xv
Foreword
We are in the midst of one of the biggest transformations of Information Technology
(IT). Rapidly evolving business requirements have demanded agility in all aspects of IT.
As more and more paper-based business processes are getting digital, rapid application
development, staging, and deployment have become the norm. In addition, the data
exhaust from these digital applications has become enormous and needs to be analyzed
in real time. Growing volumes of historical data is considered valuable for improving
business efficiency and identifying future trends and disruptions. Ubiquitous end-user
connectivity, cost-efficient software and hardware sensors, and democratization of
content production have led to the deluge of data generated in enterprises. As a result,
the traditional data infrastructure has to be revamped. Of course, this cannot be done
overnight. To prepare your IT to meet the new requirements of the business, one has to
carefully plan re-architecting the data infrastructure so that existing business processes
remain available during this transition.
Hadoop and NoSQL platforms have emerged in the last decade to address the
business requirements of large web-scale companies. Capabilities of these platforms
are evolving rapidly, and, as a result, have created a lot of hype in the industry. However,
none of these platforms is a panacea for all the needs of a modern business. One needs
to carefully consider various business use cases and determine which platform is most
suitable for each specific use case. Introducing immature platforms for use cases that
are not suited for them is the leading cause of failure of data infrastructure projects. Data
architects of today need to understand a variety of data platforms, their design goals, their
current and future data protection capabilities, access methods, and performance sweet
spots, and how they compare in features against traditional data platforms. As a result,
traditional database administrators and business analysts are overwhelmed by the sheer
number of new technologies and the rapidly changing data landscape.
This book is written with those readers in mind. It cuts through the hype and gives
a practical way to transition to the modern data architectures. Although it may feel like
new technologies are emerging every day, the key to evaluating these technologies is to
align your current and future business use cases and requirements to the design-center
of these new technologies. This book helps readers understand various aspects of the
modern data platforms and helps navigate the emerging data architecture. I am confident
that it will help you avoid the complexity of implementing modern data architecture and
allow seamless transition for your business.
—Milind Bhandarkar, PhD
Founder and CEO, Ampool, Inc.
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