Business and technology leaders like IBM, Gartner, and McKinsey, in partnership with institutions like the Business Higher Education Forum, have reported since 2017 the urgency for companies in the implementation of data science, including engineering and analytics.

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Artificial Intelligence, Programming Skills, Machine Learning, and Deep Learning continue to establish themselves as some of the most relevant skills in the business.

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However, for companies and professionals interested in the field, choosing among the different roles proves to be challenging: data scientists, engineers, analysts, and architects are some of the leading job trends in the ever-changing world of Data Science. 

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Below we focus on the role of a data scientist versus a data engineer, a frequently confusing comparison, how they differ in duties, skills, job description, and responsibilities, which are the essential tools and technologies involved, and the value that each role provides for businesses worldwide.

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What's a Data Engineer's main objective, and what do they bring to the table?

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The role of a Data Engineer consists of preparing the data infrastructure for analysis, focusing on raw data production readiness, and crucial aspects such as data resilience, scaling, storage, formats, and security.

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Building the infrastructure and architecture that enables data generations is one of the primary tasks of a Data Engineer, along with designing, building, integrating, and optimizing data from various sources.

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Enabling real-time analytics by building free-flowing data pipelines, and combining multiple big data technologies, is the main focus of Data Engineers, ensuring through the design and construction of complex queries that data is easily accessible.

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Data engineers are strong coders who enjoy learning and using new technologies to discover unique ways of making systems more efficient in dealing with the infrastructure and architecture that stores and organizes data, thriving on helping an organization save time and resources.

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Data engineering translates into organizing data to make it easy for other systems and people to use, allowing the articulation with many different consumers of data, such as:

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• Data analysts answer specific questions about data or build reports and visualizations so that other people can understand the data more efficiently.

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• Data scientists answer more complex questions than data analysts, including predictions based on statistics and probability.

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• Systems architects are responsible for pulling data into the applications they build. 

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• Business leaders need to understand the data to decide how others will use it or how it will affect their behavior.

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Working with each of these teams and understanding their specific needs is vital for Data Engineers, whose responsibilities include:

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• Gathering data requirements means understanding how long the data needs to be stored, how it will be consumed and used, and what roles and systems need to access it.

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• Maintaining metadata refers to "data about the data," such as the technologies involved in managing the data, the schema, the size, how it is secured, the source, and the data's ultimate owner.

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• Ensuring security and governance using centralized security controls and integrations like LDAP, correctly encrypting the data, and auditing access to the data.

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• Storing the data means that specialized technologies are needed depending on the type of data, the frequency, and type of consumption, such as relational databases, NoSQL databases, Amazon S3, Hadoop, and Azure blob storage.

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• Processing data for specific needs, implementing tools that access, transform and enrich data from different sources, and summarize and store it in the storage architecture.

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Following are the primary tasks performed by data engineers to address these responsibilities.

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It's common for most or all to be used for any data processing job.

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• Acquisition: obtaining the data from various and diverse systems.
• Cleansing: Detecting and solving inconsistencies and errors.
• Conversion: Translating data from one format to another.
• Disambiguation: Interpreting data that has multiple meanings.
• De-duplication: Removing duplicate copies of data.

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Differences between Data Engineers and Data Scientists or Data Analysts

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Until recently, data engineering tasks were usually performed by data scientists. But with the continuing growth of the field, gathering and managing data has become more complex.

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Organizations shifted to splitting these responsibilities into two, expecting more answers and better insights from the collected data.

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The main difference is that data engineers build and maintain the systems and structures that store, extract, and organize data.

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Meanwhile, data scientists analyze that data to predict trends, glean business insights, and answer questions relevant to the organization.

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While a data engineer's role is to build and optimize systems that make a data scientist's job possible, the latter find meaning in the troves of data that data engineers manage.

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It helps to think of both data professionals' roles as complementary. 

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Job postings or requirements often present confusion or misleading descriptions regarding what a data scientist does or what the company needs to solve its data concerns.

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Furthermore, the same required qualifications like Python and SQL commonly appear for both data science and engineering postings.

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Because these two roles are continually used and molded, the line between a data scientist and a data engineer is often obscured.

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However, a data scientist's role differs from that of a data engineer. A data scientist's primary concern is cleaning and analyzing data, answering business questions, and providing calculated metrics to solve business situations.

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On the other hand, a data engineer develops, tests, and supports data infrastructure and architecture, which the data scientist requires for analysis.

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Data Engineers lay the groundwork for Data Scientists to provide accurate metrics. Data engineering and data science are complementary.

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Data engineering makes data scientists more productive, allowing them to focus on what they do best: performing analysis. Without data engineering, data scientists spend most of their time preparing data for analysis.

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Following is a more in-depth look at both roles in the Data Science field:

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Data scientists' primary technologies are machine learning and data mining; while using tools like R, Python, and SAS to powerfully and efficiently analyze data.

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These technologies allow data scientists to communicate their insights using charts, graphs, and visualization tools, enabling them to explain their results to technical and non-technical audiences.

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These tools require the data to be ready for analysis and gathered together in one place. 

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Data engineering works with data scientists to better understand their specific needs for a job; building and maintaining data pipelines and infrastructure uses tools like SQL and Python to make data ready for data scientists.

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These data pipelines source and transform the data into the structures needed for analysis and must be well-engineered for performance and reliability, which makes software engineering best practices and a robust understanding of software engineering a necessity, along with monitoring and logging to ensure reliability and better support.

