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Apache Spark Training in Pune

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  • Hands-on Learning for Practical Experience.
  • Beginner and Advanced Level Classes in Apache Spark.
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  • Certified Apache Spark Expert With 9+ Years of Experience.
  • Trained Over 12,402+ Students and 350+ Recruiting Clients.
  • Next Apache Spark Batch to Begin This Week – Enroll Your Name Now!

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24-Jun-2024
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08:00 AM & 10:00 AM Batches

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

19-Jun-2024
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Weekdays Regular

08:00 AM & 10:00 AM Batches

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

22-Jun-2024
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22-Jun-2024
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Boost Your Career With Apache Spark Course

  • We offer guidance and mentorship to help you leverage your Apache Spark skills effectively in the gig economy or as a business venture.
  • Regular skill assessments and mock exams will gauge your progress and readiness for certification. These practice tests are designed to give you the confidence to excel in the final certification exam.
  • Practice your Apache Spark skills on cloud-based labs that provide a safe and flexible environment for hands-on learning.
  • Our curriculum is thoughtfully designed by industry experts to align with current market demands and job requirements.
  • No need to worry about setting up complex infrastructure; focus on learning and experimenting with ease.
  • Stay up-to-date with the latest developments in Apache Spark and emerge as a sought-after professional.
  • Resume and Interview Preparation Support, including Concepts like Spark and Hadoop, Introduction to Scala, SPARK Environment, SCALA Environment, Deep Dive into Scala, Traits and Mixins, Pattern Matching, Deep Dive into Spark, Parallel Programming, and Spark EcoSystem.
  • Classroom Batch Training
  • One To One Training
  • Online Training
  • Customized Training
  • Enroll Now

Course Objectives

Professionals should take Apache Spark training because it increases employment chances in data analytics and related sectors and offers skills in sophisticated data processing, scalability, integration with the big data ecosystem, and machine learning capabilities.
  • Professionals that complete Apache Spark training get enhanced data processing abilities
  • scalability
  • Integration with the big data ecosystem
  • Machine learning capabilities
  • Improving their job prospects and encouraging innovation in the data analytics industry.
  • The IT services
  • Software development
  • E-commerce
  • Financial
  • Telecommunications sectors in Pune place a high value on Apache Spark expertise.
Yes, individuals with no prior programming experience can benefit from Apache Spark training as it provides a structured learning path, hands-on exercises, and practical examples that gradually build programming skills. The training enables them to acquire foundational knowledge and develop proficiency in using Apache Spark for data processing and analytics, opening doors to exciting career opportunities in the field.
  • Resume building
  • Interview preparation
  • Connecting students with potential employers in the industry
While not mandatory, familiarity with programming concepts and basic knowledge of languages like Python or Scala can be beneficial for individuals interested in Apache Spark training. A basic understanding of big data concepts and experience with SQL may also be helpful for effective learning and application of Spark's capabilities.
  • Spark Core for distributed data processing and computation
  • Spark SQL for working with structured data using SQL queries and DataFrame API.
  • Other tools may include Spark Streaming for real-time data processing, MLlib for machine learning, and GraphX for graph processing.

Is there a certification upon completing the Apache Spark training?

  • Yes, upon completion of an Apache Spark training program, participants can often earn a certification that validates their knowledge and skills in Apache Spark.
  • This certification can enhance their credibility in the job market and demonstrate their proficiency in using Spark for data processing and analytics.

What topics are covered in the Apache Spark course curriculum?

  • Spark and Hadoop platform
  • Introduction to Scala
  • SPARK Environment
  • SCALA Environment
  • Traits and Mixins
  • Pattern Matching
  • Spark EcoSystem

How is Apache spark training different from other similar programs?

This training program differentiates itself from others in Pune by offering a comprehensive curriculum designed by industry experts, hands-on learning in Apache Spark, and lifetime access to a student portal with study materials, videos, and interview preparation resources, ensuring a holistic learning experience and strong career readiness.

What is the scope of Apache Spark in the future?

