The data analytics world is vast, comprised of multiple roles like data analysts, engineers, scientists, and architects. Data is the buzzword these days, and from self-driving cars to Facebook algorithms, data scientists are donning different hats daily.
This is for a simple reason: a well-skilled data scientist adds incredible value to a business.
Data scientists juggle many responsibilities, but even their roles are incomplete without data engineers. Most organizations store information in various formats, including databases, text files, and other varied formats. This is where data engineers step in with their data manipulation skills.
Data engineers build pipelines to transform existing and new information into usable formats, which data scientists can use to run statistical algorithms. To understand the difference between a data scientist and a data engineer, let’s use a simple analogy.
Imagine there are two sets of people: a race car builder and a race car driver. The latter is the recipient of the crowd’s cheers since he is the one hurtling along the raceway. On the other hand, the builder gets thrills from tuning engines, fixing different exhaust setups, and creating a powerful, well-oiled racecar.
If you prefer working behind the scenes and building things—like in the scenario above—you might consider becoming a data engineer.
Data Scientist vs. Data Engineer
|Data Engineers||Data Scientists|
|Functionality||Data engineers build, maintain, and run the systems which allow data scientists to carry out their day-to-day functions||Data scientists build, train, and oversee the usage of predictive models by using data. The analysis is further communicated to managers and other executives.|
|Responsibility||This role includes creating data models, crafting and designing pipelines, and overseeing ETL activities||Formulate the questions derived from existing data sets, explore potential problems and examine hidden patterns within trends. These findings need to be communicated to the stakeholders regularly.|
|Requirements||Bachelor’s degree in computer science, math, or IT. Some additional qualifications include: Python, Java, C++, Scala, SQL||Master’s Degree, Ph.D., research-oriented, an advanced degree in mathematics, statistics, computer science, or engineering|
|Salaries||Depending on the experience, role, and location, the average salary is around $142,000||Depending on the skills, experience, role, and location, the average salary is around $139,000|
What does a data engineer do?
Data engineers are primarily responsible for finding data trends and further developing algorithms to make raw data useful to the business. This role requires significant technical skills, including deep SQL knowledge, database design, and access to multiple programming languages.
Technical skills and programming skills alone can’t get data engineers this far. For this very reason, they also need good communication skills to understand the business’s requirements, so that the right level of data analysis is available with raw data sets.
A data engineer role is split into three categories:
- Generalist: Generalists exist in small teams and companies. Usually, generalists tend to wear many hats as they continue to be “data-focused” people. Generalists are usually responsible for every step of the data process, ranging from managing data to analysing it. If a person is looking to transition from data science to data engineering, then it is ideal for smaller companies to scale on a low level.
- Pipeline-centric: Such profiles are usually found in midsize companies. People in this profile work alongside data scientists, and they need to possess in-depth knowledge of distributed systems and computer science.
- Database-centric: In large enterprises, managing data flow is a full-time job and requires a lot of time and effort. Data engineers often focus on maintaining analytical databases, and they work heavily on data warehouses, including multiple databases and table schemas.
Why are data engineers important to a business?
In 2020, a data engineer job was listed as the eighth fastest-growing job, per LinkedIn’s emerging job report. Here’s why modern age companies need data engineers:
1. Modern companies are data-driven.
Gartner’s 2019 report reported that data continues to be a key driver in a company’s growth: “Data and analytics are the key accelerants of an organization’s digitization and transformation efforts. Yet today, fewer than 50% of documented corporate strategies mention data and analytics as fundamental components for delivering enterprise value.”
It’s safe to assume that companies need to be data-driven, but not all are equipped to make good use of the data they generate. With just less than 50% of companies documenting it and using them within their strategies, the idea is to understand why data roles rank so high on career and growth lists?
Gartner’s report gives a straight, useful answer to this question, too: “By 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency.”
A data engineer is rightly known as a data enabler for data science teams. Since the data engineering role is built around obtaining, cleaning, and integrating data within a company’s processes, data engineers can carry out all upfront data requirements.
2. Data engineering gives your data velocity.
Static, stale data will do your business no good, especially when it is not updated regularly. Imagine having to make meaningful insights with data from 2019. Such data might be ideal for driving a trend analysis, but it won’t help you too much in the long run. Can you predict the future with old data and figure out what decisions you need to take to secure your future, drive your sales and generate revenue on the go? Probably not.
Data engineers play an integral role in helping maintain the freshness of the data, as the pipelines are refreshed at regular intervals. This means, the management always has a good start on their data points, which can be used effectively to make well-informed decisions.
3. Better data means better forecasting.
Lack of data and the inability of enterprises to manage it impedes many entrepreneurs. If your data center ran out of storage in this world of Big Data, you needed to purchase expensive devices and install them after the purchase.
This is not the case now; Big Data has become a term for large amounts of data, and scaling up and down as per requirements has become a cinch. According to data engineers, companies can successfully increase their data processing and forecasting abilities in minutes, by plugging in new, well-developed cloud technology.
In other words, we need data like water, and to store it, we need reservoirs while pipelines to ensure fast delivery. Data engineers build and maintain these pipes. These “pipes” also transfer data from old storage bins to new storage reservoirs, so that applications can use them on the go.
How To Become a Data Engineer
In the world of data technology, it’s very difficult to remain black and white all the time. However, the process of becoming a data engineer is quite straightforward. Let’s dive in.
1. Learn programming.
Programming is the backbone of data analytics, and a data engineer needs to have an in-depth knowledge of programming languages like:
2. Master automation skills.
Repetitive tasks can be automated using different pieces of technology like Robotic Process Automation. Scripting languages can automate tasks like running pieces of code automatically, sending emails, assigning work, and more.
3. Databases are your ally.
In data engineering, it’s all about handling databases, storing data, and extracting information for use. SQL is the top language for ETL, whereas, it is also used for transferring data from one source to another. Databases can also be finetuned to facilitate fast analysis and create table schemas.
4. Data processing isn’t as simple as it sounds.
Data processing is a task that needs to be followed to the tee. Apache Spark continues to be one of the most widely used data engines for parallel data processing. The data processing framework uses batch processing, which requires collecting data points and grouping them with specific time intervals.
We’ve unpacked the process of becoming a data engineer—the idea is to ensure you have the right bandwidth and knowledge to get started.
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