Data engineering bridges the gap between data sources and end-user enablement. It is the process of designing, building, maintaining, and running systems and infrastructure for storing, processing, and analyzing large, complex datasets. With the growth of big data, data engineering has become an in demand skillset and therefore proves to be a high rewarding skill to learn.
Develop Skills.
a. Coding.
A data engineer is expected to be proficient in a number of programming languages. Common languages utilized include SQL, NoSQL, Python, Java, R and Scala. SQL is used to structure, manipulate & manage data stored in databases, while NoSQL databases can store large volumes of structured, semi-structured & unstructured data with quick iteration and agile structure as per application requirements.
b. Databases Management Systems.
Database rank as among the most common solutions for data storage. There are two main types of DBMS, relational and non-relational(NoSQL). Relational databases store data in tables that are linked using relationships while NoSQL store data in varied formats like key-value pairs, documents and graphs.
c. ETL (Extract, Transform and Load) Systems.
ETL is applied when managing a huge amount of unstructured data from one or more sources. It is the process by which data is moved from databases to a single repository like a data warehouse. Warehousing helps aggregate said data to analyze for better business. Some examples of ETL tools include Talend, Informatica, Stitch etc.
d. Data Storage.
When working with big data, it will become clear to the data engineer that not all types of data are stored in the same way. Therefore you will be required to have an understanding on whether to store data in something like a data lake compared to a data warehouse.
e. Automation and Scripting.
A challenge of working with big data is that large amounts of information is collected. You will therefore be required to write scripts that will automate repetitive tasks.
f. Machine Learning.
While machine learning is more the concern of data scientists having some level of understanding of how to put the data into use using statistical analysis and data modeling is a huge advantage
g. Big Data Tools.
When it comes to processing huge amounts of data, multiple computers are needed to divide and process in batches and combine the final output. This is known as batch processing and several frameworks are utilized such as Hadoop, Apache Storm, MongoDB and Kafka.
h. Cloud Computing.
Processing large amounts of data requires a powerful system. To avoid hardware breakdowns and regular software updates, companies resort to cloud service providers to ease the process of storing and processing data. Amazon Web Services and Google Cloud are good platforms for beginners to start learning cloud computing.
i. Data Security.
While this process can be outsourced to a data security team, it a valuable skill a data engineer should harness to securely manage and store data to protect it from loss or theft.
Build Portfolio.
When it comes to job searching, a portfolio is a great way to showcase to potential employers what you can do. Create small projects to apply your knowledge and post your work on platforms like GitHub or LinkedIn. This should allow you to secure an entry level position where you can pick up new skills and qualify for more advanced roles.
Conclusion.
Data engineering is a crucial field that helps businesses and organizations break down valuable insights from the data they have. By mastering the aforementioned skills, data engineers can solve business challenges and drive positive business growth. Whether you’re a novice or an expert in data engineering it is important that you remain curious and continue to learn as it is an ever evolving field. With the right tools and mindset, your goals will be achievable.
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