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Apache Spark Course in Hyderabad

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  • Beginner and Advanced Level Classes in Apache Spark.
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  • Certified Apache Spark Expert With 9+ Years of Experience.
  • Trained 14,452+ Students and Worked With 380+ Recruiting Clients.
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Upcoming Batches

17-Jun-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

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

12-May-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

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

15-Jun-2024
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(10:00 AM - 01:30 PM)

(Class 3hr - 3:30Hrs) / Per Session

15-Jun-2024
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(09:00 AM - 02:00 PM)

(Class 4:30Hr - 5:00Hrs) / Per Session

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Have Cracked Their Dream Job in Top MNC Companies

Upgrade Your Career With Our Apache Spark Training

  • When it comes to processing and analysing large amounts of data, nothing beats Apache Spark, a robust open-source distributed computing system.
  • Learning the ins and outs of Apache Spark for large data processing and analytics is the goal of every Apache Spark training.
  • Learning Apache Spark course may equip you to use it effectively for data processing, analytics, and machine learning.
  • Using Spark SQL, you will be able to deal with both structured and semi-structured data, run SQL queries, and connect to various data sources such as CSV, Parquet, and Hive.
  • Spark's compatibility with other big data tools like Hadoop, Kafka, and Apache Hive may be covered in class.
  • In-person classes provide students a more traditional educational experience, complete with lectures, labs, and discussions with professors and classmates.
  • Classroom Batch Training
  • One To One Training
  • Online Training
  • Customized Training
  • Enroll Now

Course Objectives

  • Improved skills and expertise
  • Career advancement opportunities
  • Competence in big data processing
  • Enhanced efficiency in data handling
  • Versatility in working with diverse data sources
  • Innovation in data analytics and machine learning
  • Spark Core
  • DataFrames and Datasets
  • Spark Streaming
  • Structured Streaming
  • Spark MLlib

While there are no strict prerequisites to learn Apache Spark, having a strong foundation in certain areas can greatly facilitate the learning process and help you make the most out of your Spark experience.

Apache Spark changed into made so that it may handle and examine huge data units. Spark makes it possible to address and method big amounts of records efficiently, which won't have been possible with different records processing gear. By mastering Spark, you may be capable of coping with very big documents and get beneficial data from them.

Real-world projects allow students to apply the concepts they have learned in a practical setting. It helps solidify their understanding of Spark's features, APIs, and data processing techniques by working on actual data problems.

  • Introduction to Spark
  • Spark SQL
  • Spark DataFrames and Datasets
  • Spark Streaming
  • Structured Streaming
  • Spark Machine Learning (MLlib)
  • Apache Hadoop
  • Apache Hive
  • Apache Kafka
  • Apache Cassandra
  • Apache Mesos
  • Big Data Processing
  • Real-time Analytics
  • Machine Learning and AI
  • Data Science and Data Engineering
  • Cloud Adoption
  • Industry Adoption

Is Apache a good career in India?

Apache Spark is an outstanding way to get activity in India because huge information processing and analytics talents are in excessive demand. Apache Spark is in excessive call for inside the business international because it is a powerful device for handling huge amounts of facts.

  • Technology and IT Services
  • Finance and Banking
  • E-commerce and Retail
  • Healthcare and Life Sciences
  • Telecommunications

What kind of learning materials are provided in the course?

You may expect to find a lot of study resources for Apache Spark along the way that are intended to give you a thorough grasp of the framework and its components. These study resources are often chosen with certain learning styles and comprehension levels in mind.

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An Complete Overview of Apache Spark

Apache Spark is a robust open-source framework for processing and analysing enormous amounts of data. Structured, semi-structured, and unstructured data may all be processed on the same, streamlined system. One of Spark's greatest advantages is that it can execute data processing in memory, which results in instantaneous processing and analytics. Spark is able to manage enormous datasets across clusters of machines because of its distributed computing approach and fault-tolerant design. The framework is designed to be usable by a large community of developers and data scientists, hence it provides a comprehensive set of application programming interfaces (APIs) in many languages. Spark's adaptability goes beyond its batch processing roots, as it also supports streaming in real time, machine learning, and graph processing.