The future scope of Apache Spark is promising as it continues to be widely adopted in industries for big data processing, real-time analytics, and machine learning due to its scalability, performance, and integration capabilities, making it a crucial technology for organizations leveraging data-driven decision-making and advanced analytics.
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Apache Spark: Master Big Data Processing

Discover the robust features of Apache Spark, a flexible data processing platform created for the effective management of large-scale data. Apache Spark is the best tool for managing large datasets because of its capacity to quickly complete difficult processing jobs. Using it alone or in conjunction with other distributed computing technologies, it provides the freedom to divide data processing responsibilities across several machines. Begin your educational journey with us at ACTE, where we provide thorough online and classroom training for Apache Spark tutorial. Unlock the possibilities of this cutting-edge data processing technology by gaining practical experience.


Additional Info

Apache Spark: The Next Big Data Technology

The big data industry is undergoing a revolution because to Apache Spark's robust capabilities and flexible features. Spark, a state-of-the-art data processing platform, is positioned to influence the direction of big data analytics. Large datasets may be processed quite quickly, and the flexibility to split up data processing duties across several processors allows for scalability and effective computing. Spark is a popular option for addressing a variety of big data difficulties because to its robust ecosystem and support for several data processing tasks, including streaming, machine learning, and graph processing. Apache Spark is advancing big data analytics with its ongoing development and innovation, and it has enormous potential to influence the direction of data-driven insights and decision-making in the future.

A Practical Approach to Iterative Algorithms:

  • Apache Spark enables efficient handling of iterative algorithms through "iterative computation" or "iterative processing."
  • The Resilient Distributed Datasets (RDDs) abstraction in Spark allows iterative algorithms to run in a distributed and fault-tolerant manner.
  • RDDs store data in memory across multiple iterations, reducing serialization and disk I/O overhead for improved performance.
  • Spark's "caching" feature stores intermediate data in memory, speeding up subsequent iterations by reusing computed results.
  • Iterative processing in Spark optimizes algorithms that require multiple iterations over large datasets.
  • The caching mechanism in Spark prevents unnecessary data re-computation, enhancing iterative computation speed.
  • High-level machine learning libraries like Spark MLlib provide optimized implementations of popular iterative algorithms.
  • Spark MLlib includes algorithms like logistic regression, collaborative filtering, and k-means clustering.
  • These libraries leverage Spark's distributed computing capabilities for scalable and faster execution.
  • Overall, Apache Spark's iterative processing features and machine learning libraries offer an efficient and scalable framework for handling iterative algorithms on large datasets.

The Process Made So Simple by Apache Spark:

    With the help of many essential characteristics that make it simple to use and very effective, Apache Spark streamlines and simplifies dealing with massive data:

  • Apache Spark Programming: Apache Spark offers a single programming API that may be used for a variety of data processing activities, including batch processing, streaming, machine learning, and graph processing. Regardless of the precise job at hand, this uniform API lowers the learning curve and enables developers to create code in a familiar way.
  • In-Memory Processing: Spark uses in-memory computing to keep data in memory, making data access and processing much faster than they would be on a disk-based system. As a result of this feature's elimination of repetitive I/O operations, execution times are sped up and overall performance is enhanced.
  • Spark Parallel: It enables parallel processing by distributing data and computation over a cluster of computers. It is simple to scale up processing power as necessary since it automatically splits and distributes data among cluster nodes. With the help of this distributed computing paradigm, Spark is able to handle enormous datasets and carry out calculations very quickly.
  • High-Level Libraries: For typical data processing tasks, Spark provides high-level libraries, such as Spark SQL for structured data processing, Spark Streaming for real-time data processing, Spark MLlib for machine learning, and Spark GraphX for graph processing. These libraries offer pre-built functionality and optimised algorithms, simplifying the performance of complicated processes without requiring extensive custom implementation.
  • Spark Shell: Spark offers a user-friendly interactive shell (Spark Shell) that enables interactive data exploration and experimentation. It is simple to test concepts and draw conclusions from the data in this interactive environment since it supports rapid prototyping, iterative development, and ad-hoc analysis.
  • Community & Ecosystem: The ecosystem of tools, frameworks, and connectors for Apache Spark is diverse and flourishing. Improvements, bug fixes, and new features are continuously added as a consequence of this community-driven development. The ecosystem allows for easy and flexible integration with current systems by supporting a variety of data sources, data formats, cloud platforms, and third-party solutions.

