What’s the difference between Data Analysts, Data Scientists, and Data Engineers?
Fri 02 March 2018 By Ed Nunes
As the number of people working with data increases, so do their titles and varying responsibilities. While there is no definitive list of responsibilities for a given title, and titles will vary between organizations, this article should give you a general sense of who these data gurus are and what they are likely to be doing in a given organization.
While this list could be longer, let’s look at:
- Business Analysts
- Data Analysts
- Data Scientists
- Data Engineers
- Database Administrators (DBAs)
Business Analysts
Business Analysts will likely be the least technical of the bunch. They will frequently be doing their work in Excel or with off-the shelf Business Intelligence tools like Tableau and others. Often their work-product will come in the form of high-level reports for their internal and external clients. What they lack in technical expertise they make up for in business or domain knowledge. The biggest value a Business Analyst can provide an organization is knowing which questions to ask, even if others may be more skilled at teasing out the answers from the data available. Often, figuring out the right questions is the hardest and most valuable part of an analysis.
Data Analysts
This title is often used interchangeably with Business Analyst, so there’s not much to add. Some organizations will treat a Data Analyst as a more technical role than a Business Analyst, however. Data Analysts may be working with databases and writing SQL queries, performing more complex analyses than Business Analyst would do.
Data Scientists
This tends to be a very technical role, as you might expect from anyone who calls themselves a ‘scientist’. Data Scientists will have a strong understanding of statistics, which informs much of their work. Unlike analysts, they will frequently transform their data to make it work with their Statistical Models. Consequently, they will be working with tools suited for fine-grain data manipulation, such as the R and Python programming languages, SQL, and SAS. Data Science is a broad field in itself: Data Scientists might be working on an estimate of the price of corn in six months, or they may use Machine Learning to build algorithms for driverless cars. With the explosion of interest in Machine Learning, Data Scientists are in high demand and are frequently listed as one of the best careers in America.
Data Engineers
Data Engineers concern themselves with building and maintaining Data Pipelines and ETL (Extract, Transform, Load) processes. Like Data Scientists, Data Engineers will spend much of their time transforming source data. Unlike Data Scientists, Data Engineers will usually be transforming the data to support other people or systems, rather than doing their own analysis.
Another distinguishing feature is Data Engineers’ emphasis on automation: building applications that continuously move and transform data as it is collected. Because of their heavy reliance of programming to automate their work, Data Engineers will usually work with a general purpose programming language, such as Java or Python. They may also work with data processing and pipeline applications such as Apache Spark and Apache Airflow. Data Engineering often involves a lot of work with databases, whether they be traditional RDBMS, or the newer Document Store type databases. Like software developers, a Data Engineer’s output will usually be code, databases, and application infrastructure, rather than reports.
Database Administrators (DBAs)
DBAs are a bit of the odd ball in this group of data workers, as they are the only people in this group that aren’t necessarily involved in any data analysis. DBAs support database systems, making sure they are running properly and performing well. They may spend much of their time tweaking SQL queries, configuring databases, or editing database indexes to improve performance. As databases are an important piece of most data analysis, DBAs could potentially play an important role. However, DBAs are more often used in support of general software applications.
Conclusion
There you have it: a quick rundown of all the data workers you’re likely to encounter. Hopefully the next time you have a meeting with a DBA in it, or find yourself talking to a Business Analyst at a party, you’ll have an idea about what it is they actually do.
Originally published at nunie123.github.io.