 

Additional Info

Future Developments in Apache Spark:

Apache Spark keeps changing and getting better to keep up with the changing needs of the big data processing and analytics environment. Here are some things to look out for in Apache Spark in the coming years:

  • Improved Streaming: Spark's streaming features, such as Spark Streaming and Structured Streaming, are expected to get better. This could mean making changes to how complicated event processing is handled, adding support for more sources of streaming data, and integrating with new streaming technologies.
  • Deeper Integration with AI and Machine Learning: Spark's machine learning library, MLlib, is expected to get new methods, better scaling, and support with other famous machine learning frameworks. This means that Spark will be able to play a bigger part in AI and machine learning apps.
  • Better Performance and Optimization: Spark's performance and optimization are anticipated to improve. This might include enhancing Spark's query optimization, memory management, data movement strategies, and effective execution plans to help it operate more quickly and efficiently.
  • Advanced Graph Processing: GraphX, Spark's graph processing tool, might benefit from improved graph techniques, enhancements, and scalability. As a result, it will be feasible to handle larger-scale graph processing and do more intricate graph analytics.
  • Extended Support for Data Formats and Connectors: It should be simpler to connect to a variety of data sources and systems thanks to Spark's increased support for various data types and connections. Improved compatibility with widely used databases, data repositories, and cloud storage services are all part of this.
  • Integration with Data Science Tools: Spark is anticipated to become better at integrating with well-known data science tools and frameworks like Jupyter Notebooks, TensorFlow, and PyTorch. As a result, it will be simple for Spark's data processing tools and data science procedures to cooperate and fit well together.
  • Improvements to Cluster Management and Deployment: Spark's cluster management and deployment capabilities may one day be enhanced with greater scalability, fault tolerance, and resource management, among other things. As a result, cluster operations will be simpler and Spark will be better able to manage large-scale deployments.

Career Scope for Apache Spark:

Many different businesses that deal with big data processing, real-time analytics, and machine learning provide job prospects for Apache Spark specialists. Professionals with expertise in Apache Spark are in great demand because of the exponential expansion of data and the growing need for data-driven insights. They may pursue careers as data scientists, engineers, architects working with big data, developers working with Spark, and administrators working with Spark.

These experts are in charge of creating machine learning models, enhancing Spark performance, and integrating Spark with other big data ecosystem components. They are also in charge of building and implementing pipelines for data processing that use Spark. Professionals skilled in Apache Spark may find work possibilities in industries including technology, banking, e-commerce, healthcare, telecommunications, and more since Spark is a widely used technology.

Moreover, the career scope for Apache Spark professionals extends beyond job roles. Skilled individuals can explore opportunities as consultants, trainers, and freelancers, offering their expertise in Spark to organisations seeking assistance with big data projects. Additionally, Apache Spark professionals can contribute to the open-source community, collaborate on Spark-related projects, and enhance their reputation and visibility in the field.

The continuous growth and advancements in Spark, coupled with the ever-increasing demand for data processing and analytics skills, ensure a promising career scope for Apache Spark professionals, with ample opportunities for career growth, skill development, and making a significant impact in the world of big data.


The Importance of Training in Apache Spark:

Training in Apache Spark is of significant importance for individuals and organisations seeking to leverage the full potential of this powerful big data processing framework. Here are some key reasons highlighting the importance of training in Apache Spark:

Maximising Efficiency and Performance:

Proper training ensures that individuals understand the core concepts, best practices, and optimization techniques of Apache Spark. By acquiring the necessary skills, trainees can effectively utilise Spark's features, APIs, and optimizations to maximise the efficiency and performance of their data processing workflows.

Building a Strong Foundation:

Training provides a solid foundation in Apache Spark, covering essential topics such as Spark Core, Spark SQL, Spark Streaming, and machine learning with MLlib. It equips learners with the necessary knowledge to work with Spark's components and understand how they fit into the overall Spark ecosystem.

Real-world Application and Use Cases:

Practical examples, hands-on activities, and real-world use cases are commonplace in training, preparing students to apply what they've learned in realistic contexts. This practical experience helps individuals understand how to solve common data processing challenges and reinforces their understanding of Spark's capabilities.

Efficient Problem Solving:

Training equips learners with the skills to tackle complex data processing problems efficiently. They gain the ability to identify bottlenecks, optimise Spark jobs, debug issues, and leverage Spark's performance tuning techniques. This empowers them to build efficient data processing pipelines and overcome challenges in real-world scenarios.

Enabling Collaboration and Teamwork:Training programs often include collaborative activities, such as group projects or team exercises. These activities promote teamwork and collaboration, allowing trainees to learn from each other, exchange ideas, and work together on Spark-related tasks. This reflects real-world scenarios where collaboration is key to successful data processing projects.

Staying Updated with Latest Features and Best Practices:Apache Spark is a rapidly evolving framework with new features, enhancements, and best practices being introduced regularly. Training programs help learners stay updated with the latest advancements in Spark, ensuring they are equipped with the most relevant knowledge and skills in their field.

Meeting Industry Demands: The demand for professionals with Apache Spark skills is increasing across various industries. By receiving training in Apache Spark, individuals can meet the industry demands and position themselves for exciting job opportunities in data engineering, data science, big data analytics, and related fields.


