Data is the raw information all around us that can be processed to provide insightful information. This includes basic structures such as words and numbers. The amount of data around us is vast and has become integral in this era. Data is crucial in aiding innovations, providing guidelines on strategies and in making informed decisions.
Data is typically stored as files or databases. A data engineer designs, builds and maintains processes necessary for the collection, storage and processing of big volumes of data. As a data engineer, you will work closely with data analysts, data scientists and other stakeholders. In this article, I am going to highlight some important and basic concepts and skills necessary to kick-start off a career in data engineering.
1. Familiarize yourself with database management.
You will spend a good amount of your day dealing with databases. This includes collecting, storing, transferring, cleaning data. You'll do this using SQL. Knowledge of any additional dialects will be added advantage.
2. Programming languages
Besides SQL, you'll need to be fluent with a programming language. There are many options on this, but Python is the most common language to use. It excels at writing pipelines for the data and executing ETL jobs.
3. Make distributed computing frameworks your friend.
A distributed framework is an environment where many components are spread across many computers on a single network. The distributed frameworks divide work across the computers, and provide room for efficient completion of the work. These include Apache Hadoop and Apache Spark, and are designed to process vast of data. They also lay foundations for some Big Data applications.
4. Develop your knowledge of cloud technology
A big part of your job as a data engineer will be to connect the systems in your company to a cloud-based system. You must have experience with cloud-storage services such as Amazon Web Services (AWS), Azure, and Google Cloud; in terms of their advantages and disadvantages and how they apply to Big Data projects.
5. Gain a practical knowledge of ETL(Extract, Transform, Load) frameworks
You will need to know how to use technologies and frameworks such as Apache Airflow and Apache NiFi to create data pipelines.
6. Learn stream processing frameworks
There is a rise in demand for data engineers with knowledge in stream processing frameworks like Flink, Kafka Streams, or Spark Streaming. These aid in data science projects that use real-time data
7. Learn Shell Commands and Scripts
This will come in handy when you're executing jobs and routines of
Cloud and Big Data tools and frameworks. You must be comfortable to execute the commands and scripts on the terminal.
8. Up your communication skills
You will need to communicate across departments with data analysts, data scientists and stakeholders. Sometimes, you might find you need to develop reports and other visualizations to communicate effectively with stakeholders.
It is important to note that the world of data keeps evolving, with technologies and systems being discovered or improved daily. It is therefore paramount for you as a data engineer to keep abreast with the changing tides.
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