Apache Spark Training in OMR

  • Comprehensive Spark Training Aimed at Big Data Applications.
  • More Than 15 Years of Experience in Apache Spark Coaching in OMR.
  • Access to Recorded Lectures, Live Sessions, and Unique Spark Projects.
  • Trusted by More Than 400 Hiring Partners and Over 15,000 Professionals.
  • Tailored Apache Spark Training With Interview Preparation and Practical.
Hands On   40+ Hrs
Projects   4 +
Placement Support   Lifetime Access
3K+

    Course Fees on Month ₹8999 ₹18000
    (Lowest price in chennai)

    See why over 25,000+ Students choose ACTE

    Curriculum of Apache Spark Training in OMR

    Curriculum Designed By Experts

    Expertly designed curriculum for future-ready professionals.

    Industry Oriented Curriculum

    An exhaustive curriculum designed by our industry experts which will help you to get placed in your dream IT company

    •  
      30+  Case Studies & Projects
    •  
      9+  Engaging Projects
    •  
      10+   Years Of Experience
  • Overview of Apache Spark and its ecosystem.
  • Comparison with Hadoop MapReduce.
  • Spark Core, SQL, Streaming, MLlib, GraphX.
  • Use cases of Spark in big data processing.
  • Agenda
  • Understanding Spark architecture.
  • Role of Resilient Distributed Datasets in Spark.
  • Transformations (map, filter, flatMap) and Actions (collect, reduce).
  • Lazy evaluation and DAG execution model.
  • Develop Project Charter
  • Introduction to DataFrames and Datasets.
  • Schema inference and working with structured data.
  • Querying data using Spark SQL.
  • Performance optimizations with Catalyst Optimizer.
  • Sequence Activities
  • Basics of real-time data processing.
  • Structured Streaming vs. DStream API.
  • Integrating Spark Streaming with Kafka, Flume, and HDFS.
  • Window operations and fault tolerance in streaming.
  • Overview of Spark MLlib and ML pipelines.
  • Feature engineering and transformation functions.
  • Supervised and unsupervised learning algorithms.
  • Hyperparameter tuning and model evaluation.
  • Plan Quality
  • Introduction to GraphX for large-scale graph analytics.
  • Graph representation and operations in Spark.
  • PageRank algorithm and social network analysis.
  • Use cases in recommendation systems and fraud detection.
  • Memory management and garbage collection.
  • Partitioning and parallelism strategies.
  • Caching and persistence techniques.
  • Performance tuning using Spark UI and job monitoring.
  • Communication Methods
  • Running Spark on standalone, YARN, Mesos.
  • Submitting applications using spark-submit.
  • Managing long-running Spark jobs.
  • Integration with cloud platforms
  • Show More

    Apache Spark Training Projects

    Become a Apache Spark Expert With Practical and Engaging Projects.

    •  
      Practice essential Tools
    •  
      Designed by Industry experts
    •  
      Get Real-world Experience

    Word Count Application

    Implement a simple Spark application to count word frequency in a text file.Learn Spark RDDs, transformations, and actions.

    Log File Analysis

    Process and analyze web server logs to extract useful insights.Gain experience with data ingestion, filtering.

    CSV File Processing

    Load, clean, and process CSV files using Spark DataFrames.Explore Spark SQL for querying structured data.

    Real-Time Twitter Sentiment Analysis

    Use Spark Streaming to analyze live tweets and determine sentiment.Work with APIs, Kafka, and Spark Streaming.

    E-commerce Recommendation System

    Build a collaborative filtering model for product recommendations.Utilize Spark MLlib for user-based recommendations.

    IoT Sensor Data Processing

    Process large-scale IoT sensor data for anomaly detection.Work with structured and unstructured data streams.

    Real-Time Fraud Detection in Financial Transactions

    Use Spark Streaming and ML models to detect fraudulent transactions . Implement anomaly detection algorithms with Kafka integration.

    Distributed Image Processing with Apache Spark

    Process large-scale images for pattern recognition using Spark.Work with deep learning frameworks integrated with Spark.

