In the times when data-driven organizations are making the headlines, data engineering may be just what you need to make your organization stand out and outshine competitors. Detailed and insightful data analytics help make the right decisions fast and be one step ahead of others.
However, there’s much more in store and there are many more data-related tasks software engineers or information technology specialists can assist companies with, helping them achieve their goals and meet their business needs.
But what should you know about data engineering in 2022 to make the most of it? Let’s see.
Table of contents:
- Data engineers, data scientists, data analysts…
- Data warehouses in a nutshell
- Data engineering jobs and career opportunities
- Data engineering boiled down
Data engineers, data scientists, data analysts...
Data scientist, data analyst, data architect, analytics engineer, big data engineer, machine learning engineer, and database architect are some of the positions that may be, to some extent, associated with data engineering. Database architects and business analysts are some other names that are often mentioned alongside, too.
Every position has its responsibilities, tasks, and duties but, still, disambiguation may be needed as the number of data analysis roles makes things a little chaotic and complicated. How can we define a data engineer and data engineering, then?
A data engineer is a professional who constructs, tests, and maintains the systems for large quantities of data storage and extraction – for further reference. And data analysts or data scientists are next in line to analyze and interpret the data gathered, uncover trends, and explain the patterns.
The bottom line of data engineering – „a software engineering approach to designing and developing information systems”, according to Wikipedia – is optimizing processes and increasing the company’s performance.
Some of the data engineering tasks include:
- data modeling
- data visualization
- data processing
- data governance
- database administration
Data warehouses in a nutshell
What data engineers often make use of is data warehousing. In short, a data warehouse is a „central repository of information that can be analyzed to make more informed decisions”. Data warehouses are known for storing terabytes or petabytes of processed data (both structured and unstructured), accumulated over time and formatted for a given purpose.
Data warehouse architecture – which defines the way data in different databases are arranged – comprises several tiers or layers, with their number depending on the model adopted. As for data warehouse components, typically there are four:
- ETL (extract, transform, load)
- metadata
- central database
- access tools
Data pipelines are also needed to make the whole process work properly, serving as means of moving data from disparate sources to a data warehouse or another target repository. They can be called sequences of actions, tools, and processes or simply a series of data processing steps.
What data engineers also make use of in terms of storing and accessing big data is a data lake, known for keeping raw data. Another interesting data storage solution is a data mart, created for particular departments or business units, like marketing or sales. An example of a popular data engineering solution is Snowflake architecture – available within the SaaS model.
Data engineering jobs and career opportunities
Data analysts, data architects, or data engineers are in high demand these days, just like data engineering skills and experience. And labor statistics reflect it, also when it comes to the compensation offered.
Data engineers are one of the best-paid developer roles, Stack Overflow Developer Survey 2022 suggests. As for median salary, they can count on $79,983 worldwide and $150,000 in the US. This is an interesting fact, especially if we take into account that a data engineering role requires one of the lowest average years of professional coding experience (11.17).
Data engineers work in a variety of settings, places, businesses, and institutions, including statistical offices, academic institutions, and research laboratories. On top of that, the corporate environment, and business in general, makes use of the big data analysis and data engineering services which cover data quality checks, data discovery, and batch data processing, to name but a few data engineering use cases.
But coding skills and a bachelor’s degree or even master’s degree in data engineering may not be enough to succeed in the IT job market. Developers and engineers need to adapt to business requirements promptly, meet novel data analysis tools regularly, and acquire new skills fast.
Data engineering boiled down
Big data and data engineering are on the rise, with specialists in this area highly demanded in the job market. However, a switch in the scope of responsibilities in this field may be observed soon. This way, a new job title – the data reliability engineer that makes sure data is trustworthy and available on time – may appear soon.
Data warehousing, building data pipelines, business intelligence engineering, computer science, relational databases, and operational data are what data engineers often deal with to analyze data on the fly and accurately, which is the heart and the ultimate goal of data engineering.
Data-driven innovation that involves top-tier data storage and data security, as well as machine learning and deep learning are all in the cards here, too. And although factors such as powerful tools and technologies, or access to a cutting-edge data warehouse do matter, it’s skilled data engineers that can make a difference, empowering businesses of all kinds and letting them spread their wings.
If you, too, would like to benefit from the most-cutting edge data engineering in the market and see your company grow, contact us now and find out what the most recent developments in data science, big data, or research & development are.
