Course Methodology

This course will be highly interactive with group discussions, case studies, hands-on practical exercises, and group activities being the core focus.

Course Objectives

By the end of the course, participants will be able to:

• Design big data implementation plans and create strategies for data driven solutions
• Explain the challenges of big data and traditional technologies like Excel
• Discuss the main challenges and advantages of Hadoop ecosystem and other big data distributed architectures
• Demonstrate and discuss key technologies for big data storage and compute, such as PostgreSQL and MongoDB
• Discuss popular machine learning algorithms and the importance of ethics in data analytics and artificial intelligence
• Deliver an architectural diagram for analytics focused use cases

Target Audience

This course is ideal for data analysts, data engineers, data scientists, as well as technically-inclined management and administrative professionals seeking to understand big data strategies, technologies and use cases.  Recommended pre-knowledge includes basic programming experience and analyzing data in python, knowledge of basic database technologies, and awareness of analytics driven business initiatives.

Target Competencies

• Big data hands-on labs
• Big Data analytics structures and technologies
• Ethics and integrity for big data analytics
• Big data storage and computer system implementation
• Architecture diagram design

Introduction to Big Data Analytics

• What is Big Data?
• 5 “V’s” of big data
• How big data relates to data analytics
• Big data impact on technologies
• Open source revolution
• Key big data concepts and data types
• Text, audio, images
• Big data professional roles
• How can big data projects meet organizational needs
• Big data Examples:
• Netflix, LinkedIn, Facebook, Google, Orbitz, Dell, others.
• Best practices in project design
• Assessing the current state of your organization

Storing Big Data

• Big data architectures and paradigms
• The Hadoop Ecosystem
• Overview of Hadoop
• Hadoop Distributed File System (HDFS)
• Massively parallel processing (MPP) versus distributed in-memory applications
• RDBMSs vs NoSQL DBs
• PostgreSQL, MongoDB, Cassandra
• Streaming data
• Data-warehousing versus Data Mart

Computing Big Data

• How to access big data
• Role of cloud computing
• Data movement risk
• Networking and co-location
• Big data extract, transform, load (ETL)
• Big data compute technologies
• Hadoop continued
• MapReduce and beyond
• Distributed compute
• High performance clusters
• Spark
• Streaming: Storm, Spark structured streaming
• Other big data technologies: Kafka, etc.
• Cloud applications for big data

Big Data Projects

• Basics of data analytics
• Roles and objectives
• Key math and statistics concepts
• Supervised versus Unsupervised
• Key technologies and applications
• Getting value out of Big Data
• 5 P’s of data science
• Importance of ethics
• Programmability

Architecting Big Data Solutions

• Identify analytical opportunities
• Define and assess the problem
• Describe the impact and use of data to address the problem
• Identify potential data sources
• Brainstorm an analytics strategy to implement
• Storage and compute
• Identify a cloud environment strategy
• Brainstorm key storage systems and compute environments