    Big Data Pipeline for Healthcare Analytics

    Build a Spark-based pipeline for analyzing electronic health records.Work with structured and semi-structured data formats.

    Key Features

    Practical Training

    Global Certifications

    Flexible Timing

    Trainer Support

    Study Material

    Placement Support

    Mock Interviews

    Resume Building

    Batch Schedule

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

    • 05-May-2025 Starts Coming Monday ( Monday - Friday) 08:00 AM (IST)
    • 07-May-2025 Starts Coming Wednesday ( Monday - Friday) 10:00 AM (IST)

    Weekend Regular (Class 3Hrs) / Per Session

    • 10-May-2025 Starts Coming Saturday ( Saturday - Sunday) 10:00 AM (IST)

    Weekend Fast-track (Class 6Hrs - 7Hrs) / Per Session

    • 11-May-2025 Starts Coming Saturday ( Saturday - Sunday) 10:00 AM (IST)

    Enquiry Form

      Top Placement Company is Now Hiring You!
      • Learning strategies that are appropriate and tailored to your company's requirements.
      • Live projects guided by instructors are a characteristic of the virtual learning environment.
      • The curriculum includes of full-day lectures, practical exercises, and case studies.

      Apache Spark Training Overview

      Exciting Career Opportunities with Apache Spark Training in OMR

      Apache Spark Training Center in OMR offers access to various rewarding career possibilities in the field of big data and analytics. As a Spark developer, you have the opportunity to work alongside top technology companies, research institutions, and financial organizations that handle large datasets. Possible career paths encompass roles such as Big Data Engineer, Data Scientist, Machine Learning Engineer, Cloud Data Engineer, and Data Architect. Each of these positions requires a solid grasp of distributed computing and the ability to effectively process and analyze vast amounts of data. Big data engineers implement Spark to design scalable data pipelines and improve performance for both batch and real-time data processing. Data scientists use Spark’s MLlib to create predictive models that enhance decision-making. In the domain of cloud computing, Spark is combined with services like AWS EMR, Google Cloud Dataproc, and Azure HDInsight, further solidifying cloud data engineering as a valuable career choice.

      Exciting Career Opportunities with Apache Spark Course Class in OMR

      • Basic Programming Knowledge: Familiarity with Python, Scala, or Java is essential, as Spark applications are written in these languages. Understanding object-oriented programming (OOP) concepts can be beneficial.
      • Fundamentals of Big Data: Basic knowledge of big data concepts, including Hadoop, MapReduce, and distributed computing, is useful. Awareness of cloud-based big data solutions such as AWS, Azure, and Google Cloud is an advantage.
      • SQL and Database Concepts: Experience with SQL and relational databases helps in understanding Spark SQL operations. Exposure to NoSQL databases like Cassandra or HBase can be beneficial.
      • Linux and Command-Line Usage: Familiarity with Linux commands and basic shell scripting aids in running Spark on clusters. Understanding distributed file systems like HDFS (Hadoop Distributed File System) is an added advantage.
      • Mathematics and Data Analytics: A basic understanding of statistics, probability, and machine learning concepts helps in working with Spark MLlib. Familiarity with data visualization and analytical tools is a plus.
      • Experience with Distributed Systems: Knowledge of parallel computing and distributed systems enhances learning efficiency. Working experience with Kafka, Flink, or Hadoop provides a competitive edge.

      The Importance of Data Professionals for Apache Spark Training in OMR

      Apache Spark Placement Training in OMR has transformed how organizations handle large-scale data, making it a crucial competency for data professionals. For individuals aiming to advance their careers, boost their earning potential, or attain expertise in advanced data technologies, training in Spark is an advantageous choice. The demand for big data professionals is increasing, as businesses need individuals skilled in real-time analytics, machine learning, and cloud-based big data solutions. One major advantage of becoming proficient in Spark is its versatility; it supports multiple programming languages (such as Python, Scala, Java, and R), integrates seamlessly with Hadoop, Kafka, and cloud services, and provides capabilities for both batch and streaming data processing. Additionally, Spark is recognized for its speed, scalability, and widespread use by leading companies like Netflix, Uber, and Facebook.

