Compared to traditional data analytics, Big Data analytics demands more skills. For instance, you can easily get away from programming as a data analyst, but for Big Data analysis, you need to be comfortable with programming. You need to familiar with programming concepts and know languages like R, Python, Julia, Scala, Java, etc.
Similarly, you need more sophisticated methods to navigate a relational database management system, as Big Data supplies a continuous stream of raw structured and non-structured data.
Our ability as a Big Data analyst is to draw insights from a supply of steady data processed every minute. Big data analysis combines traditional analytics with use of technology and tools, to handle large streams of data.
Traditional analysts have found Big Data analytics certification as way to enter the field. However, certifications help to a certain extent. Analytics is an application-oriented field. Unless, you are delivering results, it is no use. At the end of the day, it is the skills that matter.
Following are some skills that are required to be a Big Data analyst.
As mentioned earlier, in traditional analytics, full-fledged programming isn’t required. However, if you are working in Big Data, you need to be well-versed with programming. Unlike traditional analytics, where most of the work is using statistical algorithms.
Big data is evolving and there are no standard processes around working with large datasets. Continuous optimization is required every day. In such instance, programming comes handy. Knowledge of R, Python, and Java is enough to get started.
2. Data warehousing:
Knowledge and experience working with non-relational databases, including HDFS, Couch DB, Mongo DB, etc. And relational databases, MySQL, DB2, Oracle etc. Big Data is hugely dependent on non-relational databases. It is one of the aspects of Big Data, which lends it the Big prefix.
3. Knowledge of frameworks:
The volume of data processed in Big Data is huge. Thus, traditional frameworks fall short on processing large amount of data. Apache Spark, Flink, Storm, Samza, among other tools offer the capacity to stream large amount of data.
4. Business Knowledge:
Big Data analysts gather information and draw business insights. For this, it is very important to understand the business aspect of companies. How a business operates and factors that impact business operations is an important understanding for companies.
One of the reasons it is hard to find good Big Data analyst is, it is rare to find people who are good at every aspect – programming, business, statistics. Most people have fewer competencies.
5. Numerical ability:
Though Big Data processing requires use of number of technologies, understanding of fundamental underlying principles is crucial. Techniques such as probability distribution, hypothesis testing, statistics, random variables etc.
In a nutshell, you are required to know a lot of technologies and build a new set of skills. Big Data is a technology and it is evolving. In the coming time, there will be more developments.Big Data certification is a good way to advance your skills and stay ahead in the industry.