Explore architectural approaches to building Data Lakes that ingest, index, manage, and analyze massive amounts of data using Big Data technologies
About This Book
- Comprehend the intricacies of architecting a Data Lake and build a data strategy around your current data architecture
- Efficiently manage vast amounts of data and deliver it to multiple applications and systems with a high degree of performance and scalability
- Packed with industry best practices and use-case scenarios to get you up-and-running
Who This Book Is For
This book is for architects and senior managers who are responsible for building a strategy around their current data architecture, helping them identify the need for a Data Lake implementation in an enterprise context.
The reader will need a good knowledge of master data management, information lifecycle management, data governance, data product design, data engineering, and systems architecture. Also required is experience of Big Data technologies such as Hadoop, Spark, Splunk, and Storm.
What You Will Learn
- Identify the need for a Data Lake in your enterprise context and learn to architect a Data Lake
- Learn to build various tiers of a Data Lake, such as data intake, management, consumption, and governance, with a focus on practical implementation scenarios
- Find out the key considerations to be taken into account while building each tier of the Data Lake
- Understand Hadoop-oriented data transfer mechanism to ingest data in batch, micro-batch, and real-time modes
- Explore various data integration needs and learn how to perform data enrichment and data transformations using Big Data technologies
- Enable data discovery on the Data Lake to allow users to discover the data
- Discover how data is packaged and provisioned for consumption
- Comprehend the importance of including data governance disciplines while building a Data Lake
In Detail
A Data Lake is a highly scalable platform for storing huge volumes of multistructured data from disparate sources with centralized data management services. It eliminates the need for up-front modeling and rigid data structures by allowing schema-less writes. Data Lakes make it possible to ask complex far-reaching questions to find out hidden data patterns and relationships.
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 such as Spark, Storm, Hive, and so on, to create an environment in which data from different sources can be meaningfully brought together and analyzed.
Data Lakes can be viewed as having three capabilities-intake, management, and consumption. This book will take readers through each of these processes of developing a Data Lake and guide them (using best practices) in developing these capabilities. It will also explore often ignored, yet crucial considerations while building Data Lakes, with the focus on how to 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. You will be able to utilize Data Lakes for efficient and easy data processing and analytics.
Pradeep Pasupuleti has over 17 years of experience in architecting and developing distributed and real-time data-driven systems. Currently, he focuses on developing robust data platforms and data products that are fuelled by scalable machine-learning algorithms, and delivering value to customers in addressing business problems by applying his deep technical insights.Pradeep founded Datatma expressly to humanize Big Data, simplify it, and unravel new value on a previously unimaginable scale in economy and scope. He has created COE (Centers of Excellence) to provide quick wins with data products that analyze high-dimensional multistructured data using scalable natural language processing and deep learning techniques. He has performed roles in technology consulting and advising Fortune 500 companies.Beulah Salome Purra has over 11 years of experience and specializes in building large-scale distributed systems. Her core expertise lies in working on Big Data Analytics. In her current role at ATMECS, her focus is on building robust and scalable data products that extract value from the organization's huge data assets.
