Many businesses today have a vast source of information at their disposal. Every time someone clicks on their profile or purchases a product, a new avenue of data is generated. There is a need for skilled professionals who can analyse this data and bring forth useful information to the business. There is a need to refine the data to make it useful. This need has created a huge demand for professionals in the data engineering and data science field. But how do you decide which is better among Data Engineering vs Data Science?
Both these fields may seem similar but they are very different from one another even though both deal with data. Hiring the wrong profile can stall a company’s data initiatives and drain their budget. Let us analyse the core differences between these two fields and the skills relevant to different kinds of businesses.
What do Data Engineers do?
Data engineers have to construct and manage data pipelines that move raw data from the source and transform it into useful format. This transformed data is used by businesses to make useful business decisions that boost profits and help the business grow.
What do Data Scientists do?
Data scientists interpret the data given by data engineers by using statistical models and advanced analytical tools to predict future trends from the data. This helps businesses solve many complex problems and gain useful information from the available data.
What are the Relevant Skills for Data Engineers?
You need professionals who excel in the following technical areas if you want to build a robust data foundation. These are the skills necessary for data engineering careers today:
- Data Pipeline Development: Data engineers extract, transform and upload data into useful data pipelines. The scripts that they write are responsible for cleaning up text and filter out duplicates before the data lands in a database.
- Database Management: These professionals design optimal storage systems. These systems include traditional databases and modern databases used for unstructured data.
- Big Data Technologies: Data engineers use distributed computing frameworks when there is a huge data volume to process in real time. They use modern tools like Apache Spark, Apache Hadoop and Apache Kafka for this task.
- Cloud Data Warehousing: Modern businesses use cloud technology these days so that their data engineers can use cloud warehouses to store and access data without any leaks.
- Coding Proficiency: These professionals also write highly optimised and production scale code by using programming languages like Python, Scala and SQL to write this code.
What are the Relevant Skills for Data Scientists?
Businesses look for professionals who possess the following capabilities to unlock the potential of their data:
- Advanced Statistics and Mathematics: Data scientists use linear algebra and probability theory to validate their analysis.
- Machine Learning and AI: These professionals are able to build predictive systems by using modern machine learning and artificial intelligence frameworks for different businesses.
- Data Mining and Data Analysis: Data scientists have to analyse the data before they build a model to make accurate data analysis in the long run.
- Data Visualisation: Data scientists need to use various tools to turn complex maths into clear business reports that are easy to understand.
- Programming and Scripting: They rely on popular programming languages because they have to use programming language environments specifically built for data analysis.
What Kind of Businesses Need Data Engineers?
Businesses need data engineers if:
- The data lives scattered across five different software platforms and nobody can access a unified view
- The business reports take a long time to run or may crash frequently because of large data volumes
- The data analysts end up spending a lot of time manually cleaning Excel sheets instead of analysing them
Such businesses need to hire a data engineer to centralise and automate their pipelines.
What Kind of Businesses Need Data Scientists?
Businesses need data scientists if:
- A business already possesses a clean and centralised cloud data warehouse with rich historical data
- A business wants to automate complex decisions like setting dynamic prices or predicting customer behaviour
- A business needs to build AI-driven features directly into their software product
Such businesses already have the raw framework ready. They just need to hire someone who has completed a data science course to extract competitive advantages from their stable data streams.
How to Avoid the All-in-one Trap?
Many businesses make the critical mistake of trying to hire a professional who has completed a data engineering course and expect them to perform the role of a data engineer and a data scientist. Such an individual will rarely exist in the job market.
Instead of chasing such an individual, businesses must invest in a complementary team. A single data engineer can often unblock the work of multiple data analysts and scientists. This creates a massive multiplier effect across your entire organisation. It also avoids employee burnout and gives more benefits to the business in the long term.
Conclusion
Data engineering and data science are roles that complement each other. One cannot succeed without the other. Businesses need both professionals at different times in their growth journey to optimise their business operations in the right direction. You must understand the needs of the organisation before deciding which professional needs to be recruited at which specific period in time.
Frequently Asked Questions (FAQs)
1) What is the difference between data engineering and data science?
Data engineers have to build and maintain systems that store or move data and data scientists have to analyse such data to provide valuable business insights.
2) What skills does a data engineer need compared to a data scientist?
Data engineers need to be skilled in computer programming to understand cloud platforms, whereas data scientists need to be skilled in applied maths and statistics.
3) Which is better for a career: data engineering or data science?
Neither career is better than the other because both have ample career opportunities and the right career for you will depend on your interests and future goals.
4) Why do modern businesses need both data engineers and data scientists?
Modern businesses need data engineers to design and build data structures that make it possible for data scientists to analyse the clean data and drive positive business decisions.