      Techniques and Trends in Apache Spark Development Training in OMR

      • Real-Time Data Processing with Structured Streaming: Organizations are moving towards real-time analytics, leveraging Spark’s structured streaming for event-driven applications. Industries like finance and e-commerce use Spark to analyze stock trends, fraud detection, and customer interactions.
      • Machine Learning and AI Integration: Spark MLlib is enhancing AI-driven applications with distributed machine learning models. Companies are using deep learning frameworks (TensorFlow, PyTorch) with Spark for large-scale training.
      • Spark on Kubernetes for Cloud-Native Big Data: Spark is increasingly deployed on Kubernetes clusters for scalable and containerized big data processing. Hybrid cloud solutions integrate Spark with AWS, Azure, and Google Cloud for cost-efficient computing.
      • Adaptive Query Execution (AQE) for Performance Optimization: AQE in Spark 3.x optimizes query execution dynamically based on runtime statistics, improving efficiency. Dynamic partition pruning and auto-tuning are reducing computational overhead.
      • Integration with Lakehouse Architecture: Apache Spark is a key player in modern Lakehouse architectures (e.g., Databricks Delta Lake, Apache Iceberg).

      New Tools and Frameworks for Apache Spark Program in OMR

      The Apache Spark Certification Course in OMR is constantly evolving with the introduction of new tools and frameworks that improve its big data processing capabilities. Among the latest and most widely adopted tools is Delta Lake, an open-source storage layer that brings ACID transactions to Spark, thus ensuring data lakes uphold reliability and consistency. Delta Lake is gaining traction in the domains of data engineering and machine learning, making it a vital tool for professionals utilizing Spark. Another significant tool is MLflow, which simplifies the lifecycle management of machine learning models within Spark. It allows for effective experiment tracking, model versioning, and deployment, thereby making the workflow easier for data scientists working in the Spark ecosystem. Additionally, Koalas acts as a link between Pandas and PySpark, enabling Python users to seamlessly interact with Spark DataFrames.

      Add-Ons Info

      Career Opportunities  After Apache Spark

      Big Data Engineer

      A Big Data Engineer is responsible for designing, developing, and maintaining big data pipelines using Apache Spark. They work with structured and unstructured data, integrating Spark with technologies like Hadoop, Kafka, and cloud services (AWS, Azure, Google Cloud).

      Data Scientist

      A Data Scientist with Apache Spark expertise applies machine learning and statistical techniques to extract insights from massive datasets. They use Spark MLlib, Python (PySpark), or Scala to develop predictive models and run distributed computations efficiently.

      Spark Developer

      A Spark Developer specializes in building data-intensive applications using Apache Spark. They design and implement batch processing, real-time streaming, and ETL (Extract, Transform, Load) workflows. These professionals write optimized Spark jobs in Scala, Java, or Python

      Machine Learning Engineer

      A Machine Learning Engineer utilizes Apache Spark for training and deploying large-scale ML models. They leverage Spark MLlib, TensorFlow, and deep learning frameworks to handle complex datasets. This role requires expertise in parallel computing, feature selection, and hyperparameter tuning.

      Cloud Data Engineer

      A Cloud Data Engineer focuses on deploying Apache Spark in cloud-based ecosystems, such as AWS EMR, Google Cloud Dataproc, and Azure HDInsight. They design and manage serverless big data architectures, optimizing Spark jobs for cloud scalability.

      Data Architect

      A Data Architect designs high-level data frameworks, ensuring scalability, efficiency, and security in big data systems. They define data modeling strategies, data governance policies, and system integration approaches using Spark and related technologies Data Architects .


