it enables advanced analytics and machine learning.
  • Fault Tolerance: Spark offers fault tolerance via lineage information, enabling it to restore lost data or computation in the event of node failures and preserving data integrity.
  • Community & Ecosystem: There is a thriving and active open-source community for Apache Spark, which results in ongoing development, enhancements, and a plethora of tools and libraries made by the community.
  • Cost-Effective: Due to Spark's effective data processing and resource management, hardware infrastructure and operational costs may be reduced.
  • Real-Time Processing: Spark Streaming's real-time data processing feature makes it appropriate for applications that call for instantaneous insights from streaming data sources.

Future Scope of Apache Spark

  • Increasing Adoption: Due to its speed and adaptability, Apache Spark has become increasingly popular across industries. It is anticipated that Spark adoption will increase as more organizations become aware of its possibilities.
  • Processing real-time data: As real-time data insights become more crucial, Spark's streaming capabilities are expected to play an even bigger role. Applications like IoT, fraud detection, and real-time sentiment analysis of social media will all greatly benefit from it.
  • Machine Learning and AI: AI-powered apps, sophisticated analytics, and recommendation systems are all anticipated to make substantial use of Apache Spark's machine learning library, MLlib. Spark's function will be critical when machine learning becomes a key component of company plans.
  • Data Lakes: To combine and store enormous volumes of data, several enterprises are constructing data lakes. Spark is an essential part of contemporary data architectures because it is the perfect tool for processing and analyzing data in large data lakes.
  • Cloud Integration: It's anticipated that Spark's integration with cloud infrastructures like AWS, Azure, and Google Cloud will advance. Organizations will be able to use Spark's capabilities without having to manage complicated on-premises infrastructure thanks to this.
  • Industry Applications: Applications in many industries, such as banking, healthcare, e-commerce, and telecommunications, will be found for Apache Spark in the future. Innovation in these fields will be fueled by its capacity to process and evaluate a variety of datasets.
  • Advanced Analytics: Spark's strengths in graph processing (GraphX) and deep learning integration are positioned to contribute to more advanced analytics solutions, allowing organizations to extract worthwhile insights from their data.
  • Open Source Community: Apache Spark has an active open-source community, which results in constant development, enhancements, and the development of additional libraries and tools. As a result, Spark will continue to lead the pack in big data technology.
  • Opportunities for Employment: There will be a rising need for experts knowledgeable in Spark as Apache Spark continues to gain popularity.

Tools Used in Apache Spark

  • Spark Core: The Apache Spark core engine offers in-memory data processing capabilities and serves as the framework for other Spark libraries. It has APIs for cluster administration and distributed data processing.
  • Spark SQL: Using SQL-like queries, Spark SQL enables you to interact with structured and semi-structured data. Data lakes, data warehouses, and external databases can all be queried, among other sources.
  • Spark Streaming: This part enables analytics and real-time data processing. It is appropriate for applications like as log processing and fraud detection since it can process and analyze data streams from sources including Kafka, Flume, and HDFS.
  • Machine learning library (MLlib): The machine learning library for Spark offers a variety of tools and methods for creating and deploying machine learning models. It is employed in projects including recommendation, grouping, regression, and classification.
  • GraphX: GraphX is a Spark graph processing toolkit intended for processing massive amounts of graph data and graph analytics. Applications like social network analysis, fraud detection, and recommendation systems can all benefit from it.
  • SparkR: Data scientists and analysts may use the power of Apache Spark from within the R environment thanks to the SparkR R package. It permits the use of R syntax for data processing, analysis, and visualization.
  • PySpark: A Python library for Apache Spark, PySpark is comparable to SparkR. Because it gives Python programmers access to Spark's features, it is a popular option for data engineers and data scientists who use Python.
  • Spark DataFrames: For working with structured data, Spark DataFrames offers a higher-level API. They are compatible with both Python and Scala, enable optimizations, and are simple to utilize for data processing jobs.
  • SparkSubmit: The command-line program SparkSubmit is used to submit Spark applications to a cluster. It enables users to deploy Spark applications to a cluster and set different application parameters.
  • Cluster Manager: Different cluster managers, such as Apache Hadoop YARN, Apache Mesos, and Kubernetes, can be coupled with Apache Spark. For Spark applications running on a distributed cluster, these managers oversee resource scheduling and allocation.
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Key Features

ACTE Chennai offers Apache Spark Training in more than 27+ branches with expert trainers. Here are the key features,

  • 40 Hours Course Duration
  • 100% Job Oriented Training
  • Industry Expert Faculties
  • Free Demo Class Available
  • Completed 500+ Batches
  • Certification Guidance

Authorized Partners

ACTE TRAINING INSTITUTE PVT LTD is the unique Authorised Oracle Partner, Authorised Microsoft Partner, Authorised Pearson Vue Exam Center, Authorised PSI Exam Center, Authorised Partner Of AWS and National Institute of Education (nie) Singapore.


