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Big Data Champion

Empowering the Future with Big Data | Transform Insights into Action

  • Global Leader in Big Data Training for Aspiring Professionals
  • Earn 50 Learning Hours | 15 Real-world Projects | 1500+ Big Data Questions
  • Master Hadoop, Spark, and Real-time Data Processing Techniques
  • Comprehensive Cheat Sheets | Personalized Study Plans
  • Guaranteed to Run 25+ Live Cohorts in the Next 90 Days
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Course Overview

Transform into a Big Data expert with eVani's comprehensive program. Master core concepts, tools, and technologies like Hadoop, Spark, Scala, and PySpark. Gain hands-on experience through real-world projects. Build scalable data pipelines and perform complex data analysis. Become proficient in data warehousing, mining, and machine learning. Acquire in-demand skills for high-paying Big Data roles. Join us to unlock your potential in the data-driven world.

Course Description

The Big Data Champion Program is a comprehensive course designed to provide professionals with the necessary skills to excel in Big Data engineering. The program emphasizes core competencies such as Apache Spark, Scala, and PySpark, blending theory with hands-on practice. Participants will develop expertise in managing large datasets, conducting complex data processing, and building scalable pipelines.

Course Outcomes

Understand the fundamentals of Big Data and its applications.
Learn about various Big Data technologies and architectures.
Develop skills in data warehousing and data mining.
Gain proficiency in big data analytics tools and techniques.

Pre-requisite

Basic knowledge of programming (Python or Java) and SQL.
Familiarity with general IT concepts and practices.

Target Audience

IT professionals seeking to expand their Big Data cloud skills.
Anyone looking to enhance their career prospects with Big Data expertise.
Aspiring data analysts, data engineers, software developers, and professionals looking to enter the Big Data domain.

This course includes:

  • Duration 100 hours

  • Lectures 106 lessons

  • Certificate of Completion

  • Full lifetime access

Upcoming Cohort Class

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Course Curriculum

  • 1 Introduction to Scala
  • 2 Core language features: variables, data types, operators
  • 3 Control flow statements (if-else, loops).
  • 4 Functions and higher-order functions.
  • 5 Object-oriented programming concepts (classes, objects, inheritance, polymorphism)
  • 6 Collections (Lists, Maps, Sets, Tuples)
  • 1 Python programming basics: data types, control flow, functions, object-oriented programming.
  • 2 Introduction to Apache Spark and its architecture.
  • 3 PySpark environment setup and configuration
  • 4 Core PySpark operations: transformations and actions.
  • 1 Understanding Spark performance metrics.
  • 2 Identifying performance bottlenecks.
  • 3 Caching and persistence optimization.
  • 4 Resource allocation and configuration.
  • 1 Introduction to GraphX
  • 2 Graph representation and operations
  • 3 Graph algorithms.
  • 4 Connected components.
  • 5 Triangle count.
  • 1 Introduction to Spark MLlib
  • 2 MLlib pipeline
  • 3 Classification and regression algorithms.
  • 4 Collaborative filtering
  • 5 Model evaluation and tuning.
  • 1 Introduction to Spark Streaming
  • 2 Discretized Streams (DStreams)
  • 3 Input and output sources
  • 4 Transformations and output operations
  • 5 State management.
  • 1 Introduction to Spark SQL.
  • 2 DataFrames and Datasets.
  • 3 SQL-like operations on DataFrames
  • 4 Creating DataFrames from various sources.
  • 5 schema manipulation
  • 1 Introduction to Apache Spark
  • 2 Spark architecture and components.
  • 3 Resilient Distributed Datasets (RDDs).
  • 4 Transformations and actions.
  • 5 Caching and persistence
  • 6 Shared variables (broadcast variables, accumulators).
  • 1 What is Big Data?
  • 2 Characteristics of Big Data (Volume, Velocity, Variety, Veracity)
  • 3 Categories of Big Data.
  • 4 Technologies of Big Data
  • 5 Challenges of traditional data processing systems
  • 1 Oozie architecture and components.
  • 2 Creating and executing workflows.
  • 3 Coordinating MapReduce, Hive, Pig, and Sqoop jobs.
  • 4 Error handling and retries.
  • 5 Execution of Apache Airflow
  • 1 importing data from relational databases to HDFS
  • 2 Exporting data from HDFS to relational databases
  • 3 Incremental loads and full loads
  • 1 NoSQL databases vs. relational databases
  • 2 HBase architecture.
  • 3 HBase data model (row, column family, column, qualifier, value).
  • 1 Introduction to HiveQL.
  • 2 Creating and managing tables.
  • 3 Data definition language (DDL) and data manipulation language (DML).
  • 4 Hive optimization techniques.
  • 1 Pig Latin scripting language
  • 2 Load, transform, and store data.
  • 3 Using UDFs and custom functions.
  • 1 What is MapReduce?
  • 2 Traditional & MapReduce way of data processing.
  • 3 MapReduce Components.
  • 4 How does MapReduce work ?
  • 1 What is Hadoop
  • 2 Why Hadoop
  • 3 Hadoop Ecosystem.
  • 4 Hadoop distributions (Cloudera, Hortonworks, MapR).
  • 5 Hadoop architecture: NameNode, DataNode, Secondary NameNode and components.
  • 6 Data Replication and Fault Tolerance.
  • 7 HDFS Commands.
  • 1 Data Ingestion and Manipulation.
  • 2 Reading data from various sources: CSV, JSON, Parquet, text files, databases.
  • 3 Writing data in different formats
  • 4 Data cleaning and preprocessing: handling missing values, outliers, and inconsistencies.
  • 5 Creating custom data types and user-defined functions (UDFs)
  • 1 Introduction to Spark SQL
  • 2 Creating DataFrames and Datasets
  • 3 SQL-like operations on DataFrames
  • 4 Working with complex data structures.
  • 5 Integrating PySpark SQL with Hive.
  • 1 introduction to machine learning with PySpark
  • 2 Data preparation for machine learning.
  • 3 Feature engineering and selection.
  • 4 Classification algorithms (Logistic Regression, Decision Trees, Random Forest)
  • 5 Regression algorithms (Linear Regression, Decision Trees, Random Forest)
  • 6 Pipeline creation and deployment.
  • 1 Introduction to Spark Streaming
  • 2 Creating DStreams (Discretized Streams).
  • 3 input and output operations.
  • 4 State management and updates
  • 5 Real-time data processing pipelines
  • 1 Building end-to-end data pipelines
  • 2 Orchestration tools (Airflow, Luigi).
  • 3 Data quality and validation
  • 4 Debugging and troubleshooting
  • 1 PySpark GraphX for graph processing.
  • 2 Spark Structured Streaming
  • 3 Distributed deep learning with PySpark.
  • 4 Big data project case studies
  • 1 Project 1
  • 2 Project 2

Why Choose Us?

Excellent Mentors: Industry-experienced instructors with deep expertise in their respective fields.
Hands-on Projects: Practical hands-on projects and assignments that reinforce learning and facilitate skill application.
Customer Success: Dedicated customer support and learning assistance throughout the learning journey.
Lifetime Access: Lifetime access to course materials and updates, ensuring continuous learning and skill enhancement. One-time investment for lifetime access.
Cost-Effective Solutions: Affordable pricing models to fit varying budgets while maintaining high standards of education.

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Learning Path

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