The demand for skilled data engineers and scientists is going through the roof. Organizations today have much more data than they had a decade earlier and this pile is only increasing with each fleeting moment. With so much data, these organizations are mostly stuck in a pickle when it comes to finding a right candidate to trust with this data. We’re talking about data engineers, yes.
There’s a severe shortage of skilled data engineers, but there is a lot of opportunity for grabs. For instance, a simple search of “Data Engineer” on Naukri.com will list before you more than 5,000 openings. There’s a severe gap between the demand and the supply of skilled data professionals, and especially data engineers.
Here’s our attempt to help you get on the right track from day one. This is the part one of a two-part series to help you set your foundation correct for a potential data engineer.
It’s crucial to know what are the key roles of a data engineer and how do they differ from the roles of other data-professional. So, this part will give you a sneak-peak into the daily life of a data engineer in terms of the work they do.
It’s crucial to know what are the key roles of a data engineer and how do they differ from the roles of other data-professional. So, this part will give you a sneak-peak into the daily life of a data engineer in the terms of the work they do.
What does a data engineer do?
Ideally, the role of a Big Data Engineer includes building systems, algorithms, and processes, depending on what the Big Data Architect has designed. A Big Data Engineer is responsible for developing, maintaining testing, and evaluating Big Data solutions within organizations. A Big Data engineer is expected to be hands-on with Hadoop and Hadoop based technologies like MapReduce, MongoDB/Cassandra, Hive, etc. Using these tools, a big data engineer develops large-scale data processing systems. A data engineer should also be able to work with data warehousing solutions as well as with the latest Not Only SQL technologies.
At the end of the day, a Big Data engineer is just an engineer working on Big Data. So, like any software engineer, a Big Data engineer, too, is expected to have a fair bit of understanding of software development lifecycle and software engineering concepts. These engineering concepts are basics and must know for any engineer, Big Data or not. More often than not, beginners tend to skip the concepts of software engineering, and that hurts them later when they’re to develop large-scale Big Data solutions.
A Big Data engineer is required to code, and hence it’s advised to have a hands-on experience with object-oriented designing, coding, and testing patterns. Also, being hands-on with engineering platforms and large-scale data infrastructures goes a long way in the career of any data engineer. As a prominent data engineer, you’ll be working with tens of thousands of GBs of data and a lack of knowledge on how to manage such large-scale datasets might turn out to be a major pitfall. An in-depth understanding and knowledge of how algorithms work and the ability to assess their complexities along with building high-performance algorithms also comes in handy during the journey.
Facing terabytes or even exabytes of data on a daily basis should not be a source of fright to any budding Big Data engineer. In order to develop scalable as well as innovative big data solutions, a Big Data engineer should have a sufficient knowledge of different programming and scripting languages like Java, C++, Ruby, Python, and/or R. Also expert knowledge should be present regarding different (NoSQL or RDBMS) databases such as MongoDB or Redis.
The systems developed by a data engineer should be capable of collecting, parsing, managing, analyzing, and visualizing large sets of data to turn raw data into actionable insights. Further, they also need to decide on their hardware and software design needs and work on the same. The most important thing a Big Data engineer does is developing prototypes and proof of concepts for the selected solutions.
Other than what we’ve described above, there are some other traits that are invariably found in any successful data engineer:
- Enjoying challenges and solving complex, non-regular, problems on a daily basis.
- Having excellent communication skills as Data Engineers act like the middlemen between the organization’s stakeholders and the clients.
- Proficiency in designing efficient and robust ETL workflows;
- Ability to work in the cloud
- Ability to efficiently work while collaborating with a large team.
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How does a data engineer differ from a data scientist?
While there is a certain amount of overlap between the roles of all the data professionals when it comes to skills and responsibilities, these two roles are being increasingly separated into distinct and specialized roles,
Data scientists focus more on the interaction with data rather than building or maintaining scalable solutions. They are often required to conduct high-level market and business operation research. This research helps in identifying trends and relations. For the same, they use a variety of sophisticated machines and methods to interact with and act upon data.
Data Scientists, unlike Data Engineers, should be well-versed with machine learning and advanced statistical techniques. Their work revolves around taking the raw data and turning it into actionable, understandable content. This isn’t attainable without the help of advanced mathematical models and algorithms. This information is often used as an analysis source to tell the “bigger picture” to the stakeholders.
So, all in all, what is it that makes data engineers different from data scientists? Generally speaking, the main difference is that of focus. While Data Engineers are focused on building infrastructure and systems for data generation; Data Scientists focus on advanced mathematical and statistical analysis on the raw data. To put it even merely, Data Engineers work with the data provided by Data Scientists and build maintainable systems to digest that data and facilitate the analysis process.
Now it’s time to take a little break. By now, you’re aware of what a Data Engineer is, and what he isn’t. Further, we’ll be talking about the various tools, technologies, and skills that you should master. Also, we’ll look at some certifications and courses that’ll help you strengthen your learning as well as your credibility.
Stay tuned for the second part!
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Why is data engineering such a critical role?
Engineers specialize according to the demands of the job. With the tsunami of completed corporate digital transformations, the Internet of Things, and the rush to become AI-driven, it is evident that businesses require a large number of Data Engineers to lay the groundwork for successful data science programmes. As a result, the function of Data Engineers will continue to grow in relevance and scope. Companies require teams of employees whose main purpose is to process data in such a way that it can be used to extract value.
What are the most common job titles within Data Engineering?
The discipline of data engineering comprises the following positions
1. Data Architect - Data architects create data management solutions for entire companies or individual departments within them.
2. Database Administrator - Database administrators assist in the creation and upkeep of database systems. They make sure that database systems work well for all users in a company.
3. Data Engineer - Data engineers are in charge of ensuring that an organization's data infrastructure is stable and interconnected. They are expert coders using programming languages such as Python, Java, Scala, C++, etc.
What are the responsibilities of a Data Engineer?
Data engineering is the process of organizing data such that it is easier to utilize by other systems and people. A Data Engineer works with Data Analysts, Data Scientists, System Architects and Business Leaders to understand their specific needs. The responsibilities of a Data Engineer include:
1. Obtaining data requirements, such as how long the data must be held, how it will be used, and who and what systems must have access to it.
2. Maintaining metadata on the data, such as what technology is used to handle it, its schema, size, security, source, and eventual owner. Using centralised security controls like LDAP, encrypting the data, and auditing data access to ensure data security and governance.
3. Storing data with specialised technologies such as a relational database, a NoSQL database, Hadoop, Amazon S3 or Azure blog storage, optimised for the specific application of the data.
4. Using tools to access data from many sources, convert and enhance the data, summarise the data, and save the data in a storage system.