Databases Books

The MongoDB Notes for Professionals book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow.

A database is an organized collection of data. A database may be on paper, or, held in computer files such as spreadsheets or more formally in a software system known as a computerized database management system (for example: DB2, db4o, IMS, MS Access, MS SQL Server, mySQL, Oracle, Sybase, Total, Versant). In this book we focus on Relational databa

While the documentation around PostgreSQL is detailed and technically rich, finding a simple, clear path to learning what it is, what it does, and how to use it can be challenging. This book seeks to help with that challenge. From getting your first instance, or cluster, up and running, to backups, to server configurations, we cover all the basics

If a query is performing poorly, and you can't understand why, then that query's execution plan will tell you not only what data set is coming back, but also what SQL Server did, and in what order, to get that data. It will reveal how the data was retrieved, and from which tables and indexes, what types of joins were used, at what point filtering,

Few software developers would build an application without using source control, but its adoption for databases has been slower. Yet without source control to maintain the scripts necessary to create our database objects, load lookup data, and take other actions, we cannot guarantee a reliable and repeatable database deployment process, let alone c

R is one of the most popular, powerful data analytics languages and environments in use by data scientists. Actionable business data is often stored in Relational Database Management Systems (RDBMS), and one of the most widely used RDBMS is Microsoft SQL Server. Much more than a database server, it's a rich ecostructure with advanced analytic capab

The SQL Notes for Professionals book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow.

With the big data boom, the rise of the Internet of Things (IoT), and the development of artificial intelligence (AI) applications, we've entered a new era of smart data. Unfortunately, not many enterprises are ready for it. Some companies are deficient in data management, while others lack standard data engineering systems. Some simply lag behind

Traditional data architecture patterns are severely limited. To use these patterns, you have to ETL data into each tool - a cost-prohibitive process for making warehouse features available to all of your data. The lack of flexibility with these patterns requires you to lock into a set of priority tools and formats, which creates data silos and data

Are your data systems slowing down your AI initiatives? The potential of AI to revolutionize business is undeniable, but many organizations struggle to bridge the gap between ambitious ideas and real-world results. The cause? Traditional data architectures remain too rigid and siloed to support today's dynamic, data-intensive demands. If you're a d