Basic understanding of databases
Familiarity with SQL
Basic programming knowledge (Python)
Understanding of data analytics concepts
Laptop meeting minimum requirements
___ is ___.
This instructor-led, live training (online or onsite) is aimed at beginner-level / intermediate-level / advanced-level ___ who wish to use ___ to ___.
By the end of this training, participants will be able to:
- Install and configure ___.
- ___.
- ___.
- ___.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Day 1: Big Data Fundamentals and Architecture
Morning Session (8:00 AM - 12:00 PM)
Introduction to Big Data
o Understanding the 5 V's of Big Data
o Big Data ecosystem overview
o Business applications and use cases
o Modern data architecture patterns
o Data lakes vs data warehouses
Big Data Technologies Overview
o Distributed computing concepts
o Apache Hadoop ecosystem
o Apache Spark introduction
o NoSQL databases
o Stream processing platforms
o Hands-on Exercise: Setting up a basic big data environment
Afternoon Session (1:30 PM - 5:00 PM)
Data Storage and Processing
o HDFS architecture and concepts
o Data partitioning strategies
o Data format selection (Parquet, ORC, Avro)
o Storage optimization techniques
o Hands-on Exercise: Working with HDFS and data formats
Day 2: Data Processing and Analytics
Morning Session (8:00 AM - 12:00 PM)
Apache Spark Fundamentals
o Spark architecture
o RDD concepts
o DataFrame operations
o SparkSQL basics
o Performance optimization
Big Data ETL
o ETL vs ELT in big data
o Batch processing patterns
o Incremental loading strategies
o Data quality and validation
o Error handling and recovery
Afternoon Session (1:30 PM - 5:00 PM)
Real-time Processing
o Stream processing concepts
o Apache Kafka basics
o Real-time analytics
o Event processing patterns
o Handling late arriving data
o Hands-on Exercise: Real-time data pipeline
Day 3: Big Data Analytics and Implementation
Morning Session (8:00 AM - 12:00 PM)
Analytics with Big Data
o Distributed SQL engines
o Advanced analytics capabilities
o Machine learning with big data
o Visualization techniques
o Performance optimization
o Hands-on Exercise: Analytics implementation
Afternoon Session (1:30 PM - 5:00 PM)
Integration with BI Tools
o Connecting BI tools to big data
o Query optimization
o Caching strategies
o Performance considerations
o Best practices