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The Big Data Trade-Off

Because of the incredible task of dealing with the data needs of the World Wide Web and its users, Internet companies and research organizations realized that a new approach to collecting and analyzing data was necessary. Since off-the-shelf, commodity computer hardware was getting cheaper every day, it made sense to think about distributing database software across many readily available servers built from commodity parts. Data processing and information retrieval could be farmed out to a collection of smaller computers linked together over a network. This type of computing model is generally referred to as distributed computing. In many cases, deploying a large number of small, cheap servers in a distributed computing system can be more economically feasible than buying a custom built, single machine with the same computation capabilities.

While the hardware model for tackling massive scale data problems was being developed, database software started to evolve as well. The relational database model, for all of its benefits, runs into limitations that make it challenging to deploy in a distributed computing network. First of all, sharding a relational database across multiple machines can often be a nontrivial exercise. Because of the need to coordinate between various machines in a cluster, maintaining a state of data consistency at any given moment can become tricky. Furthermore, most relational databases are designed to guarantee data consistency; in a distributed network, this type of design can create a problem.

Software designers began to make trade-offs to accommodate the advantages of using distributed networks to address the scale of the data coming from the Internet. Perhaps the overall rock-solid consistency of the relational database model was less important than making sure there was always a machine in the cluster available to process a small bit of data. The system could always provide coordination eventually. Does the data actually have to be indexed? Why use a fixed schema at all? Maybe databases could simply store individual records, each with a different schema, and possibly with redundant data.

This rethinking of the database for an era of cheap commodity hardware and the rise of Internet-connected applications has resulted in an explosion of design philosophies for data processing software.

If you are working on providing solutions to your organization’s data challenges, the current era is the Era of the Big Data Trade-Off. Developers building new data-driven applications are faced with all manner of design choices. Which database backend should be used: relational, key–value, or something else? Should my organization build it, or should we buy it? How much is this software solution worth to me? Once I collect all of this data, how will I analyze, share, and visualize it?

In practice, a successful data pipeline makes use of a number of different technologies optimized for particular use cases. For example, the relational database model is excellent for data that monitors transactions and focuses on data consistency. This is not to say that it is impossible for a relational database to be used in a distributed environment, but once that threshold has been reached, it may be more efficient to use a database that is designed from the beginning to be used in distributed environments.

The use cases in this book will help illustrate common examples in order to help the reader identify and choose the technologies that best fit a particular use case. The revolution in data accessibility is just beginning. Although this book doesn’t aim to cover every available piece of data technology, it does aim to capture the broad use cases and help guide users toward good data strategies.

More importantly, this book attempts to create a framework for making good decisions when faced with data challenges. At the heart of this are several key principles to keep in mind. Let’s explore these Four Rules for Data Success.

Build Solutions That Scale (Toward Infinity)

I’ve lost count of the number of people I’ve met that have told me about how they’ve started looking at new technology for data processing because their relational database has reached the limits of scale. A common pattern for Web application developers is to start developing a project using a single machine installation of a relational database for collecting, serving, and querying data. This is often the quickest way to develop an application, but it can cause trouble when the application becomes very popular or becomes overwhelmed with data and traffic to the point at which it is no longer acceptably performant.

There is nothing inherently wrong with attempting to scale up a relational database using a well-thought-out sharding strategy. Sometimes, choosing a particular technology is a matter of cost or personnel; if your engineers are experts at sharding a MySQL database across a huge number of machines, then it may be cheaper overall to stick with MySQL than to rebuild using a database designed for distributed networks. The point is to be aware of the limitations of your current solution, understand when a scaling limit has been reached, and have a plan to grow in case of bottlenecks.

This lesson also applies to organizations that are faced with the challenge of having data managed by different types of software that can’t easily communicate or share with one another. These data silos can also hamper the ability of data solutions to scale. For example, it is practical for accountants to work with spreadsheets, the Web site development team to build their applications using relational databases, and financial to use a variety of statistics packages and visualization tools. In these situations, it can become difficult to ask questions about the data across the variety of software used throughout the company. For example, answering a question such as “how many of our online customers have found our product through our social media networks, and how much do we expect this number to increase if we improved our online advertising?” would require information from each of these silos.