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The large datasets used and demanding SLAs by Data Scientists teams require Data Engineers to design for performance and scalability to ensure that data scientists can consume data reliably and consistently.

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Because data projects are enormously complex and time-consuming, only 1 in 10 data science projects makes it to production.

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This complexity means data scientists and engineers must work together, requiring their total contribution with each skill set equally essential and having a future in technology fields and companies. 

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Projects fall through the cracks for several reasons, but often the data team cannot get past the production pipeline phase due to cumbersome data sets.

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Without data engineers to build robust and reliable architecture, data scientists cannot efficiently analyze data.

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Projects become delayed and sometimes dropped altogether when teams run out of time and money.

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Are Data Engineering and Data Scientist roles interchangeable? 

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The short answer is yes; many of the skills needed for both overlap, like working with data pipelines or knowledge of programming languages; this means that specialists from both professions are skilled enough in the essential knowledge and vocabulary to transition from one role to the other.

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However, data engineers have a greater focus on the architecture and infrastructure that supports the work of data scientists.

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In contrast, data scientists are more concerned with developing and testing hypotheses and answering business needs through data; both professions would have to complement additional skills before making the leap.

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Primary skills and technologies involved in Data Engineering

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Software engineering experience and proficiency in programming languages including Python, Scala, Java, and SQL are some of the most common backgrounds for Data Engineers.

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At the same time, degrees in statistics or mathematics contribute to solving problems through varied analytical approaches.

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Most data engineering teams consist of members with a bachelor's degree in computer science, information technology, or math, with complementing data engineering certifications by industry leaders like Google's Professional Data Engineer or IBM Certified Data Engineer.

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Experience building and maintaining big data warehouses capable of running ETL (Extract, Transform and Load) routines on big data sets is also valuable. Specialized tools are employed to work with data, as each system, architecture, and business introduces different challenges.

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It's critical to consider how data is stored, modeled, secured, and encoded while understanding the most efficient ways it can be accessed and manipulated.

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"Data Pipeline" refers to how Data Engineers understand the complete data engineering process, each usually having multiple sources and destinations.

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Data undergoes several transformations, enrichments, validation, summarization, or other processes within the pipeline.

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Data engineers create these pipelines with a variety of technologies such as:

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• ETL Tools. Extract Transform Load (ETL) is a technology category that moves data between systems. These tools access data from many different technologies and then apply rules to "transform" and cleanse the data so it is ready for analysis. Examples of ETL products include Informatica and SAP Data Services.

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• SQL. Structured Query Language (SQL) is the standard language for querying relational databases. Data engineers use SQL to perform ETL tasks within a relational database. SQL is handy when the data source and destination are the same database types. SQL is very popular and well-understood by many people and supported by many tools.

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• Python. Python is a general-purpose programming language. It has become a popular tool for performing ETL tasks due to its ease of use and extensive libraries for accessing databases and storage technologies. Python can be used instead of ETL tools for ETL tasks. Many data engineers use Python instead of an ETL tool because it is more flexible and powerful for these tasks.

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• Spark and Hadoop. Spark and Hadoop work with large datasets on clusters of computers. They make it easier to apply the power of many computers working together to perform a job on the data. This capability is critical when the data is too large to be stored on a single computer. Today, Spark and Hadoop are not as easy to use as Python, and many people know and use Python.

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• HDFS and Amazon S3. Data engineering uses HDFS or Amazon S3 to store data during processing. HDFS and Amazon S3 are specialized file systems that can store unlimited data, making them useful for data science tasks. They are also inexpensive, which becomes essential as processing generates large volumes of data, and managing costs is crucial. Finally, these data storage systems are integrated into environments where the data will be processed, making managing data systems much more straightforward.

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New data technologies emerge frequently, often delivering significant performance, security, or other improvements that let data engineers do their jobs better.

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Many of these tools are licensed as open-source software.

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Open source projects allow teams across companies to collaborate on software projects easily and to use these projects with no commercial obligations.

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Since the early 2000s, many of the largest companies specializing in data, such as Google and Facebook, have created critical data technologies released to the public as open source projects.

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Why are Data Engineers currently in such high demand?

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As companies become more reliant on data, the importance of data engineering continues to grow. Since 2012 Google searches for the phrase "data engineering" have tripled, and job postings for this role have also increased by more than 50%. Just in the past year, they've almost doubled.

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There's more data than ever, and data is growing faster than ever. 90% of today's data has been created in the last two years.

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Data is more valuable to companies, and across more business functions—sales, marketing, finance, and others areas of the business are using data to be more innovative and effective.

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Most companies today create data in many systems and use various technologies for their data, including relational databases, Hadoop and NoSQL.

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Companies are finding more ways to benefit from data. They use data to understand the current state of the business, predict the future, model their customers, prevent threats and create new kinds of products. Data engineering is the linchpin in all these activities.

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As data increases in complexity, this role will continue to grow. And as the demands for data increase, data engineering will become even more critical.

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Data engineering is not commonly an entry-level role; many data engineers get their start in software engineering, business intelligence, or systems analytics roles that give them experience with the systems and infrastructure crucial to the data science field.

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Wrap Up

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Does Your Business Need Data Engineering? The short answer is "Yes!" Companies of all sizes have extensive amounts of disparate data to comb through to answer critical business questions.

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Data engineering is designed to support the process, making it possible for data analysts, scientists, and business executives, to reliably, efficiently, and securely consume all available data.

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Is your company interested in data engineering to analyze big data to understand and answer business questions? Reach us out here!