Big data Engineering Course

Big data Engineering refers to the process of designing, building, and maintaining large-scale data processing systems that can handle massive volumes of data in various formats, such as structured, semi-structured, and unstructured data. It involves the development and deployment of tools and technologies that can store, manage, process, and analyze big data, to derive insights and drive decision-making.

The field of big data engineering is complex and constantly evolving, with a range of technologies and platforms available to handle different types and sizes of data. Big data engineers must have a strong foundation in computer science, software engineering, and database management, as well as an understanding of data analysis, statistics, and machine learning.

Benefits Of Big data Engineering Course

  • High Demand for Big Data Engineers: The demand for skilled big data engineers is consistently high as organizations across various industries seek professionals who can manage and process large volumes of data.
  • Handling and Processing Large Data Sets: Big data engineering equips you with the skills to handle and process massive amounts of data efficiently.
  • Developing Data Pipelines and ETL Processes: Big data engineering involves designing and developing data pipelines and Extract, Transform, Load (ETL) processes. These pipelines enable the efficient movement of data from various sources, perform necessary transformations, and load it into data storage systems for further analysis.
  • Scaling and Performance Optimization: With big data engineering, you learn techniques to scale data processing systems horizontally and vertically.
  • Integration with Big Data Technologies: Big data engineering involves working with a range of big data technologies and frameworks such as Apache Hadoop, Spark, Kafka, and NoSQL databases.

Course Content

Introduction to Big Data and Distributed Computing
  • Overview of Big Data
  • Distributed Computing Paradigms: Hadoop, Spark
  • Introduction to Hadoop Distributed File System (HDFS)
  • Setting up a Hadoop Cluster
  • Getting Started with Big Data and Understanding HDFS Concept along with Linux Commands
  • Hadoop MapReduce
  • MapReduce Basics
  • MapReduce - Distributed Computing Framework
  • Hadoop MapReduce Framework
  • Advanced MapReduce Concepts
  • Introduction to Spark
  • Basic of scala and python
  • Spark Architecture and Components
  • Resilient Distributed Datasets (RDDs)
  • Spark SQL and DataFrames and Dataset
  • Spark Streaming and Structured Streaming
  • Spark API with pyspark and scala
  • Apache spark optimization and streaming
  • Introduction to Hive Databases
  • Key-Value Stores: Redis, Riak, DynamoDB
  • Document Databases: MongoDB
  • Column Family Stores: HBase, Cassandra
  • Introduction to Apache Kafka
  • Apache Kafka - Distributed Event Streaming Platform
  • Kafka Architecture and Components
  • Kafka Core APIs: Producer and Consumer
  • Kafka Connect
  • Kafka Streams
  • Apache Sqoop - Moving Data into Hadoop
  • Cloud Computing Overview
  • Big Data on AWS: EMR, S3, Redshift
  • Big Data on Google Cloud: Dataproc, Bigtable, BigQuery
  • Big Data on Azure: HDInsight, Blob Storage, SQL Data Warehouse
  • Introduction to PySpark
  • PySpark RDDs, DataFrames, and Datasets
  • PySpark SQL
  • PySpark Streaming and Structured Streaming
  • Big Data Industry grade project.
  • Big Data Analytics in Credit card fraud detection.
  • Big Data Analytics in Healthcare
  • Big Data Analytics in Social Media
  • Big Data Analytics in Transportation