Key Skills Required for Data Engineering

Jul 25, 2022

Data Engineering is a term whose probability of appearing on social media platforms is as high as encountering a black car on a highway. It is a hot topic everywhere due to a number of reasons. In the past couple of years, Data Engineering has been chosen as a profession by so many people. Organizations have increased the number of vacancies for this job, and all this for what? Because data is everything. Handling a bulk of data that we store on our clouds or hardwares, structuring it, making it useful, formatting it, and so much more can be done if you have the right data engineering skills. So, discussed in this blog are some key skills required for Data Engineer.

To become a good Data Engineer, you must have command of the following skills.

  • Database System

A Data Engineer has to play around with a lot of data. To retrieve the required information, they should be able to handle Database Management System (DBMS), which requires good knowledge of SQL and NOSQL.

  • Structured Query Language (SQL): If you have strong SQL skills you can easily create data warehouses, which can be integrated with other tools to analyze the data required for a particular business. Big Data and Advanced Modelling are two of the SQL types that you might require to focus upon undividedly for the detailed information in your project. But the base of technology for all this is SQL.
  • Not only Structured Query Language (NoSQL): Often referred to everything other than SQL, NoSQL is a non-relational database that is independent of the typical table and rows schema. It doesn’t use SQL queries but rather uses other programming languages to construct the query for the desired data. Famous NoSQL technologies are MongoDB, Cassandra, etc.

Read about different types of databases in Google Cloud here.

  • Data Transformation

You might have learnt by now that it is of utmost importance to extract useful data from the bulk of raw data collected via different database tools. Transforming the raw data into useful information depends on factors like:

  • Source of data
  • Format of data
  • Desired output

The above-mentioned factors are also responsible for the level of difficulty in data transformation such as easy, moderate, and complex. Some famous data transformation tools are Alteryx, Data Building Tool (dbt), Dataform etc.

  • Data Ingestion

Once the data is extracted and transformed into valuable information, the next step is to move it from one source to another. This data could be in varied formats, moving which gets further complex. In such scenario data ingestion tools come in handy for a data engineer. These tools help in:

  • Identifying the data sources,
  • Validating them
  • Dispatching them in an effective manner, and so on.

Examples of some of the data ingestion tools are Hevo Data, Apache Nifi, Apache Kafka, Apache Flume, etc.

  • Data Mining

Since the data is available in bulk, even after data transformation and ingestion, it is essential to filter out the vital information. This is where data mining comes into picture. It helps in finding out patterns in large sets of data which is helpful in preparing the data for further analysis. It is beneficial in carrying out:

  • Data classifications
  • Data predictions

Some important data mining tools that every data engineer should be familiar with are Rapid Miner, Weka, Oracle Data Mining, and so on.

  • Data Warehousing and ETL

When such a large amount of data is ready to be sorted out to suffice various business problems, streamlining this process becomes important. Data Warehousing is all about handling large data that comes from different sources. With the help of Extract, Transfer, Load (ETL) tool, this raw data can be collected, read as well as assigned to different database or business intelligence platforms. Some of the popular ETL tools available are IBM Data Storage, Oracle Data Integrator, Hadoop, etc.

  • Machine Learning

There is so much that can be written about Machine Learning as it is a vast topic. But when it comes to data engineering, Machine Learning helps in making predictions based on the previous data. There are several algorithms in it that are designed considering the pattern of the incoming data. Further this data is translated into useful information. If you have a good understanding of these algorithms, you would be able to build more accurate data pipelines.

I will explain this by giving a more real-life example. If you search for blinds for your office windows in a browser, you will see that every time you open that browser it starts suggesting blinds from different brands etc., in the form of ads. Ever wondered how this happens? I hope now you are able to guess it. Yes, it is because of Machine Learning, it identifies the pattern of the data and starts suggesting similar products. Some of the most famous machine learning tools are TensorFlow, Amazon Machine Learning (AML), Google Cloud AutoML and so on.

  • Programming Language

Since categorization of data into different patterns depends on the math done by Machine Learning, it is essential to have knowledge of programming languages that are helpful for this process. Python, Java, Scala, are the languages that are a must to learn in order to become a good data engineer.

  • Cloud Computing Tools

As we all know, in the past couple of years, several organizations have started preferring cloud services much more than before. Hence, it is of utmost importance for you as a data engineer to have knowledge of cloud computing tools. There are large chunks of data that are stored in the cloud and quick availability of data is the most important task that needs attention. So, whether your organization works with public, private, hybrid or multi cloud, you must be aware of cloud platforms like Amazon Web Services (AWS), Azure, Google Cloud Provider (GCP), OpenStack, etc.

Read about cloud computing architecture here.

Conclusion

Tim Berners-Lee has righty said, “Data is a precious thing and will last longer than the systems themselves.”

Highly skilled professionals have always been the prime choice of organizations that are hiring them for respective job posts. And if the job is in high demand, it adds bonus points to your profile if you have a good grip of the required skills. Discussed above are the key skills required for Data Engineer. So, if you are also looking forward to taking it as your profession, enhance these skills and you will definitely prosper in this field.

At Fibonalabs, we have skilled professionals who have great knowledge in UX/UI design, product development, cloud services as well as data engineering. In case you are looking forward to availing yourself of these services, visit us at www.fibonalabs.com. You can also get details about choosing the best cloud service provider for your organization here.

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