The<\/em> domain of data science has been at the focal point of discussion for quite a few years now and there are no signs of it slowing down. As more and more businesses, organizations, and companies are waking up to the importance of extracting important insights from the pile of data that they are sitting on, the demand for data scientists, data engineers<\/strong>, and other experts in the field has increased significantly. No wonder that while there\u2019s an increased focus on bringing such data science talent onboard, a whole new set of data science titles and roles too have been created to address the needs of the market.<\/p>\n

Recently, a lot has been discussed and written about the differences between various roles in the domain of data science. Among others, the ones that have got the spotlight on them are those that discuss and debate the differences between data scientists<\/strong> and data engineers<\/strong>. If you are wondering what triggers this tremendous interest in these roles, a change in perspective that has been felt over the years could be the driving factor.<\/p>\n

If you step back a couple of years ago, you will find that the predominant focus was on retrieving precious insights from data. As companies and organizations started making data-based and data-driven decisions, which brought several benefits their way, the significance of data management started to sink in the industry \u2013 slowly but surely. This also made the interested parties realize that the quality of data was important to derive useful insights because it\u2019s the principle of “Garbage In, Garbage Out” that works in the domain of data science too. Even if you are capable of creating the best models, your results are likely to be weak and ineffective in case your data isn’t qualitative. And this was what brought the role of the data engineer<\/strong> under the spotlight.<\/p>\n

According to Gartner, merely 15% of big data projects ever make their way into production. According to domain experts, one of the chief reasons behind such failures is due to the inability to build a production pipeline, which is one of the principal tasks of a data engineer<\/strong>. In the modern age of analytics, data scientists get most of the spotlight and attention. However, the roles played by data engineers are equally important, though they are often overlooked. It\u2019s important to realize that data science (and even data analytics) would fail to flourish if no data engineering workbench exists. If you don\u2019t believe it, you can consider what Glassdoor\u2019s records say.<\/p>\n

According to Glassdoor\u2019s data in 2018, the number of job openings earmarked for data engineers<\/strong> was almost five times more than that for data scientists. Elsewhere, one may find data scientist jobs exceeding the number of data engineer<\/strong> jobs though some say it could be because numerous organizations don\u2019t always (or are unable to) draw a distinct line between a data scientist and a data engineer<\/strong>. Thus, they end up posting jobs for the former whereas in reality, the jobs should have been seeking data engineers<\/strong> instead. Such actions on the part of organizations are perhaps triggered by their ignorance of the significant differences between data scientists<\/strong> and data engineers<\/strong>. Many reports have revealed that the majority of organizations require more data engineers<\/strong> than data scientists on their team. So, the question comes to this \u2013 what exactly is data engineering and how\u2019s the role played by a data engineer <\/strong>different from that played by a data scientist.<\/p>\n

Let\u2019s dig a little deeper to answer the questions and find out the differences between data scientists<\/strong> and data engineers<\/strong>.<\/p>\n

1- Who is a<\/strong> data engineer?<\/strong><\/h3>\n

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S\/He<\/em> is a professional with specialized skills in creating software solutions around Big Data.<\/p>\n

Another way of defining a data engineer is that s\/he is an inquisitive, skilled problem-solver, who loves both data and creating things that are useful to others. Thus, along with data scientists and business analysts, data engineers<\/strong> form an integral part of the team effort that converts raw data in ways which offer organizations useful insights and provides them with the much need competitive edge.<\/p>\n

To understand what the role of a data engineer <\/strong>is, it can be said that this professional is someone who builds, develops, evaluates and maintains architectures like databases and large-scale processing systems. In contrast, a data scientist is someone who cleans, organizes, and acts upon (Big) data.<\/p>\n

It\u2019s the job of data engineers<\/strong> to suggest and at times, even implement ways to improve data quality, efficiency, and reliability. To handle such tasks, they need to utilize a range of tools and languages to blend systems together or try to track down opportunities to get hold of new data from other systems, which can help system-specific codes, for example, to act as the basic information in advanced processing by data scientists.<\/p>\n

A data engineer<\/strong> will also need to make sure that the architecture that\u2019s in place is capable of supporting the needs of the data scientists as well as the business\/organization and its stakeholders.<\/p>\n

