Feed: MemSQL Blog.
Author: Floyd Smith.
You can take advantage of a free download of key chapters from the exciting e-book, Designing Data-Intensive Applications. The chapters included in the free download cover two areas of vital concern to most MemSQL users, and indeed most developers and architects: transactions and streaming data.
Designing Data-Intensive Applications, as a complete book, is more than 500 pages long. It takes as its premise that data is at the center of many of the challenges in system design today. (This is not a premise that is a surprise to us at MemSQL.)
the way you think about application design.
Author Martin Kleppman is an expert on this topic. He’s a researcher in distributed systems at Cambridge University. Before that, he was a software engineer tackling these topics at several companies, including LinkedIn and Rapportive. Martin is a frequent conference speaker, blogger, and contributor to open source projects. Besides the work under discussion here, his other currently available book is a free ebook on stream processing. Check it out!
You will find that reading the excerpted chapters, the free ebook, or the complete Designing Data-Intensive Applications book will be good for you in every possible way. You will grow professionally and also personally. Martin’s work is that good.
However, we here at MemSQL will also challenge you to go beyond even Martin’s ambitious thesis, and his ambitious book. (Which, as we mentioned, is more than 500 pages long.) Rather than focusing on data-intensive applications, as the author does, we believe that most applications under development today should be treated as data-intensive.
That is, we believe that every application should be reconsidered, during design, around key data-related questions:
- What is the core data needed as input to this application?
- What additional data could usefully be gathered as part of the application?
- What is the short-term and long-term value of the core data?
- What is the short-term and long-term value of the potential, additional data?
- Where will the operational data store be? Where will the archival data store be? (Which database; in the cloud vs. on-prem; etc.)
- Do you have the data science and data analytics resources in-house to make the best use of the data? If not, should you consider licensing it, partnering around it, or otherwise making sure you make full use of it?
Machine learning and AI, in particular, bring these questions to life. You can’t do machine learning and AI without data; with machine learning, AI, and the data needed to power relevant applications, you may be able to accomplish things you had not previously believed to be achievable.
We hope we have made the case that our free download for Designing Data-Intensive Applications is not only desirable, but a necessity, to help you prepare for the future use of data in your applications. We hope you download the free chapters today.