Big data is all the buzz. Startups and seasoned companies alike are all exploring a new approach to problem-solving through the use of "big data."
But what is big data? And how can you take advantage of the increasing demand for big data science and technology?
Data is information. Big data is more information. The key distinctions between data and big data are volume, velocity, and variety. Generally, big data is more extensive information with more individual components that are collected in a shorter period of time. Big data sources are often new but can encompass older data streams as well.
Nowadays, we create more data than ever before. Within this data are valuable insights that we can use to improve our various systems and processes. Data Scientists, Analysts, and Engineers collect and analyze data to find valid and useful conclusions.
Below, we'll take a closer look at big data, the technologies behind it, the challenges of using it, and more.
Big data examples
As we said, big data holds valuable insights. Many of these insights help companies better serve their customers — and generate more money.
Because of this, big data is often used in marketing. Many of our online behaviors are tracked, from our activity on social media to our shopping habits. Marketers use this data for targeted advertising, promoting products and services that align with your interests.
Big data is also used in the healthcare industry. Think of all the wearable devices we have today, from Apple Watches to Fitbits. These devices can track your heart rate, breathing, sleep habits, and more — and they can even alert you to any concerning changes. Plus, physicians can use the data from these devices to create more comprehensive health profiles and better treat their patients.
We can also find examples of big data in the transportation and automotive industries. Self-driving cars and trucks use data on weather and road conditions, vehicle and pedestrian information, and more to increase safety and efficiency.
As you can see, big data holds great potential for improving our society. But, before we can make use of big data, it needs to be processed.
Big data processing
Because big data is so vast and comprehensive, it needs to be processed before being analyzed for insights. This involves collecting and comparing data from multiple sources, cleaning it to remove any errors or duplicates, and more.
After big data has been processed, data science professionals go through it to find any relevant patterns. This often involves the use of machine learning algorithms. Then, data visualization methods are used to make these insights easy to understand. Statistics also play a key role in data analysis as it helps us understand the relationships between data and probable outcomes.
Big data programming languages
There are several programming languages behind the tools Data Scientists use to collect, process, analyze, and visualize big data. Each language has its own advantages. Some of the most popular languages used for big data include:
Python is easy to learn, and it's one of the most popular languages used for data science. As a result, there are many Python libraries designed for data processing, analysis, and visualization. These libraries make it much easier to work with big data.
Python can also be used for statistical analysis, and it's also commonly used in machine learning — two critical components of data science.
Java also has utility for big data. Some of the most popular big data tools are written in Java. They're open-source, flexible, and free to use — making Java a key language for anyone aspiring to work with big data.
C and C++ are incredibly useful programming languages. While C was invented in the early 1970s and C++ in the mid-1980s, C and C++ programmers are still in strong demand today — and for a good reason.
When it comes to speed, C++ is often the best choice. Processing massive amounts of data quickly is one of the key advantages of C programming languages. When information is needed quickly in certain use cases for big data, C++ may be the best option.
With big data, statistical analysis is an integral part of drawing valid and useful conclusions. R excels at statistical analysis and visualization. When complicated statistics need to be applied, R is often the programming language of choice for data analysis.
SQL is used for accessing information stored in databases. The language was developed for handling large databases with relationships between different variables from different datasets. SQL is often the language utilized to easily access large amounts of stored data.
Challenges for big data
There are many challenges for big data. Incoming data that needs to be analyzed can be structured, unstructured, or somewhere in between.
Structured data is clearly defined, like a birthday or the number of widgets sold per day. As a result, it's much easier to process and interpret.
Unstructured data is not well defined and needs further interpretation to be useful. The text of an email or a tweet is typically a good example of unstructured data.
Part of the challenge of big data is just making sense of the massive amount of information available. Algorithms for understanding the general meaning of text are often a key part of gleaning insights from big data.
The privacy and security of big data are also major challenges. It often seems like we hear about the theft of personal information from thousands of people every week. Big data demands new tools and techniques to keep information secure. Losing control of information can damage a company's reputation and lead to various legal and financial consequences.
Data storage and processing are also huge challenges with big data. With large quantities of data that change rapidly, quick access and interpretation are required. Cloud storage is often utilized but can present challenges with speed, costs, and accessibility.
Learn more about big data
Opportunities in big data abound, and the demand for data science professionals is likely to grow as the online world continues to produce more information.
If you're interested in working with big data, the first step is learning how to use some of the programming languages listed above. Use any of the courses below to get started:
Or, if you'd rather learn everything all at once, check out our Data Scientist or Data Analyst Career Paths. These courses will teach you how to use the programming languages above to collect, manipulate, and analyze data. You'll also put your skills to use by creating projects that you can feature in a portfolio to impress future employers and clients.