Syllabus of Apache Spark Training in Chennai
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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Hands-on Real Time Apache Spark Projects

Project 1
Healthcare Data Analytics

Analyze electronic health records (EHR) and medical data using Spark for healthcare insights.

Project 2
Multi-modal Data Fusion

Fuse data from multiple sources, such as text, images, and sensor data, to extract meaningful insights.

Excite Your Career With Apache Spark Job Opportunities

The Advanced Placement Assistance program from ACTE 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.

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.

  • Skill Enhancement
  • Career Advancement
  • Validation of Expertise
  • Increased Earning Potential

The time to obtain our Apache Spark certification ranges from a few weeks to a few months, depending on prior knowledge, study pace, and certification complexity.

  • Hortonworks Certified Spark Developer
  • AWS Certified Data Analytics - Specialty
  • HDPCD Spark Certification
  • Databricks Certified Developer for Apache Spark

Yes, obtaining an Apache Spark certification greatly improves your employment prospects. When hiring for positions involving big data, data engineering, and analytics, employers place a high value on credentialed individuals.

  • Basic Programming Skills
  • Database and SQL Knowledge
  • Understanding of Big Data Concepts

Complete Your Course

a downloadable Certificate in PDF format, immediately available to you when you complete your Course

Get Certified

a physical version of your officially branded and security-marked Certificate.

Get Certified

Gain the Best Knowledge From Apache Spark Trainers

  • Apache Spark trainers are experts in distributed data processing, with years of industry experience and deep knowledge.
  • They possess a comprehensive understanding of Spark's architecture, libraries, and ecosystem, making them valuable instructors.
  • These trainers have a knack for simplifying complex concepts, ensuring students grasp Spark's intricacies effectively.
  • Our experts employ real-world examples and hands-on exercises to reinforce learning and practical application.
  • In order to keep their training materials up to date and pertinent to market trends, Apache Spark Training in Chennai keeps up with the most recent advancements in the industry.
  • Their teaching methodologies emphasize problem-solving, enabling students to tackle real-data challenges confidently.

Apache Spark Software Course FAQs

Looking for better Discount Price?

Call now: +9193833 99991 and know the exciting offers available for you!
  • ACTE 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
  • ACTE 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
  • Please Visit Your Student Portal | Here FREE Lifetime Online Student Portal help you to access the Job Openings, Study Materials, Videos, Recorded Section & Top MNC interview Questions
    ACTE Gives Certificate For Completing A Course
  • Certification is Accredited by all major Global Companies
  • ACTE 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
  • The entire Apache Spark Software training has been built around Real Time Implementation
  • You Get Hands-on Experience with Industry Projects, Hackathons & lab sessions which will help you to Build your Project Portfolio
  • GitHub repository and Showcase to Recruiters in Interviews & Get Placed
All the instructors at ACTE are practitioners from the Industry with minimum 9-12 yrs of relevant IT experience. They are subject matter experts and are trained by ACTE for providing an awesome learning experience.
No worries. ACTE 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.
We offer this course in “Class Room, One to One Training, Fast Track, Customized Training & Online Training” mode. Through this way you won’t mess anything in your real-life schedule.

Why Should I Learn Apache Spark Software Course At ACTE?

  • Apache Spark Software Course in ACTE is designed & conducted by Apache Spark Software experts with 10+ years of experience in the Apache Spark Software domain
  • Only institution in India with the right blend of theory & practical sessions
  • In-depth Course coverage for 60+ Hours
  • More than 50,000+ students trust ACTE
  • Affordable fees keeping students and IT working professionals in mind
  • Course timings designed to suit working professionals and students
  • Interview tips and training
  • Resume building support
  • Real-time projects and case studies
Yes We Provide Lifetime Access for Student’s Portal Study Materials, Videos & Top MNC Interview Question.
You will receive ACTE globally recognized course completion certification Along with National Institute of Education (NIE), Singapore.
We have been in the training field for close to a decade now. We set up our operations in the year 2009 by a group of IT veterans to offer world class IT training & we have trained over 50,000+ aspirants to well-employed IT professionals in various IT companies.
We at ACTE believe in giving individual attention to students so that they will be in a position to clarify all the doubts that arise in complex and difficult topics. Therefore, we restrict the size of each Apache Spark Software batch to 5 or 6 members
Our courseware is designed to give a hands-on approach to the students in Apache Spark Software. The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.
You can contact our support number at 93833 99991 / Directly can do by's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India
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