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This book explores the evolution of data platforms over the last five decades, spanning from data warehousing to big data and cloud technologies. It discusses architecture, guiding principles, technology, and various use cases in the banking industry. The role of fintech and meeting digital payment demands with modern platforms is addressed. Techniques for handling PII/SPDI data in the cloud, ingestion frameworks, real-time and streaming data, and data availability are discussed practically. Additionally, it covers the increasing roles of CDOs, governance, data security, and DPDP. These chapters serve as valuable references for banks and financial institutions, drawing from real-world data sources and global events.
This book explores the evolution of data platforms over the last five decades, spanning from data warehousing to big data and cloud technologies. It discusses architecture, guiding principles, technology, and various use cases in the banking industry. The role of fintech and meeting digital payment demands with modern platforms is addressed. Techniques for handling PII/SPDI data in the cloud, ingestion frameworks, real-time and streaming data, and data availability are discussed practically. Additionally, it covers the increasing roles of CDOs, governance, data security, and DPDP. These chapters serve as valuable references for banks and financial institutions, drawing from real-world data sources and global events.
"Building Cloud Data Platforms Solutions: An End-to-End Guide for Designing, Implementing, and Managing Robust Data Solutions in the Cloud" comprehensively covers a wide range of topics related to building data platforms in the cloud. This book provides a deep exploration of the essential concepts, strategies, and best practices involved in designing, implementing, and managing end-to-end data solutions. The book begins by introducing the fundamental principles and benefits of cloud computing, with a specific focus on its impact on data management and analytics. It covers various cloud services and architectures, enabling readers to understand the foundation upon which cloud data platforms are built. Next, the book dives into key considerations for building cloud data solutions, aligning business needs with cloud data strategies, and ensuring scalability, security, and compliance. It explores the process of data ingestion, discussing various techniques for acquiring and ingesting data from different sources into the cloud platform. The book then delves into data storage and management in the cloud. It covers different storage options, such as data lakes and data warehouses, and discusses strategies for organizing and optimizing data storage to facilitate efficient data processing and analytics. It also addresses data governance, data quality, and data integration techniques to ensure data integrity and consistency across the platform. A significant portion of the book is dedicated to data processing and analytics in the cloud. It explores modern data processing frameworks and technologies, such as Apache Spark and serverless computing, and provides practical guidance on implementing scalable and efficient data processing pipelines. The book also covers advanced analytics techniques, including machine learning and AI, and demonstrates how these can be integrated into the data platform to unlock valuable insights. Furthermore, the book addresses an aspects of data platform monitoring, security, and performance optimization. It explores techniques for monitoring data pipelines, ensuring data security, and optimizing performance to meet the demands of real-time data processing and analytics. Throughout the book, real-world examples, case studies, and best practices are provided to illustrate the concepts discussed. This helps readers apply the knowledge gained to their own data platform projects.
Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is an hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you''ll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You''ll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyse it. about the technology Access to affordable, dependable, serverless cloud services has revolutionized the way organizations can approach data management, and companies both big and small are raring to migrate to the cloud. But without a properly designed data platform, data in the cloud can remain just as siloed and inaccessible as it is today for most organizations. Designing Cloud Data Platforms lays out the principles of a well-designed platform that uses the scalable resources of the public cloud to manage all of an organization''s data, and present it as useful business insights. about the book In Designing Cloud Data Platforms, you''ll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform. Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs. Starting with foundational principles, you''ll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors. As you progress past the basics you''ll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more. what''s inside The tools of different public cloud for implementing data platforms Best practices for managing structured and unstructured data sets Machine learning tools that can be used on top of the cloud Cost optimization techniques about the reader For data professionals familiar with the basics of cloud computing and distributed data processing systems like Hadoop and Spark. about the authors Danil Zburivsky has over 10 years experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years.
This book shows banking professionals how to leverage the best practices in the industry to build a structured and coordinated approach towards the digitization of banking processes. It provides a roadmap and templates in order to industrialize the financial services firm over iterative cycles. To achieve the planned business and revenue results at the optimal costs, the digital transformation has to be calibrated and coordinated across both the front and back office, scaled and timed against external innovation benchmarks and Fintechs. To this end, data collection and evaluation must be ingrained, banking-specific artificial intelligence methods must be included, and all digitization approaches must be harmonized on an iterative basis with the experience gained. Spread over several chapters, this book provides a calibration and coordination framework for the delivery of the digital bank 4.0.
The formula for the Future of Work is called SMAC - social, mobile, analytics and cloud on one integrated stack where each function enables another to maximize its effect. This is the new enterprise IT model delivering an organization that is more connective, collaborative, real time and productive. This book provides a comprehensive view of how SMAC Technologies are impacting the entire banking "eco-system" as well as the key stakeholders, namely customers, employees and partners.
The rise of the Data Cloud is ushering in a new era of computing. The world’s digital data is mass migrating to the cloud, where it can be more effectively integrated, managed, and mobilized. The data cloud eliminates data siloes and enables data sharing with business partners, capitalizing on data network effects. It democratizes data analytics, making the most sophisticated data science tools accessible to organizations of all sizes. Data exchanges enable businesses to discover, explore, and easily purchase or sell data—opening up new revenue streams. Business leaders have long dreamed of data driving their organizations. Now, thanks to the Data Cloud, nothing stands in their way.
DIVE INTO THE GREAT WIDE OPEN Young. Hungry. Visionary. Chris Zadeh saw a future few could ever imagine possible. When he presented his idea--coding a new core banking engine in the cloud--bankers laughed. They scoffed. Then they rejected his revolutionary idea outright. They said, "The cloud's not safe." "Who do you think you are--telling us how to run our banks?" "And who the hell is ever going to put a bank on the cloud?" Chris Zadeh and his team of underdogs knew the answer: everyone. They also knew the cloud wasn't just safe, it was the future of banking. And more than anything, he knew exactly who he and his team were: they were Ohpen, the first software company in the world to put a bank on the cloud. Together, this band of brothers harnessed their give-it-all mentality, fighting spirit, and bold vision to change how the world banks. In this surprising, unfiltered, outstandingly told story, Banking the Cloud takes the reader on an epic journey--from thrill of discovery, to the devastation of defeat, and to an exhilarating and last-minute rally that changed everything. This is a remarkable tale of what it takes to innovate, survive, and ultimately win no matter what the obstacle. You'll find it impossible to put this book down.
Financial institutions must become more innovative in the conduct of their business. Cloud computing helps to achieve several objectives: innovative services, re-engineered processes, business agility and value optimization. Research, consultancy practice and case studies in this book consider the opportunities and risks with vendor relationships.