The advent of big data has created a high demand for data experts. As companies depart from traditional settings into the big data revolution, there is a need for data engineers to develop their skills. This will place them in good positions to carry out designs, maintenance, and testing of big data within the architecture of businesses.
Data engineers are mainly concerned with the development of systems that harbor large quantities of data. When data engineers build the right data architecture, it’s easier for data scientists to dig out the relevant insights to fuel the growth of organizations.
Here are some useful resume tips you’ll need for career development in a role as a data engineer.
With the rising popularity and boon of Python, it has become one of the most sought-after and widely used programming languages in the industry. Creating a euphoria among developers, one is bound to wonder what are the Python Developer skills in order to become a Python developer. Python is used by Intel, IBM, NASA, Pixar, JP Morgan Chase, and a number of other major companies. It is one of the four main languages at Google (along with C++, Java, and Go). Google’s YouTube is largely written in Python. So are Reddit, Pinterest, and Instagram. The most popular are Java, Perl, Scala, Go, C++. There are others, but not as prominent.
Cloud computing is one of the hottest technologies with a high demand for qualified professionals. The median salary for IT pros currently in a cloud computing career in the U.S. is $124,300. However, it is not the easiest of jobs to acquire because it is a specialty area. To secure a job in this field, a candidate must have a number of specific skills. Cloud computing is the provision of computing services on demand, from applications to storage and processing power, typically over the Internet and in a pay-as-you-go system.
Social media is the most popular application of cloud computing. Cloud computing is used by Facebook, LinkedIn, MySpace, Twitter, and many other social networking sites. Social networking sites are designed to find people you already know or would like to meet.
In recent years, Apache Doop has been receiving major developments. Its elements such as MapReduce, HDFS, and HBase, have caught the attention of many employers, and right now recruiters see skills in these areas as ‘hot-cakes’. Even though Hadoop is nearly 10 years old, a lot of software companies still depend on its clusters because Hadoop allows them to perfectly map their results.
Legacy SQL databases such as Oracle and DB2, are increasingly being replaced by modern NoSQL databases including MongoDB & Couchbase. The reason for this transformation is that NoSQL databases are well-equipped with features that allow for storage and access of big data. Added to that, the data-crunching potential of these databases helps complement Hadoop. All in all, it means that data engineers who have expertise in NoSQL are also in high demand.
Looking at the reliability that’s required in big data networks, much work needs to be outsourced to experts who can set up cloud clusters in order to eliminate the hassles. For organizations to be able to accommodate the huge chunks of data flowing into databases, they may have to set up many cloud clusters. The advantage of setting up cloud clusters is that they allow engineers to easily digest vast data and recognize patterns. Therefore, data engineers who can set up cloud clusters are great assets to multinational companies that have eyes for growth.
As a result of the big data revolution, many companies are sitting on mountains of data. Towards the end of 2019, the global big data market value was estimated to be around $31 billion. That figure represented a growth rate of 14% compared to the previous year. However, raw data can be useless to an organization if they lack expert employees to analyze it. Big data engineers who are skillful at data analytics are also valuable to large organizations dealing with large amounts of data.
Before you use data to obtain solutions to questions, you need to ask the right questions in the first place. And this demands you to be a critical thinker. Critical thinking occurs when a person applies rigorous standards of evidence, logical principles, and careful reasoning to the analysis and discussion of issues.
To prosper in data engineering fields, you need to think analytically, much like a data scientist. Data analytics skills are useful for uncovering and synthesizing data connections that are obscure. Although one’s ability to interpret data may be an inborn quality, you can learn to harness your critical thinking power. For instance, you can learn to always think carefully before making conclusions.
To summarize, many companies have now understood the role of big data in their organizations. As data-driven marketing strategies begin to take effect in many business establishments, branding yourself as a skillful data engineer can be a good investment.