Big data Course Syllabus
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Learn Big Data technologies at SLA Institute, the leading institute for the Big Data Syllabus. Our syllabus covers essential topics to build a strong foundation in Big Data analytics, Hadoop, Spark, and data processing frameworks. Explore key concepts such as data ingestion, storage, real-time processing, data lakes, and machine learning integration. Gain hands-on experience through real-world case studies and industry-relevant projects. SLA Institute provides expert training and career support to help you excel in Big Data engineering, analytics, and cloud-based data solutions. Download our Big Data Syllabus PDF for a detailed course structure and topics. Join our Big Data Certification Course with 100% Placement Support and take the first step toward a successful career in Big Data and analytics. Start your journey with SLA Institute today!
Course Syllabus
Download SyllabusModule 1: Introduction to Big Data
- Understanding the importance and evolution of Big Data
- Big Data vs Traditional Data Processing
- Key challenges in Big Data Storage and Processing
- Real-world applications and use cases
- Overview of Big Data Technologies & Frameworks
Module 2: Hadoop and HDFS (Hadoop Distributed File System)
- Introduction to Hadoop Architecture
- Installation & configuration of Hadoop Cluster
- Understanding HDFS and its components
- File operations in HDFS
- Hands-on practice with Hadoop CLI
Module 3: MapReduce Programming & Data Processing
- Introduction to MapReduce framework
- Writing MapReduce programs using Java & Python
- Implementing data transformations with MapReduce
- Debugging and optimizing MapReduce Jobs
- Working with YARN (Yet Another Resource Negotiator)
Module 4: Apache Spark for Big Data Processing
- Understanding Apache Spark Architecture
- Spark Core and RDD (Resilient Distributed Datasets)
- DataFrames & Spark SQL for data analysis
- Spark Streaming for real-time data processing
- Hands-on with PySpark and Scala programming
Module 5: NoSQL Databases & Big Data Storage
- Introduction to NoSQL databases
- Working with MongoDB, HBase, Cassandra
- NoSQL vs SQL: Key Differences
- Data Modeling and Indexing in NoSQL
- Querying NoSQL databases using Hive and Impala
Module 6: Data Ingestion, ETL, and Workflow Automation
- Understanding ETL (Extract, Transform, Load) processes
- Data ingestion with Apache Sqoop & Apache Flume
- Automating workflows using Apache Oozie & Airflow
- Real-world ETL pipeline implementation
Module 7: Real-time Data Processing & Streaming
- Introduction to Apache Kafka
- Implementing real-time streaming with Apache Flink
- Spark Streaming vs Flink vs Storm
- Processing real-time IoT and Social Media data
Module 8: Big Data on Cloud (AWS, Azure, GCP)
- Cloud computing fundamentals for Big Data
- Working with AWS EMR, Azure HDInsight, and Google BigQuery
- Deploying Hadoop and Spark on Cloud platforms
- Data processing with Google Dataflow & AWS Glue
Module 9: Data Visualization and Reporting
- Introduction to Data Visualization for Big Data
- Working with Tableau, Power BI, and Kibana
- Creating interactive dashboards and reports
- Generating insights from Big Data Analytics
Module 10: Machine Learning & AI with Big Data
- Introduction to Machine Learning in Big Data
- Implementing ML algorithms using Spark MLlib
- Hands-on with predictive analytics & sentiment analysis
- Real-world case studies in AI & Big Data
Module 11: Big Data Security & Governance
- Security challenges in Big Data Architecture
- Implementing Data Encryption & Access Control
- Compliance with GDPR, HIPAA, and other regulations
- Risk management & data privacy best practices
Module 12: Capstone Project & Certification Preparation
- End-to-end Big Data project with real-world datasets
- Designing Big Data pipelines
- Implementing ETL, Data Storage, and Analytics solutions
- Preparing for Big Data Certification exams
- Mock interviews & career guidance
In conclusion, our Big Data Syllabus equips learners with the essential skills to master Big Data technologies and analytics. The course covers key topics such as Hadoop, Apache Spark, NoSQL Databases, Data Streaming, and Cloud-based Big Data Processing, offering hands-on experience through real-world projects and case studies. With a structured curriculum, students will gain proficiency in data ingestion, ETL processing, machine learning with Big Data, and real-time analytics. This Big Data Training is designed to prepare learners for careers in Big Data Engineering, Data Science, and Cloud Data Analytics. Start your journey today with SLA Institute and become an expert in Big Data Analytics and Processing!
