Preface - Time Series Databases: New Ways to Store and Access Data (2014)

Time Series Databases: New Ways to Store and Access Data (2014)


Time series databases enable a fundamental step in the central storage and analysis of many types of machine data. As such, they lie at the heart of the Internet of Things (IoT). There’s a revolution in sensor–to–insight data flow that is rapidly changing the way we perceive and understand the world around us. Much of the data generated by sensors, as well as a variety of other sources, benefits from being collected as time series.

Although the idea of collecting and analyzing time series data is not new, the astounding scale of modern datasets, the velocity of data accumulation in many cases, and the variety of new data sources together contribute to making the current task of building scalable time series databases a huge challenge. A new world of time series data calls for new approaches and new tools.

In This Book

The huge volume of data to be handled by modern time series databases (TSDB) calls for scalability. Systems like Apache Cassandra, Apache HBase, MapR-DB, and other NoSQL databases are built for this scale, and they allow developers to scale relatively simple applications to extraordinary levels. In this book, we show you how to build scalable, high-performance time series databases using open source software on top of Apache HBase or MapR-DB. We focus on how to collect, store, and access large-scale time series data rather than the methods for analysis.

Chapter 1 explains the value of using time series data, and in Chapter 2 we present an overview of modern use cases as well as a comparison of relational databases (RDBMS) versus non-relational NoSQL databases in the context of time series data. Chapter 3 and Chapter 4 provide you with an explanation of the concepts involved in building a high-performance TSDB and a detailed examination of how to implement them. The remaining chapters explore some more advanced issues, including how time series databases contribute to practical machine learning and how to handle the added complexity of geo-temporal data.

The combination of conceptual explanation and technical implementation makes this book useful for a variety of audiences, from practitioners to business and project managers. To understand the implementation details, basic computer programming skills suffice; no special math or language experience is required.

We hope you enjoy this book.