Название: High-Performance Data Architectures: How to Maximize Your Business with a Cloud-Based DatabaseАвтор: Joe McKendrick, Ed Huang
Издательство: O’Reilly Media, Inc.
Год: 2023-08-04
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.2 MB
By choosing the right database, you can maximize your business potential, improve performance, increase efficiency, and gain a competitive edge. This insightful report examines the benefits of using a simplified data architecture containing cloud-based HTAP (hybrid transactional and analytical processing) database capabilities. You'll learn how this data architecture can help data engineers and data decision makers focus on what matters most: growing your business.
There was a time when database choices were fairly limited. Enterprises could work with flat-file databases, or, eventually, more adaptable relational databases. These databases worked well for basic transactions and internal corporate operations, but users had to rely on IT teams to craft and generate reports about the state of their business. That all changed about a decade ago, with an explosion of new types of databases, built on the web and cloud, that put more power in the hands of end users—and gave database managers more powerful tools to serve fast-changing business needs.
As databases made the transition to the cloud, they evolved, from serving as simply data storage frameworks to becoming essential instruments for delivering greater customer service, as well as understanding of the business and its environs. Databases evolved with the platforms that arose within the computing world—from mainframes to midrange-class computers to personal computers, from proprietary to open source systems, and ultimately, to the cloud. But the evolution isn’t stopping there. There are many frontiers still open for the advancement of database solutions, with many issues that still need to be addressed: data silos that keep information from effectively reaching the users and applications that need it, data quality and integrity issues, a lag in adapting to new business realities, scalability issues in an era when data and associated applications are exploding, security issues, privacy requirements, and lack of talent to maintain data environments.
Relational databases helped in discovering and understanding trends within the business but were expensive in terms of multiuser or per-processor licensing, as well as difficult to set up and maintain. SQL itself required a robust understanding of its structure and commands. Seeking to avoid the complexity of building SQL-based queries for relational databases, along with their restrictions, a new breed of databases emerged: not only SQL (NoSQL) databases. The first generation of NoSQL databases focused on key-value stores (Berkeley DB and similar), text searching (Elasticsearch), and later document stores such as CouchDB and MongoDB.
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