Data science isn’t always the most fascinating career in the data field, despite the fact that data scientists may get more attention than other data professionals. According to DataCamp, there was a 100% rise in open data engineering opportunities between 2021 and 2022, showing that the need for data engineers is currently outperforming the demand for data scientists.
You’re in the perfect place if you want to profit from this trend. We’ll cover all you need to know about how to become a data engineer in the sections below, along with what to expect once you land a job.
What is Data Engineering?
Data engineering is a subfield of data science that focuses on creating and maintaining the pipelines and infrastructure required for data collection, processing, storage, and delivery. It entails laying the groundwork for data-driven operations and analytics and making sure that data is readily available, trustworthy, and prepared for analysis by data scientists, analysts, and other stakeholders.
By creating and maintaining the systems that enable the efficient flow of data through various stages, from its raw condition to important insights, data engineers play a critical role in the data lifecycle.
In order to give data scientists, analysts, and other stakeholders the information they require to gain insights and make wise decisions, data engineering is a collaborative field that works closely with them. Data engineers create the infrastructure and procedures that support data-driven businesses, bridging the gap between raw data and insightful conclusions.
Why do companies hire a Data Engineer?
Data scientists, analysts, and other stakeholders need to have access to, accurate data that is prepared for analysis by data engineers. The following are the principal duties and jobs frequently performed by a data engineer:
Data Infrastructure Building:
Data engineers are in charge of laying the groundwork for an organization’s effective performance and use of its data. They plan and construct data pipelines to make sure that data is seamlessly transferred from different sources to storage and analytics platforms. This infrastructure makes data accessible and analyzeable, enabling data-driven decision-making.
Data Quality Assurance:
The accuracy and integrity of data are crucially dependent on data engineers. They remove conflicts, deal with missing numbers, and preprocess raw data before putting it into a format that may be used. This prevents errors from having an impact on company choices and guarantees that the data used for analysis is trustworthy and accurate.
Efficient Data Processing:
Data engineers create effective data handling systems in a world with rising data volumes. They make use of tools like distributed computing frameworks (such as Apache Spark) to analyze and convert huge datasets, improving data pipelines and processing operations. This efficiency guarantees that data processing is scalable and does not damage the performance of the entire system.
Real-Time Analytics:
Data engineers set up systems that can process streaming data in real time in response to the demand for immediate insights. This includes creating data processing pipelines that can handle and analyze data as it is generated, enabling businesses to act quickly to address new patterns or problems.
Collaboration and Innovation:
Data engineers fill the gap between raw data and practical experience. They enable these teams to produce valuable insights by giving data scientists, analysts, and business stakeholders clean, well-structured data. Teams can experiment with sophisticated analytics, machine learning, and predictive modeling as a result of this collaboration in order to find novel opportunities and find solutions to challenging problems.
Roadmap to become a Data Engineer
To become a Data Engineer, you must acquire a wide range of information and abilities. However, before you delve further into anything else, you must have this as a foundation:
1. Programming Fundamentals:
Learn Python thoroughly:
You can choose from a variety of coding languages, such as Java, Python, or Scala, but I advise you to go with Python because it is the foundation for many abilities that you will need to master in the future. Python will therefore be an excellent place for you to start learning about data structures, loops, functions, and libraries (like pandas) for manipulating data.
Some online courses to learn Python:
Master SQL: SQL is a core skill you must have in order to work in data engineering, just like Python is the standard language for interacting with relational databases, which are an essential component of data engineering. You can conduct a variety of data engineering tasks, including database interface, data retrieval, manipulation, integration, query optimization, and data modeling, thanks to the broad skill set that SQL gives you.
2. Database Knowledge:
Now that you’ve mastered Python and SQL, you need to learn how to connect with data warehouses in real life, or at the very least, how to create them. Since databases are essential for the storage, retrieval, and processing of data, data engineers frequently work with them. There are two different kinds of databases: NoSQL databases like MongoDB or Cassandra, or relational databases like MySQL and PostgreSQL. To properly manage and handle data, one must be familiar with both relational and NoSQL databases.
3. Cloud Platform Proficiency:
The Cloud is becoming a crucial skill you must master in order to become a data engineer due to the trend of companies shifting their storage from locally to the Cloud. Data engineers are able to create scalable, effective, and cheap data pipelines and infrastructure thanks to their knowledge of cloud platforms. It offers the resources and techniques required to handle the complexity of today’s data engineering.
You can choose any Cloud Provider like AWS, GCP, or Azure, but it is best to decide based on preference or job market demand.
Understand the basics of Cloud Services such as storage, compute, and data processing on your chosen platform. You can choose suitable learning ways like books, hands-on project or online courses from trustful website like Udemy or edX to pursue cloud certifications.
4. Containerization and Orchestration:
The deployment and management of complex applications and data pipelines are simplified by containerization and orchestration technologies like Docker and Kubernetes, assuring consistency and scalability.
Understanding Docker and Kubernetes will help you deploy and manage your apps, ETL processes, and pipelines more effectively, assuring consistency, scalability, and simpler communication between development and operations teams.
You can watch online tutorials and documentation to learn about Docker and Kubernetes because both of them provide thorough documentation and tutorials on their official websites. Online classes are a nice option, or you may put up a Kubernetes cluster and install Docker to experiment with containers and orchestration (using Minikube for local settings).
5. Continuous Learning:
However, always be updated. To learn about new tools and techniques, make it a habit to read blogs frequently, follow industry news, and participate in forums (such as Stack Overflow). Don’t let yourself fall behind because technology is advancing rapidly.
You should also create a network in data engineering communities, by attending meetups, and connecting with professionals for insights and opportunities.
Keep in mind that developing your skills as a data engineer requires time and commitment. Don’t give up lightly because this is a dynamic sector that involves constant learning and adapting to new technologies and obstacles. If you put forth a little bit more effort, you can succeed.