In order to deliver the required data to the data science team, it will be the responsibility of the data engineers<\/strong> to develop data set processes for data mining, modeling, and production.<\/p>\n

2- Key differences between data scientist<\/strong>s and data engineers<\/strong><\/h3>\n

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With<\/em> respect to skills and responsibilities, you\u2019ll find considerable overlapping between data scientists and data engineers<\/strong>. One of the key differences between data scientists<\/strong> and data engineers<\/strong> is the area of focus. For data engineers<\/strong>, the emphasis is on creating architecture and infrastructure for data generation.\u00a0 On the contrary, the focus of data scientists is on advanced statistical and mathematics analysis on that generated data.<\/p>\n

Though the role of data scientists demands a constant interaction with the data infrastructure that the data engineers<\/strong> have created and maintained, the former isn\u2019t responsible for that infrastructure\u2019s creation and maintenance. Rather, they can be called the internal clients, whose job is to perform high-level business and market operation research to spot trends and relations, which in turn need them to use an array of sophisticated methods and machines to interact with the data and act upon it.<\/p>\n

It\u2019s the job of data engineers<\/strong> to provide the necessary tools and infrastructure to support data analysts and data scientists so that these professionals can deliver end-to-end solutions for business problems. Data engineers<\/strong> are tasked with creating high performance, scalable infrastructure that helps deliver business insights with clarity from raw data sources in addition to implementing complex analytical projects where the emphasis is on gathering, evaluating, managing, and visualizing data along with developing real-time and batch analytical solutions.<\/p>\n

Perhaps you now understand that despite some key differences between data scientists<\/strong> and data engineers<\/strong>, the formers depend on the latter. While data scientists deal with advanced analysis tools like Hadoop, R, advanced statistical modeling, and SPSS, the focus of data engineers<\/strong> remain on the products that support such tools. Thus, a data engineer<\/strong> may deal with NoSQL, MySQL, SQL, Cassandra, etc.<\/p>\n

In a way, you can say that in the data value-production chain, the role of data engineers<\/strong> is akin to the plumbers since they facilitate the job of data scientists, data analysts and other professionals working on the fed of data science. As with any infrastructure, plumbers don\u2019t get the limelight, and yet, they are irreplaceable since nobody can get any work done without them. The same applies to data engineers<\/strong> as well.<\/p>\n

3- Language, tools, and software used by data engineers<\/strong><\/h3>\n

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Due to<\/em> the difference in their skill sets, differences between data scientists<\/strong> and data engineers<\/strong> translate into the use of different tools, languages, and software use.<\/p>\n

For data scientists, common languages in use are Python, R, SPSS, Stata, SAS, and Julia to construct models. However, Python and R are the most popular tools without a doubt. When these data science professionals are working with Python and R, they often resort to packages like ggplot2 to make remarkable data visualizations in R or opt for the Pandas (Python data manipulation library). There are several other packages that can come for them, which include NumPy, Scikit-Learn, Statsmodels, Matplotlib, etc. The data scientist’s toolbox is also likely to have other tools like Matlab, Rapidminer, Gephi, Excel, etc.<\/p>\n

The tools that data engineers<\/strong> often work with include Oracle, SAP, Redis, Cassandra, MongoDB, MySQL, PostgreSQL, Riak, neo4j, Sqoop, and Hive.<\/p>\n

Languages, tools, and software that both the parties have in common are Java, Scala, and C#.<\/p>\n

One of the key differences between data scientists<\/strong> and data engineers<\/strong> emerges from the emphasis given on data visualization and storytelling, which gets reflected in the tools these professionals put to use, some of which are mentioned above.<\/p>\n

4- When organizations get the roles wrong<\/strong><\/h3>\n

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As<\/em> mentioned before, several organizations fail to distinguish the key differences between data scientists<\/strong> and data engineers<\/strong> and often task the former with the job that the later is specialized to do. For example, asking data scientists to create a data pipeline, which is the job of a data engineer, <\/strong>would mean making the former function at just 20-30% of their actual efficiency.\u00a0So, it becomes important to know the differences between data scientists<\/strong> and data engineers<\/strong> and hire each for roles specifically designed to match their skill sets.<\/p>\n