Indeed, whenever you move from one database paradigm to another, there is an inherent, and often unknown, cost. A simple example might be the process of moving from a relational database to a key–value database. Already managed data must be migrated, software must be installed, and new engineering skills must be developed. Making smart choices at the beginning of the design process may mitigate these problems. In Chapter 3, “Building a NoSQL-Based Web App to Collect Crowd-Sourced Data,” we will discuss the process of using a NoSQL database to build an application that expects a high level of volume from users.

A common theme that you will find throughout this book is use cases that involve using a collection of technologies that deal with issues of scale. One technology may be useful for collecting, another for archiving, and yet another for high-speed analysis.

Build Systems That Can Share Data (On the Internet)

For public data to be useful, it must be accessible. The technological choices made during the design of systems to deliver this data depends completely on the intended audience. Consider the task of a government making public data more accessible to citizens. In order to make data as accessible as possible, data files should be hosted on a scalable system that can handle many users at once. Data formats should be chosen that are easily accessible by researchers and from which it is easy to generate reports. Perhaps an API should be created to enable developers to query data programmatically. And, of course, it is most advantageous to build a Web-based dashboard to enable asking questions about data without having to do any processing. In other words, making data truly accessible to a public audience takes more effort than simply uploading a collection of XML files to a privately run server. Unfortunately, this type of “solution” still happens more often than it should. Systems should be designed to share data with the intended audience.

This concept extends to the private sphere as well. In order for organizations to take advantage of the data they have, employees must be able to ask questions themselves. In the past, many organizations chose a data warehouse solution in an attempt to merge everything into a single, manageable space. Now, the concept of becoming a data-driven organization might include simply keeping data in whatever silo is the best fit for the use case and building tools that can glue different systems together. In this case, the focus is more on keeping data where it works best and finding ways to share and process it when the need arises.

Build Solutions, Not Infrastructure

With apologies to true ethnographers everywhere, my observations of the natural world of the wild software developer have uncovered an amazing finding: Software developers usually hope to build cool software and don’t want to spend as much time installing hard drives or operating systems or worrying about that malfunctioning power supply in the server rack. Affordable technology for infrastructure as a service (inevitably named using every available spin on the concept of “clouds”) has enabled developers to worry less about hardware and instead focus on building Web-based applications on platforms that can scale to a large number of users on demand.

As soon as your business requirements involve purchasing, installing, and administering physical hardware, I would recommend using this as a sign that you have hit a roadblock. Whatever business or project you are working on, my guess is that if you are interested in solving data challenges, your core competency is not necessarily in building hardware. There are a growing number of companies that specialize in providing infrastructure as a service—some by providing fully featured virtual servers run on hardware managed in huge data centers and accessed over the Internet.

Despite new paradigms in the industry of infrastructure as a service, the mainframe business, such as that embodied by IBM, is still alive and well. Some companies provide sales or leases of in-house equipment and provide both administration via the Internet and physical maintenance when necessary.

This is not to say that there are no caveats to using cloud-based services. Just like everything featured in this book, there are trade-offs to building on virtualized infrastructure, as well as critical privacy and compliance implications for users. However, it’s becoming clear that buying and building applications hosted “in the cloud” should be considered the rule, not the exception.

Focus on Unlocking Value from Your Data

When working with developers implementing a massive-scale data solution, I have noticed a common mistake: The solution architects will start with the technology first, then work their way backwards to the problem they are trying to solve. There is nothing wrong with exploring various types of technology, but in terms of making investments in a particular strategy, always keep in mind the business question that your data solution is meant to answer.

This compulsion to focus on technology first is the driving motivation for people to completely disregard RDBMSs because of NoSQL database hype or to start worrying about collecting massive amounts of data even though the answer to a question can be found by statistical analysis of 10,000 data points.

Time and time again, I’ve observed that the key to unlocking value from data is to clearly articulate the business questions that you are trying to answer. Sometimes, the answer to a perplexing data question can be found with a sample of a small amount of data, using common desktop business productivity tools. Other times, the problem is more political than technical; overcoming the inability of admins across different departments to break down data silos can be the true challenge.

Collecting massive amounts of data in itself doesn’t provide any magic value to your organization. The real value in data comes from understanding pain points in your business, asking practical questions, and using the answers and insights gleaned to support decision making.

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