Data has been increasing at an exponential rate and organizations are either struggling to cope up or rushing to take advantage by analyzing it. Hadoop is an excellent open source framework, which addresses this big data problem.
I have used Hadoop within the financial sector for the last few years but could not find any resource or book that explains the usage of Hadoop for finance use cases. The best books I have ever found are again on Hadoop, Hive, or some MapReduce patterns, with examples on counting words or Twitter messages in all possible ways.
I have written this book with the objective of explaining the basic usage of Hadoop and other products to tackle big data for finance use cases. I have touched base on the majority of use cases, providing a very practical approach.
Chapter 1, Big Data Overview, covers the overview of big data, its landscape, and technology evolution. It also touches base with the Hadoop architecture, its components, and distributions. If you know Hadoop already, just skim through this chapter.
Chapter 2, Big Data in Financial Services, extends the big data overview from the perspective of a financial organization. It will explain the story of the evolution of big data in the financial sector, typical implementation challenges, and different finance use cases with the help of relevant tools and technologies.
Chapter 3, Hadoop in the Cloud, covers the overview of big data in cloud and a sample portfolio risk simulation project with end-to-end data processing.
Chapter 4, Data Migration Using Hadoop, talks about the most popular project of migrating historical trade data from traditional data sources to Hadoop.
Chapter 5, Getting Started, covers the implementation project of a very large enterprise data platform to support various risk and regulatory requirements.
Chapter 6, Getting Experienced, gives an overview of real-time analytics and a sample project to detect fraudulent transactions.
Chapter 7, Scale It Up, covers the topics to scale up the usage of Hadoop within your organization, such as enterprise data lake, lambda architecture, and data governance. It also touches base with few more financial use cases with brief solutions.
Chapter 8, Sustain the Momentum, talks about the Hadoop distribution upgrade cycle and wraps up the book with best practices and standards.