Let’s paint a happy picture.
Things are progressing nicely at your organization. You’ve got a strong bank of existing customers whose business you can grow. You’ve got a healthy level of interest from the visitors to your website, enquiries, and trials. You’re regularly finding great leads and you’ve got some good ideas for more lead generation. And you’re consolidating your relationships with your partners, potential new partners, your channels, suppliers, and so forth.
That means there’s one Hell of a lot of data running through your organization. Capture it all, analyze it precisely, and interpret it right, and you’ve got a precious resource that could really build your business. You just need the right person to make this happen. So, given the choice, which analytics job title should you choose: A data engineer or a data scientist?
It’s a welcome problem to have, but it’s a tricky choice, because in some respects there’s a fine line between the two, so here’s a useful guide to help you decide.
Digging Deep For Data Diamonds – The Data Engineer
Data’s like diamonds. You need to find it, extract it, then refine and polish it to maximize its value.
First comes extraction. That’s the job of the data engineer. The engineer is the data prospector and miner, responsible for finding opportunities to acquire data and then developing, constructing, testing and maintaining the ways to extract, model, and produce the raw data. Here, we’re talking about architectures like databases and large-scale processing systems.
Engineers seek to dig out the data from all the sources available to your organization, then integrate, manage, and optimize it. The data’s likely to be unformatted, perhaps unvalidated and may contain errors and codes that are system-specific. Their key task is to find it, retrieve it, ensure that it’s easily accessible, and build the infrastructure that facilitates data generation. Sometimes engineers recommend and implement ways to improve this process and enhance data reliability, efficiency, and quality.
An engineer’s focus is on the data pipeline: building it and ensuring it’s efficient, so that there’s a free flow of data that serves the needs of the data scientists and the business. With all this in place, and having unearthed the data, the work is handed over to the data scientist.
Refining Raw Materials Into Valuable Insights – The Data Scientist
If the data engineer is like the diamond miner, the data scientist is like the diamond cutter. They take the raw data and polish it into valuable insights that fit your business and meet your specific needs. The data scientist is mostly focused on the relationships of the data within an organization’s database. They use their skills to compare data, and perform statistical analyses on the data, which provide insights.
These precious gems — the insights — will then be used to enhance your business by creating a better understanding of your organization and your customers. This is usually done when the data scientist hands their findings and insights to a business analyst. While the data scientist collects and analyzes data to discover what creates and drives trends, the business analyst focuses on identifying and presenting these trends to business stakeholders and decision-makers in ways that all of them can understand, not just data experts. To extend our analogy, if the data scientist is the diamond cutter, then they pass the material on to the last expert in the chain – the jeweler (business analyst) – to create something valuable for a non-expert audience. They enable their business colleagues to visualize findings, trends and patterns based on their analysis. So, acting as a key intermediary between the data engineers and business analysts, the data scientist combines technical expertise with business understanding.
Thanks to the efforts of the data engineer, the data scientist gets a large volume of raw data from the widest array of sources with which they can answer business needs. Using analytics programs, machine learning and other methods, the data scientist designs algorithms to collect, clean, manipulate, organize and analyze data in order to reveal insights that will be useful for their business or stakeholders.
Often with a background in advanced mathematics and/or statistical analysis, data scientists conduct high-level market and business research to help identify trends and opportunities, and then, to summarize, these findings are presented by the business analyst to the business and stakeholders in a manner that aids decision-making.
What Are The Tools For These Roles?
According to Glassdoor and TechRepublic, data engineers work heavily with a wide range of big data tools for data structuring, management, storage and transfer such as Hadoop, Spark, Kafka, MySQL, Redis, Riak, PostgreSQL, MongoDB, neo4j, Hive, and Sqoop. They also use data pipelines and workflow management tools such as Azkaban, Luigi, and Airflow and with relational SQL and NoSQL databases like Postgres and Cassandra.
As more and more data warehousing moves to the cloud, engineers increasingly find themselves working with AWS cloud services, EC2, EMR, RDS, and Redshift, other cloud-based data warehouses such as Snowflake and Google BiqQuery, cloud computing services like Microsoft Azure, and data orchestration systems such as Kubernetes.
Plus, an understanding of machine learning and AI is becoming more important, as software engineers start to work with neural networks, and data engineers will need to prepare data pipelines to feed these neural networks.
Typically, data engineers use Python, R, Java, C++, and Scala programming languages.
Similarly, data scientists use Python and R, Scala, Java, and C++, although Scala is more popular with data engineers because the integration with Spark is especially handy to set up large ETL flows. Java is also used more heavily by data engineers, although its popularity is growing amongst data scientists. Data scientists use languages such as SPSS, SAS, Stata, and Julia to build models; also Matlab and F#.
When using Python, data scientists may elect to use the software machine learning library Scikit-learn, the numerical and scientific libraries NumPy and SciPy, plotting library Matplotlib, and statistical data exploration package Statsmodels, among others.
Tools that both have in common are the distributed data/computing tools such as Hadoop, Hive, Storm, Gurobi, MySQL, and Spark, plus cloud services like AWS.
Have They Got The Skills to Pay The Bills?
With all these tools at their fingertips, you want to be sure that your choice of data scientist vs data engineer has truly got the chops to use them to your best advantage.
It almost goes without saying that both roles require a background in computer science, but that’s where they might diverge and where you can identify what role you need to fill, by pinpointing the skills you want.
Data Engineers are inclined to have a more technical background including computer engineering in particular. The key considerations for data engineers are that they have experience with big data, a variety of databases, cloud data solutions, and with extracting value from and processing large and disconnected data sets. They should understand code and script and have system monitoring, alerting, and dashboarding experience
Data scientists may have different or more business-focused studies behind them, such as econometrics, mathematics, statistics, and operations research. They tend to come into the industry from a variety of backgrounds, mostly scientific in nature, or from areas such as web development and database administration, which makes them already aware of some of the skills and challenges involved in the role.
Data scientists usually have a richer mathematical and statistical experience, more hands-on knowledge of data modeling and machine learning, and a track-record of visualizing and presenting data-driven insights, because an important element of their skillset needs to be the ability to convey technical findings to a non-technical audience
A Winning Combination
The abilities of the data engineer vs data scientist have a significant influence on which of these specialists you choose. Each plays a vital role in maximizing the benefits you can derive from your data.
Ideally, of course, you’d have the volume of data and the breadth of opportunity to employ them both, because the hand-over from data engineer to data scientist is a powerful one. It makes for a winning combination that will get you the best insights to drive your organization forward.