The buzz around data engineering, data science, and data analysis seems to be growing each day. Terms like AI, big data, and cloud are trending and getting more internet by the minute. In 2012, the Harvard Business Review crowned data science as the “sexiest job of the 21st century.” Since then, the number of job postings related to this field has been multiplying each year. But what does the situation look like when you’re the one who wants to implement data engineering in your company? Let’s find out!
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When it comes to data engineering, you have two options. You can either hire a full-time specialist or find yourself an external company. For many organizations with no experience with data whatsoever, it’s usually beneficial to pick the latter option. With a decent data engineering company, you can quickly achieve your goals and better understand processes within your company.
This article will show you some of the essential elements you ought to pay attention to if you want to find a reliable data partner.
Difference between data engineering, data analysis, and data science
Although the three roles vary from one company to another, there is a big difference between them. Data engineering is a set of operations that aim to transport, transform, and store data. Think of data engineering as a specific technical field. On the other hand, data science involves analyzing and interpreting data, while data analysis uses data to help companies make better decisions. Data engineers act as an intermediary between analysts and scientists. If a business is serious about working with data, it needs a set of these three skills.
Roles Of Data Engineers
Below are three primary roles that data engineers play:
- The generalist role: Is responsible for all data processing steps, from management to analysis, most often for small businesses that do not have large volumes of data.
- Pipeline-centric: They involve working with data scientists to help make sense of collected data, mainly in mid-sized companies.
- Database-centric: They sit at the interface of data science and engineering and combine mathematical models with artificial intelligence (AI), mainly for large organizations.
Qualities of an exemplary data engineering company
The abundance of data engineering vendors in the market makes it challenging to pick the right one to partner with. Here are some of the qualities worth paying attention to get a head start in your search for the best service provider:
Moving data loads to the cloud and modern data platforms such as Spark and Hadoop does not demand a shift in thinking for a data engineer. A basic understanding of the distributed computing concept is all that is required in this space. However, data velocity, volume, and variety take the right kind of tools to tame. A qualified professional knows which types of technology platforms are suitable for solving which kinds of problems. Therefore, it is essential to perform a background check to determine the level of skills possessed by employees.
Here are some of the basic skill sets to look out for in descriptions of top data engineering companies:
- The ability to work with programming languages: Java, Python, Scala, SQL databases, and Apache Airflow.
- Excellent understanding of ETL tools used in data integration.
- Undisputed understanding of how data warehouses and data lakes work.
- The ability to deploy machine learning algorithms is majorly considered a data scientists’ domain, and therefore finding a proficient provider in this field is a plus.
Experience determines the quality of services that a given company provides. Data engineering can be deeply understood or known only from the surface, just like any other field of expertise. Inexperienced vendors might deliver the one-size-fits-them-all type of solutions that are slightly polished and fit a specific case. In contrast, experienced ones can outthink any challenge and promptly crack the problem at hand.
IT consultant in the data engineering company
Experience is subject to how long a company has been in operation and its many tasks. For instance, a company that has been in business for 20 years and completed 200 projects is less experienced than that which has been in operation for ten years and has completed 500 projects.
Checking for the relevant expertise is also essential. A company that has worked with a similar business as yours is more likely to provide valuable data guidance since they know the pain points and have viable solutions to the potential problems.
A data engineering provider worth working with should have a solid track record. It should be able to provide high-quality evidence and testimonials of tenure in the industry. Case studies are good indicators of credibility. They show the company’s background, technology, and data architecture used to achieve respective outcomes. It is also a good idea to contact previous clients to evaluate their level of competence.
Reputation also goes all the way to the company’s standing in the community: Whether it is regarded as a good employer or contributor in charity events. Ensure to conduct a background check on Google and social media to dig out all the skeletons in the closet.
An excellent data engineering firm should exhibit the same traits as any other good problem-solver. It should look at issues from different perspectives to understand what they are supposed to be doing before reaching out to the toolbox. Problem-solving extends to risk analysis and project improvement. An A-list service provider should factor in all the possible risks at the beginning of a model and make possible improvements to their services to guarantee data quality. Finding a good problem solver involves looking for contradictory skill sets: The technical intelligence of creating applicable models and an intuitive understanding of the problem they are solving.
Despite a rapid increase in computing power and data access over the past years, the ability to use data in the decision-making phase has not yet been fully maximized. Therefore, a viable data engineering company needs to understand the needs of a business and develop systems that match those objectives. They need intuition to know when the data architecture designs in place are stale and need to be replaced to respond to an evolving business environment. It is good to have a partner that can speak the business, data analysis, and data science languages to bridge the three units.
Many factors go into determining the right data engineering company to work with. There is a need to combine intrinsic characteristics and learned skills and an excellent approach to project implementation. It is essential to make the right hiring decision since poor data quality is pegged as a source of inaccurate analytics and ill business strategies. Having a clear project goal and understanding the kind of expertise needed is paramount to help in making the right choice.