What is Big Data?

Take a moment to think about how much data Amazon has to deal with every day. Amazon has to track customer purchases, refunds, make recommendations for products, and update delivery dates at different times of the day. How do you think Amazon keeps up with all of this data? The answer lies, at least in part, in the field of big data.

“Big data” is a buzzword to some. To many in the technology industry, however, big data describes a set of practices and tools that keep websites and services that use massive amounts of data up and running.

In this guide, we’re going to chat about what big data is, why it is important, and how companies use big data in their operations.

What is Big Data?

Big data refers to large data sets which may be constantly changing. Big datasets are often so big that regular data analysis and processing tools struggle to handle all of the data that has been stored. Big data refers to both the systems companies use to work with massive datasets as well as tools used for big data management.

Big data has become increasingly important as it has become clear how web services can grow. Companies like Amazon, Facebook, and Netflix have all demonstrated how easy it is for a large company to incur massive amounts of data. Most of the data these sorts of companies collect could hold important business insights. Thus, it is not feasible to just ignore problems with big data sets: companies need to tackle them head on.

Big Data: The Three Vs

You may hear some people talk about the three “Vs”. In reality, there is no clear standard on what the Vs are and some people refer to different Vs than others. In any case, the three Vs relate to what constitutes a big data set.

Essentially, the three Vs help us answer the question: are we dealing with big data or regular data? Then, from there, a data engineer can assess their next steps in working with a dataset.

The three Vs are:

• Variety. This refers to what sorts of data are available. A big data set may contain a wide range of different sorts of data. For instance, a dataset could contain voice and video data. Or a dataset could contain just text. Or metadata.

• Volume. Volume refers to how much data is being processed. Big data sets are, by definition, big. Companies like Facebook and American Express, who both use big data, will collect massive amounts of data. Traditional tools may not be capable of analyzing such high-volume data sets.

• Velocity. Big datasets often change. Think of social networks. Every time you like a post, a post changes to account for your like. Now think of all of the millions of people who may have liked a post on that same social network in the last few minutes. New data keeps coming in. The term velocity refers to how quickly data comes in or how quickly a dataset changes.

Using these three Vs, data scientists can clearly identify their data needs and gain a deeper understanding as to how their infrastructure should evolve.

How is Big Data Used?

Any business that stores massive amounts of data is likely to use big data principles in their architecture at least to some extent. Because massive amounts of data can be collected in a range of industries, big data is not exclusive to the traditional “big tech company” examples.

Here are five scenarios in which big data is used:

• Fraud detection. Credit card companies and banks can use big data to determine whether a purchase has been made by an unauthorized party. Detecting fraud is a task completed by complex systems that accounts for things like the location of purchases, irregular purchase patterns, and other factors.

• Social media. Social networks like Facebook and Twitter have to deal with so much data changing. Every time a post is liked, the like must be saved. The same goes for new posts, comments, profile updates, and every other piece of data a user submits. Social networks use big data to ensure their services are reliable.

• Advertising. Advertising algorithms are based on massive datasets that track what sorts of sites people have visited that are linked to the algorithm. These algorithms then make recommendations about advertisements to serve users. Without big data, these algorithms may not be able to move nearly as quickly as they do.

• Autonomous Cars. Self-driving cars use big data to decide what actions are necessary to move around. A self-driving car will use big data to decide where to steer, how to turn corners, when to stop, how to respond to potholes, and more. The more data an autonomous vehicle has, the more likely the car will be able to understand what to do in any given road situation.

• Providing Broadband. Telecommunications companies can use big data to determine areas in which new investments in infrastructure may be optimal. For instance, a business could take into account their connections information to determine which areas are underserved. A business could also use data to model places in which new equipment could go to optimize its impact.

These are three scenarios where you may encounter big data. Because big data is quite a new field, the exact applications of big data are still being explored. For instance, some people are exploring how big data could be used in the insurance industry. Others are using big data sets in healthcare for medical research.

Here are a few other fields where you may encounter big data:

• Government
• Insurance
• Media and streaming
• Healthcare
• Virus modeling (i.e. analyses of the spread of COVID-19)

What Does Big Data Involve?

Big data is concerned with managing big data sets. Big data also involves building and improving upon the infrastructure necessary to store big data sets. On a day-to-day basis, a big data professional will monitor existing datasets to make sure infrastructure is keeping up to date with all of the data coming in.

Big data professionals will also plan new architecture to ensure a business can scale and they will help to implement any new systems they think are necessary. This is likely to involve programming and working with systems. For instance, big data work may include implementing a new Apache Spark system for a project that has not yet been released.

Big data engineers will work with other members of a data team to ensure their needs are met. For instance, a big data engineer may help ensure analysts have the data they need to conduct an analysis. Or a big data engineer may work with regular data engineers to advise on infrastructure. Big data is not just about coding: it’s about planning infrastructure.

Overall, big data is about ensuring that a business has the infrastructure necessary to collect the data they need. The overall strategy for a business will be set by managers and it is the job of big data engineers to ensure that any big data needs are met.

Working in Big Data

Big data is usually managed by big data engineers. These are specialists in working with large data sets. While big data is based on the field of data science, big data engineers are experts in new techniques and tools specifically for handling large data sets.

In a growing business, data scientists may take on big data roles but as a business’ data needs grow so too does the importance of having dedicated big data professionals.

To work in big data, one must have a good understanding of big data tools such as Hive or Pig. Experience with regular tools used in data science such as SQL, NoSQL, MongoDB, and Spark will all be useful. A strong understanding of data science is necessary, too. Most people who go on to work in big data acquire experience in regular data science first.

The Future of Big Data

Any data set that meets one of the three Vs — volume, velocity, variety — can be classified as a big data set. Businesses use big data to provide their services and to ensure a positive customer experience. Without big data, businesses like social networks, which deal with massive volumes of data, may not be able to provide such a swift user experience.

Big data is still in development. Every day more data is created so the tools we have for handling big data will need to stay constantly up to date. And big data is not just about the tools for storing data. As big data matures, more potential use cases of big data will be explored, thus opening up new opportunities in the field.