With over 50 billion connected smart devices collecting, sharing, and analyzing data, it is undeniable that Data Science is ruling over the world and is here to stay. Not just this, big data is big money too. The industry is expected to generate over 274.3 billion U.S. dollars by 2022. Simply put, data science obtains large amounts of data from the internet and other smart devices and makes use of modern scientific methods, algorithms, processes, and systems to analyze this and predict customer behavior. This information is then leveraged in making major business decisions for growth and increased revenues.
The incredibly large industry has multiple people performing different tasks, right from cleaning data, implementing predictive models, to creating comprehensible business strategies. While data scientists are the most sought-after in this industry, there are many other stakeholders involved in generating the data, such as data analysts, data architects, and data engineers. On average, data scientists are earning more than the typical software jobs, with an annual income of $1,13,000 annually. While the salaries of data architects and data engineers range from $103000 to $108000 respectively.
Data engineers are the foundation for the data science industry as they convert the raw data into a useful format for the data scientists. They also find the trends in data sets that are in turn used to convert raw data. While a data scientist is more concerned with the end-user, a data engineer is the one interacting in the back end to collect vast amounts of data. Typically, a data engineer is concerned with building pipelines that convert the data into formats that data scientists can use.
The three major categories of a data engineer based on the company size and roles are as follows:
Typically, generalists are found in small teams or companies where their role is broad. They are the one-man-army for data and are responsible for every step of the data process, from streamlining data, managing it, to analyzing it from time to time. Since these companies do not have numerous users, the systems architecture knowledge required for this role is also less.
Pipeline-centric data engineers are found in mid-sized companies and convert huge data into a useful format for analysis. They are usually required in companies with complex data needs and work closely with data scientists. A pipeline-centric data engineer is expected to have an in-depth knowledge of computer science and distributed systems.
Database-centric engineers are responsible for setting up and populating the analytics of databases. They go beyond creating pipelines and adjust the database into bite-sized formats for quicker analysis. They are concerned with ETL (extract, transform, load) work and creating table schemas, and are required in large companies with data distributed across databases.
Some common responsibilities of a data engineer include:
Data engineers are architects at heart working on large-scale systems or huge amounts of data. Technical knowledge of software such as Apache Hadoop, NoSQL, Apache Spark is highly in demand today. Expertise in setting up cloud clusters and machine learning is also highly beneficial for aspiring data engineers.
Without data engineers, data scientists will not function, making them a critical-first member of the data science team. It was found that bad data is costing US businesses alone $600 billion annually, which shows the growing need for organized data – and data engineers are vital for this process. If data is something that excites you, a variety of online courses, right from boot camps to introductory practice modules are available to get you started.
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