Key Skills Required for Data Engineering
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 many 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 hardware, 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 a 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 as 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.
- 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:
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 a scenario data ingestion tools come in handy for a data engineer. These tools help in:
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 vital information. This is where data mining comes into the picture. It helps in finding out patterns in large sets of data which helps prepare the data for further analysis. It is beneficial in carrying out:
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 the Extract, Transfer, Load (ETL) tool, this raw data can be collected, read as well as assigned to different databases 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 previous data. Several algorithms in it 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 can 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 the 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, and 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.
Conclusion
Tim Berners-Lee has rightly 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 their respective job posts. And if the job is in high demand, it adds bonus points to your profile if you have a good grip on the required skills. So, if you are also looking forward to taking it as your profession, enhance these skills and you will definitely prosper in this field.