Top Companies Using Apache Spark:

  • Amazon Web Services (AWS): On its cloud platform, AWS provides the Amazon EMR (Elastic MapReduce) service, which makes use of Apache Spark to handle massive data sets.
  • Databricks: Databricks offers a unified analytics platform that mainly relies on Spark for data processing, machine learning, and real-time analytics. Databricks was formed by the people who created Apache Spark.
  • Netflix: Netflix uses Apache Spark for analytics, content personalisation, and a variety of data processing jobs.
  • IBM: To allow scalable and effective big data processing and sophisticated analytics, IBM has integrated Apache Spark into its data analytics platform, IBM Watson.
  • Microsoft: Apache Spark is supported as a processing engine by Microsoft Azure HDInsight, a cloud-based big data analytics platform, enabling customers to take use of Spark's capabilities for data processing and analytics.
  • Intel: To improve their products and services, Intel leverages Apache Spark for massive Big Data Analytics Courses data analytics, machine learning, and real-time stream processing.
  • Adobe: To obtain insights and improve user experiences across their range of creative and marketing products, Adobe uses Apache Spark for data processing, analytics, and machine learning jobs.
  • Uber: For a variety of data processing andReal-time analytics application cases include fraud detection analytics, and demand forecasting, Uber makes use of Apache Spark.
  • LinkedIn: LinkedIn utilises Apache Spark for a variety of data-driven applications, such as fraud detection, content analysis, and personalised recommendations.
  • Salesforce: To improve its customer relationship management (CRM) platform and provide data-driven insights, Salesforce uses Apache Spark for data processing, analytics, and machine learning operations.

Apache Spark Ecosystem: Understanding the Components and Tools

  • Apache Spark Core: This is the foundation of the Spark ecosystem. It provides the distributed task scheduling, memory management, and fault tolerance capabilities that power all other Spark components.
  • Spark SQL: Spark SQL is a module in Apache Spark that provides a programming interface and optimized execution engine for querying structured and semi-structured data using SQL-like syntax. It enables seamless integration between relational and procedural processing.
  • Spark Streaming: Spark Streaming enables processing and analyzing real-time streaming data, making it suitable for applications like log analysis, fraud detection, and sentiment analysis. It ingests and processes data in mini-batches, allowing near-real-time analysis.
  • Spark MLlib: MLlib is Spark's machine learning library that offers a wide range of distributed machine learning algorithms and utilities. It simplifies the development of scalable machine learning pipelines, including feature extraction, model training, and evaluation.
  • Spark GraphX: Spark GraphX is a graph processing library that provides high-level APIs for graph computation and analytics. It allows users to perform graph-based operations such as graph traversal, motif finding, and graph algorithms like PageRank and connected components.
  • SparkR: SparkR is an R package that allows R users to interact with Spark, enabling them to leverage Spark's distributed computing capabilities from the R programming environment. It enables seamless integration between R and Spark data processing workflows.
  • Apache Zeppelin: Zeppelin is a web-based notebook that offers a collaborative, interactive environment for data exploration. It is practical for using Spark since it supports several programming languages, including Scala, Python, SQL, and R.
  • Apache Hive: Hive is a Hadoop Distributed File System (HDFS)-based data warehouse architecture that offers a SQL-like interface for querying and managing big datasets. Data processing between the two systems is simple because to its integration with Spark SQL.
  • Apache Kafka: Spark Streaming is frequently used in combination with Kafka, a distributed streaming platform. It is a popular option for real-time data input in Spark applications because it enables high-throughput, fault-tolerant streaming of data from multiple sources.
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Key Features

ACTE Pune 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.

