As the field of big data continues to evolve, data engineers play a crucial role in managing and processing large datasets. Data engineers are responsible for designing and managing infrastructure that allows easy access to all types of data (structured and unstructured). Data engineers are responsible for designing, constructing, installing, testing, and maintaining architectures, including databases and systems for large-scale processing. They also develop, maintain, and test data management systems. The contemporary world experiences a huge growth in cloud implementations, consequently leading to a rise in demand for data engineers and IT professionals who are well-equipped with a wide range of application and process expertise. Hence, learning and developing the required data engineer skills set will ensure a better future. Data Engineers are professionals who bridge the gap between the working capacity of software engineering and programming. They are people equipped with advanced analytical skills, robust programming skills, statistical knowledge, and a clear understanding of big data technologies.
Data engineers use their technical expertise to ensure the systems they build are secure, scalable, and reliable—meaning they can handle vast amounts of data and provide it in real time. Data engineering is a rapidly growing field with many lucrative job opportunities. In today’s fast-paced business landscape, the ability to efficiently design, build, and manage data pipelines is crucial for enterprises aiming to extract valuable insights and make data-driven decisions. Due to its instrumental role in transforming raw data into actionable intelligence, Data Engineering has emerged as a high-demand job. They are expected to know about big data frameworks, databases, building data infrastructure, containers, and more. It is also important that they have hands-on exposure to tools such as Scala, Hadoop, HPCC, Storm, Cloudera, Rapidminer, SPSS, SAS, Excel, R, Python, Docker, Kubernetes, MapReduce, Pig and many more.
Key Responsibilities of a Data Engineer are:
- Obtain data from third-party providers with the help of robust API integrations.
- Build, Design, and maintain data architectures using a systematic approach that satisfies business needs.
- Create high-grade data products by coordinating with engineering, product, data scientists, and business teams.
- Develop optimized data pipelines and make sure they are executed with high performance.
- Track the latest developments in the domain of data infrastructure and analytical tools.
- Perform research to handle any problems faced while meeting the business objectives.
- Use the data efficiently and identify tasks that can be automated.
- Implement different methods to enhance data quality and reliability.
Here is a list of the important skills for data engineers that one should possess to build a successful career in big data:
1. SQL
Data engineers use SQL for performing ETL tasks within a relational database. SQL is ideal for use when the destination and data source are the same type of database. Today, more and more cloud-based systems add SQL-like interfaces that allow you to use SQL. ETL is central to getting your data where you need it. Relational database management systems (RDBMS) remain the key to data discovery and reporting, regardless of their location. Traditional data transformation tools are still relevant today, while next-generation Kafka, cloud-based tools, and SQL are on the rise for 2024. Strong SQL skills allow using databases to construct data warehouses, integrating them with other tools, and analyzing that data for business purposes. There are several SQL types that data engineers might focus exclusively on at some point (Advanced Modelling, Big Data, etc.), but getting there requires learning the basics of this technology.
2. Machine Learning and AI
A big data engineer should be familiar with Python’s libraries SciPy, NumPy, sci-kit learn, pandas, etc. They should also be familiar with the terminology and algorithms. Machine Learning is a big data analytics skill that is used to predict or process data through algorithms like Clustering, Classification, Regression, or Natural language processing. A big data engineer must understand the basic concept of machine learning. Machine learning is a subset of artificial intelligence. Data engineers typically require a functional knowledge of machine learning, which involves data modeling and statistical analysis.
Applying this skill can help you better understand data scientists’ requirements and create relevant and usable solutions for them.
3. Multi-Cloud computing
A data engineer needs to have a thorough understanding of the underlying technologies that make up cloud computing. They would need to know their way around IaaS, PaaS, and SaaS implementation. Cloud computing refers to the provision of computing services over the Internet. These services include servers, storage, databases, networking, software, analytics, and intelligence, to help businesses innovate faster and more efficiently. Companies worldwide increasingly depend on the cloud for their computing power and data storage needs.
As a result, they often require the services of data engineers who can use various cloud computing solutions on an organizational scale, such as SaaS, PaaS, and IaaS. Data engineering is all about designing, programming, and testing software, which is required for modern database solutions. This can be easier when you are using existing cloud services. The trend is to participate in multi-cloud over cloud technology and have a good understanding of the underlying technologies that make up cloud computing. Concepts of IaaS, PaaS, and SaaS are the trend, and big companies expect data engineers to have the relevant knowledge.
4. NoSQL
A data engineer should know how to work with key-value pairs and object formats like Avro, JSON, or Parquet in the open-source Apache-based or MongoDB and Cassandra. Big resources still manage file data hierarchically using Hadoop’s open-source ecosystem. The cloud could also be full of semi-structured or unstructured data with more than 225 no SQL schema data stores, which makes it one of the most important skills to be thorough with. Knowing how to work with key-value pairs and object formats is still necessary. NoSQL is a type of database management system (DBMS) that is designed to handle and store large volumes of unstructured and semi-structured data. Unlike traditional relational databases that use tables with pre-defined schemas to store data, NoSQL databases use flexible data models that can adapt to changes in data structures and are capable of scaling horizontally to handle growing amounts of data. NoSQL databases are often used in applications where there is a high volume of data that needs to be processed and analyzed in real-time, such as social media analytics, e-commerce, and gaming. They can also be used for other applications, such as content management systems, document management, and customer relationship management. Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages, lack of ability to perform ad hoc joins across tables, lack of standardized interfaces, and huge previous investments in existing relational databases. Most NoSQL stores lack true ACID transactions, although a few databases have made them central to their designs. Examples of NoSQL include Apache River, BaseX, Ignite, Hazelcast, Coherence, and many more others.
5 . Hyper Automation
Hyperautomation focuses on improving the quality of work, increasing decision-making agility, and accelerating business processes. They require skills to run value-added tasks. Hyper automation is the concept of automating everything in an organization that can be automated. Organizations that adopt hyper automation aim to streamline processes across their business using artificial intelligence (AI), robotic process automation (RPA), and other technologies to run without human intervention.
In addition to these technical skills, having a good understanding of data governance, and data security, and the ability to work in cross-functional teams will be invaluable for future data engineers. Continuously updating your knowledge and staying abreast of emerging technologies and trends is also vital to remain competitive in the rapidly evolving field of data engineering. The technical skills that are most in-demand for data engineers are constantly evolving, and it’s important to stay up-to-date and continually develop your skills in this exciting and rapidly growing field. The world is full of data, which is why the demand for data engineers is at an ever-increasing high. Society and industries of every kind depend on data to make critical decisions. A leading expert in the field can become a champion in the industry after acquiring relevant skills for data engineer and gaining hands-on experience.