Big data basics for tech beginners
Despite big data currently ranking among top business intelligence and data analytics trends, businesses continue to suffer from a lack of data-savvy talent. Research from BARC shows half of respondents reporting a lack of analytical or technical know-how for big data analytics. This is good news for tech beginners, however, whose knowledge and skills are being welcomed by companies who want to reap the benefits of big data.
If you find data science a tempting opportunity, you’ll benefit from this overview of big data basics for beginners. Below, we’ll discuss what the requirements for jobs are and which skills you should master in order to start a successful data science career.
Instead of reciting a definition or giving a generic overview, let’s look at big data’s key features through the lens of something that is well known to all of us: recommendation engines. These are tools widely used in e-commerce to aid in customer experience, but that also help gather data about consumers. Web store visitors search for products, view them, add and remove them from their carts, make purchases like, etc. – and every activity is an entry in a database. The entry may look like “Customer X opened Product Y page.” Millions of customers exist, and they perform dozens of activities per visit, which means that a retailer needs impressive storage capacity to log all these actions.
Distributed data storage has become a solution to this problem. According to this principle, data is stored on numerous standard computers rather than on one custom-built powerful machine. This allows companies to achieve high scalability: when the number of records increases, the retailer can just add extra machines.
Each time a visitor starts a new tour on the website, the analytical system tracks all their activities and compares them with previous activities of this particular visitor and those of other visitors. In order to perform this task quickly, the analytical system divides the tasks among numerous machines to enable parallel data processing. The analysis results lay the basis for personalized recommendations.
Summing it up: Big data is data sets that resemble a log of events by nature and require distributed data storage, parallel data processing and special approaches and methods. You can learn more about big data use cases in this primer.
You should generally expect to master multiple technologies to become an expert in big data. We’ve selected the most popular frameworks and programming languages for a beginner to get acquainted with. The list is not exhaustive: so, feel free to go beyond it whenever you are ready.
Big data frameworks Apache Hadoop is a framework for parallel data processing and distributed data storage. Apache Spark is a parallel data processing framework.