Curriculum

Syllabus of Apache Spark Training in Pune
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
Real-time Twitter Sentiment Analysis

Analyze and visualize the sentiments of live Twitter feeds using Apache Spark's streaming capabilities.

Project 2
Movie Recommendation System

Build a collaborative filtering-based recommendation system with Spark's MLlib for personalized movie suggestions.

Apache Spark Training with Placement: Master Data Analytics

ACTE Pune provides placement opportunities as an add-on benefit to every student or professional who completes our classroom or online training.

  • Our association with top organizations like HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM, and more enables us to place our students in leading MNCs worldwide.
  • Our dedicated student portal provides access to interview schedules and timely email notifications, keeping students informed about placement opportunities.
  • Upon completing 70% of the Apache Spark training, we arrange interview calls and prepare students for face-to-face interactions with prospective employers.
  • Our Apache Spark training assist students in creating resumes that align with current industry requirements, maximizing their chances of securing desirable positions.
  • With a dedicated placement support team, we provide personalized assistance to students in securing placements based on their preferences and career goals.
  • We conduct mock exams and interviews to assess the knowledge gaps of candidates, helping them identify areas for improvement and enhance their interview performance.

Apache Spark Certification: Master Data Processing & Analytics

ACTE's certification, including the Apache Spark certification, holds global accreditation and is recognized by major companies worldwide. Upon completion of our comprehensive theoretical and practical training, both fresher's and corporate trainees receive a valuable certification that enhances their resumes and opens doors to prominent job positions in leading MNCs globally. This certification is awarded only after successfully completing our rigorous training program and practical-based projects.

Apache Spark certification validates the expertise and proficiency of individuals in utilizing Apache Spark for data processing, analytics, and machine learning. It serves as a recognized credential that demonstrates the ability to effectively leverage Spark's capabilities in real-world scenarios, enhancing career opportunities in the field of big data analytics.
  • Getting certified in Apache Spark is beneficial as it validates your skills and expertise in utilizing Spark for data processing and analytics, enhancing your credibility in the job market.
  • It opens up new career opportunities and demonstrates your commitment to continuous learning and professional growth in the field of big data analytics.

    Yes, Apache Spark certifications are available at different levels and types.

  • The certifications typically include beginner, intermediate, and advanced levels, allowing individuals to demonstrate their proficiency in various aspects of Apache Spark, such as data processing, analytics, machine learning, and optimization.
Having an Apache Spark certification provides several benefits, including enhanced career prospects, increased job opportunities in data processing and analytics roles, recognition of expertise in Apache Spark, and the ability to work on complex big data projects with confidence. It also validates your skills to employers and clients, boosting your professional credibility in the field.
In general, persons planning to take the Apache Spark certification exam are advised to have a fundamental grasp of programming principles, familiarity with Apache Spark, and hands-on experience with Spark projects.

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Learn Apache Spark from Our Industry Experts

  • Our Apache Spark training is delivered by certified professionals with a minimum of 9+ years of experience in their respective domains.
  • Our trainers bring their industry expertise to the training sessions by incorporating real-life projects, providing practical exposure to learn Apache Spark concepts and applications.
  • Our trainers have affiliations with renowned companies such as Cognizant, Dell, Infosys, IBM, L&T InfoTech, TCS, HCL Technologies, etc., offering valuable insights into industry practices and requirements.
  • Trainers actively support candidates in securing placements through employee referrals and internal hiring processes within their respective companies.
  • Our trainers are not only industry experts but also subject specialists who have mastered the art of running applications, delivering the best Apache Spark training to students.
  • Our Apache Spark training in Pune has received prestigious awards from recognized IT organizations, validating our commitment to delivering high-quality training and industry-relevant skills.

Apache Spark Software Course FAQs

Looking for better Discount Price?

Call now: +91 93833 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 +91 93800 99996 / Directly can do by ACTE.in's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India
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