      Skill to Master
      Mastering Spark Core API
      Expertise in Spark SQL
      Real-Time Data Processing with Spark Streaming
      Building ETL Pipelines with Apache Spark
      Hands-on Experience with Spark MLlib
      Data Partitioning and Performance Optimization
      Distributed Computing and Parallel Processing
      Integration with Hadoop and Big Data Ecosystems
      Deploying Spark Applications on Cloud Platforms
      Writing Efficient Spark Applications in Python, Scala, and Java
      Working with Graph Processing Using GraphX
      Implementing Apache Spark in Data Lake and Lakehouse Architectures
      Show More

      Tools to Master
      Apache Hadoop
      Apache Kafka
      Apache Hive
      Delta Lake
      MLflow
      Apache Airflow
      Google Cloud Dataproc
      Apache Flink
      Apache Cassandra
      Jupyter Notebook with PySpark
      GraphFrames & GraphX
      Kubernetes for Spark
      Show More
      Our Instructor

      Learn from certified professionals who are currently working.

      instructor
      Training by

      Shreya, having 7 yrs of experience

      Specialized in: Apache Spark Development, Real-time Data Processing, Big Data Pipeline Optimization, and Cloud-based Spark Deployments.

      Note: Shreya is known for his expertise in distributed computing and cloud integrations, helping organizations optimize Spark performance on AWS, Azure, and Google Cloud.

      Job Assistant Program

      We are proud to have participated in more than 40,000 career transfers globally.

      Apache Spark Certification

      Certificate
      GET A SAMPLE CERTIFICATE

      Earning an Apache Spark Training certification validates your expertise in big data processing, real-time analytics, and machine learning using Apache Spark. As businesses increasingly rely on big data technologies, certified professionals stand out in the competitive job market.

    • Industry Recognition
    • Better Job Opportunities
    • Higher Salary Potential
    • Hands-on Learning Experience
    • Increases job prospects
    • Enhances skill validation
    • A strong portfolio is key
    • There are no strict prerequisites, but basic knowledge of programming (Python, Scala, or Java) and SQL is beneficial. Familiarity with big data frameworks like Hadoop, Spark, and cloud platforms (AWS, Azure, GCP) can also help in understanding advanced concepts.

    • Understand Exam Objectives
    • Hands-on Practice
    • Take Mock Tests
    • Enroll in a Training Program
    • Yes, most Apache Spark certification exams are available online.
    • You can take them from home with remote proctoring.
    • Registration is done through official certification providers like Databricks or Cloudera.
    • Ensure a stable internet connection and meet system requirements before attempting the exam.
    • While real-world experience is not mandatory for most Apache Spark certifications, it can significantly boost your understanding of the subject. Many entry-level certifications focus on foundational Spark concepts, making them accessible to beginners. However, higher-level certifications may require prior hands-on experience with big data projects, cloud deployments, and performance tuning.

      Yes! ACTE’s Apache Spark Training Certification is a valuable investment for anyone looking to build expertise in big data processing, machine learning, and cloud-based analytics. The certification program provides structured training, hands-on projects, real-world case studies, and mentorship from industry experts.

      Show More

      Frequently Asked Questions

      • Yes, ACTE offers free demo sessions – You can attend a trial class to understand the course structure, teaching methodology, and instructor expertise.
      • Experience live training – The demo session provides insights into course content, hands-on exercises, and real-world project exposure before enrollment.
      • ACTE instructors are industry experts with extensive experience in Apache Spark, Big Data, and Cloud Computing. They have worked with leading tech companies and possess practical knowledge of real-world applications. Instructors specialize in Apache Spark development, data engineering, machine learning, and performance optimization, ensuring that students receive top-tier training.
      • Yes! ACTE provides dedicated placement support to help students land jobs in big data engineering, data science, and cloud computing. Placement assistance includes resume building, mock interviews, job referrals, and interview coaching with industry experts.
      • ACTE Apache Spark Training Certification
      • Preparation for industry certifications
      • Yes! The course includes real-world projects that help students gain hands-on experience with Apache Spark. You will work on batch processing, real-time streaming, machine learning models, and cloud-based Spark deployments. These projects simulate industry use cases, preparing you for real-world job scenarios and boosting your confidence in handling big data challenges.

      STILL GOT QUERIES?

      Get a Live FREE Demo

      • Flexibility: Online, weekends & more.
      • Hands-on: Projects & practical exercises.
      • Placement support: Resume & interview help.
      • Lifelong learning: Valuable & adaptable skills.
      • Full curriculum: Foundational & advanced concepts.

        Enquiry Now