What Is Big Data and How Does Amazon Use It?

Big data has a lot of hype in the Data Science and Data Analytics field. If you work in any technical or analytical workspace, chances are you’ve probably heard this phrase.

If you’ve heard about big data then you may have heard the acronym AWS before too. Immersing yourself in the data science field is difficult enough and learning all these topics can be challenging.

Do not worry though, because these topics make complete sense once they are explained.

Big Data is a general term used throughout the industry. AWS stands for Amazon Web Services which is a service that helps organizations large and small harness the power of big data.

It is crucial for a Data Scientist or Analyst to know about both topics and how they impact the world.

What is Big Data?

Before understanding Amazon’s prolific service, it’s essential to know what Big Data is.

Every person in the modern world interacts with data in their daily lives and many people use it to their advantage.

The phrase “big data” describes data sets that are so large and intricate that conventional methods are not powerful enough to process or analyze them.

Big Data is commonly defined by the three V’s, which were first coined by Doug Laney, former Vice President and distinguished analyst with Gartner’s Chief Data Officer (CDO) research and advisory practice.

• Volume: Businesses, organizations, and governments collect data from a growing number of sources, such as business transactions, the Internet of Things (IoT), and social media. In the past, storing all this data was problematic. However, computing power has evolved to a point that storage is no longer an issue.
• Velocity: The speed at which data is received. There is no lag time anymore instead, data is being collected at extremely fast speeds close to real-time.
• Variety: There are multiple types of data. It can come in a structured, unstructured, numeric, text, email, or video form. Data can be anything and everything in today’s world.

Over time, the understanding of big data evolved and two more V’s have been added.

• Variability: Not only is the velocity and variety of data growing but the flow of data can be unpredictable. The velocity and variety of a data type can change without notice.
• Veracity: Any data scientist or analyst can tell you that data cleansing is a large part of the job. Raw data is most often “noisy” and “dirty” because of how many different sources and forms it can come from. Making connections and relationships between data and deciding what data is important or unimportant can become difficult with the level of veracity.

Big Data in Businesses and Organizations

Big data’s usefulness comes from the insights that large data sets have concealed within them.

Organizations can uncover trends and patterns in the data they collect that influence human decision making. Furthermore, businesses can use the insights found to cut costs, mitigate risk, set prices, and make predictions on future trends.

Here are some real-world examples of how big data is used:

Redfin ingests massive amounts of real estate listings and utilizes big data practices to process it all and deliver the necessary information to its internal and external stakeholders.

The health care industry receives data from patient records, treatment plans, and prescription information. They must employ big data practices to ensure the safety of their patients and constantly search for ways to improve care.

Even the financial industry uses big data! Think about the number of trades that are being executed on the New York Stock Exchange and the amount of sensitive information held within the banks.

Big Data is critical in the modern world and obtaining ways to harness its power has been a staple in Amazon’s business plan.

Amazon Web Services and Big Data

Not only does Amazon apply big data in the same way other organizations do by tracking customer sentiment and patterns, but they also discovered a way to make products out of it.

Related: Amazon changes prices on its products about every 10 minutes — here’s how and why they do it.

Amazon Web Services is a cloud computing service that provides organizations the servers, infrastructure, security, and human capital needed to accomplish the storage, processing, and analysis of immense data sets.

Amazon deploys various products under the AWS umbrella for the diverse facets of big data.

Amazon EMR is used to process data, Amazon S3 can be used to create data lakes and store data, and Amazon Sage Maker is used as a platform service for predictive analytics and machine learning. These are only a few of the products AWS offers.

Just like its e-commerce platform, Amazon has created a service that provides convenience and ease to its users through cloud computing.

Conclusion

Big data is a general term used to describe large data sets that require advanced storage, processing, and analytic techniques to understand.

Large and small organizations can use big data to track trends and patterns like never before to aid in their decision making.

Unsurprisingly, Amazon found a gap in the market they could fill and created Amazon Web Services (AWS) to provide a cost-efficient and effective way for people to manage big data.

Don’t be shocked if you see a generation of children named “Amazon” in the near future with how many times everyone hears that name now.