Book Image

Data Lake Development with Big Data

By : Pradeep Pasupuleti, Beulah Salome Purra
Book Image

Data Lake Development with Big Data

By: Pradeep Pasupuleti, Beulah Salome Purra

Overview of this book

A Data Lake is a highly scalable platform for storing huge volumes of multistructured data from disparate sources with centralized data management services. This book explores the potential of Data Lakes and explores architectural approaches to building data lakes that ingest, index, manage, and analyze massive amounts of data using batch and real-time processing frameworks. It guides you on how to go about building a Data Lake that is managed by Hadoop and accessed as required by other Big Data applications. This book will guide readers (using best practices) in developing Data Lake's capabilities. It will focus on architect data governance, security, data quality, data lineage tracking, metadata management, and semantic data tagging. By the end of this book, you will have a good understanding of building a Data Lake for Big Data.
Table of Contents (13 chapters)

About the Authors

Pradeep Pasupuleti has 18 years of experience in architecting and developing distributed and real-time data-driven systems. He constantly explores ways to use the power and promise of advanced analytics-driven platforms to solve the problems of the common man. He founded Datatma, a consulting firm, with a mission to humanize Big Data analytics, putting it to use to solve simple problems that serve a higher purpose.

He architected robust Big Data-enabled automated learning engines that enterprises regularly use in production in order to save time, money, and the lives of humans.

He built solid interdisciplinary data science teams that bridged the gap between theory and practice, thus, creating compelling data products. His primary focus is always to ensure his customers are delighted by assisting and addressing their business problems through data products that use Big Data technologies and algorithms. He consistently demonstrated thought leadership by solving high-dimensional data problems and getting phenomenal results.

He has performed strategic leadership roles in technology consulting, advising Fortune 100 companies on Big Data strategy and creating Big Data Centers of Excellence.

He has worked on use cases such as enterprise Data Lake, fraud detection, patient re-admission prediction, student performance prediction, claims optimization sentiment mining, cloud infrastructure SLA violation prediction, data leakage prevention, and mainframe offloaded ETL on Hadoop.

In the book Pig Design Patterns, Packt Publishing, he has compiled his learning and experiences from the challenges involved in building Hadoop-driven data products such as data ingest, data cleaning and validating, data transformation, dimensionality reduction, and many other interesting Big Data war stories.

Out of his office hours, he enjoys running marathons, exploring archeological sites, finding patterns in unrelated data sources, mentoring start-ups, and budding researchers.

He can be reached at and