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Analyze, Design, and Build scalable, resilient, and cost-effective Big Data pipelines with a methodical process
What you’ll learn
System Design for Big Data Pipelines

Learn about the building blocks of a big data pipeline, their functions, and challenges
Adapt an end-to-end methodical approach to designing a big data pipeline
Explore techniques to ensure the overall scaling of a big data pipeline
Study design patterns for building blocks, their advantages, shortcomings, applications, and available technologies
Focus additionally on Infrastructure, Operations, and Security for Big Data deployments
Exercise the learnings in the course with a Batch and real-time use case study
Requirements
Big Data Technology Concepts
Familiarity with Big Data Technologies like Apache Spark, Apache Kafka, and NoSQL
Development / Deployment Experience with Big Data Technologies and Pipelines
Software Design and Development Experience including Cloud & Microservices
Description
Big data technologies have been growing exponentially over the past few years and have penetrated every domain and industry in software development. It has become a core skill for a software engineer. Robust and effective big data pipelines are needed to support the growing volume of data and applications in the big data world. These pipelines have become business-critical and help increase revenues and reduce costs.
Do quality big data pipelines happen by magic? High-quality designs that are scalable, reliable, and cost-effective are needed to build and maintain these pipelines.

How do you build an end-to-end big data pipeline that leverages big data technologies and practices effectively to solve business problems? How do you integrate them in a scalable and reliable manner? How do you deploy, secure and operate them? How do you look at the overall forest and not just the individual trees? This course focuses on this skill gap.

What are the topics covered in this course?
We start by discussing the building blocks of big data pipelines, their functions, and their challenges.
We introduce a structured design process for building big data pipelines.
We then discuss individual building blocks, focusing on the design patterns available, their advantages, shortcomings, use cases, and available technologies.
We recommend several best practices across the course.
We finally implement two use cases to illustrate how to apply the learnings in the course to a real-world problem. One is a batch use case and another is a real-time use case.
Who this course is for:
Big Data Pipeline Designers & Architects
Big Data Developers looking to move into Design/Architecture roles
Software Architects looking to gain Big Data Experience