Tools used for Apache Spark:

  • Apache Hadoop is one of Apache Spark's most used supporting tools. Spark may be used with the popular distributed storage and processing framework Apache Hadoop.
  • Spark is compatible with the Hadoop ecosystem, which includes Hadoop MapReduce and Hadoop YARN for managing cluster resources, and may use Hadoop's distributed file system (HDFS) for data storage.
  • Together, Hadoop and Spark provide a formidable tool for big data analytics thanks to Hadoop's dependable and scalable data storage and processing infrastructure and Spark's lightning-fast and energy-efficient in-memory processing.
  • Apache Kafka is another popular component of the Spark ecosystem. Data may be streamed with high speed and reliability using Apache Kafka, a distributed streaming platform.
  • Data from Kafka topics may be consumed by Spark, allowing for streaming processing in real time and further integration with other data sources.
  • Spark's distributed and fault-tolerant processing of streaming data is made possible by Kafka, a scalable and dependable data intake and messaging system.
  • Real-time analytics, fraud detection, and event-driven applications are just some of the use cases made possible by the combination of Apache Spark and Kafka.
  • In addition to enhancing Apache Spark's functionality, these technologies allow it to integrate with a wider variety of data stores, streaming platforms, analytics tools, and visualisation frameworks.
  • Organisations may create scalable and reliable big data processing and analytics solutions that meet their unique requirements by combining these technologies with Apache Spark.
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Key Features

ACTE Hyderabad 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 Course in Hyderabad
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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Need customized curriculum?

Get Impactful Real Time Apache Spark Projects

Project 1
Real-time Data Streaming and Analysis

Create a real-time data pipeline using Apache Spark Streaming or Structured Streaming to collect data from IoT devices, social media, and site logs for analysis.

Project 2
Twitter Sentiment Analysis

Utilize the Twitter API for real-time sentiment analysis of current tweets, creating a dashboard that updates with the latest data and uses graphs to display trends.

Access Our Apache Spark Job Opportunities

Every student or professional who successfully completes our classroom or online training will also receive opportunities for apache spark job placement.

  • You will learn how to make mobile apps, web apps, and more by using the most up-to-date tools and methods used in the industry.
  • We help Apache Spark developers find jobs by putting them in touch with top companies in their field and helping them prepare for interviews and write resumes.
  • We give our students good chances of getting jobs with big companies like Wipro, Accenture, CTS, Siemens, Dell, and others.
  • The internship program bridges the gap between what students learn in school and what they learn on the job by giving them the skills and direction they need to do well in their chosen field.
  • After they have finished 75% of the Apache Spark training course, we will set up phone interviews and prepare them for face-to-face meetings with possible MNCs.
  • Our expert placement team works directly with students to guide them through the interview process and help them build skills that will help them get jobs.

Obtain Our Resourceful Apache Spark Certification

Our ACTE Apache Spark certification is well respected across the world. Having this certification on your CV will make you more marketable to the Fortune 500 companies across the globe. The ACTE Certification is recognized by all major worldwide companies. Earning an Apache Spark certification is a great way to demonstrate your mastery of the framework's many tools, techniques, and best practices. Professionals' confidence in their Apache Spark knowledge and skills is strengthened by these certifications.

  • Databricks Certified Associate Developer for Apache Spark
  • Databricks Certified Professional Data Scientist for Apache Spark
  • Databricks Certified Professional for Apache Spark
  • Cloudera Certified Spark and Hadoop Developer (CCA175)
  • IBM Certified Data Engineer - Big Data
  • Enhanced skills and expertise.
  • Career advancement opportunities.
  • Competitive edge in the job market.
  • Validation of Spark proficiency.
  • Access to specialized roles and projects.

Apache Spark certification can substantially increase your likelihood of securing a job in data-related roles. Your certification bolsters your qualifications and enhances your appeal to potential employers.

  • Big Data Engineer
  • Data Scientist
  • Spark Developer
  • Data Engineer
  • Data Analyst
  • Machine Learning Engineer
  • Programming Languages
  • Spark Components and Libraries
  • Data Processing and Analytics
  • Data Engineering Concepts
  • Cluster Configuration and Performance Tuning

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

Train From Professional Apache Spark Experts

  • Our Apache Spark experts are good at breaking down intimidating ideas in Apache Spark so that their pupils may understand them.
  • Because of their extensive familiarity with Apache Spark, they can provide students both theoretical and hands-on guidance.
  • Our experts in Apache Spark Course in Hyderabad keep up with the newest research in their field so they can teach their students the most effective practices.
  • Because of their in-depth familiarity with Apache Spark, they are able to provide students both theoretical and hands-on guidance.
  • Students may rely on our devoted placement staff for assistance with their resumes, interviews, and introductions to top-tier businesses.
  • Throughout the course of the training, the Apache Spark instructor will show each participant specific examples of tasks related to the role.
  • High placement rates are evidence of the quality of our training program and our commitment to meeting industry demands.

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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