Big data is changing the future of almost every industry. The market for big data is expected to reach $23.5 billion by 2025.
Data science is an increasingly attractive career path for many people. However, the outlook is hazy for people that are not as familiar with the career path.
If you want to become a data scientist, then you should start by looking at the career options available. Northwestern University has a great list of ways that people can pursue a career in data science. You should also learn the career path that you need to follow to get started, which includes learning the right programming languages.
Forging a Career Path in the Field of Data Science
With advancing technology, the data science space is rapidly evolving. Unlike the old days where data was readily stored and available from a single database and data scientists only needed to learn a few programming languages, data has grown with technology. Any company’s growth and development means that they begin handling huge amounts of data from different sources. While some arrive in batches, others are sourced from various channels in different sizes. This explains the current surge in demand for data engineers, especially in data-driven companies.
That said, if you are determined to be a data engineer, getting to know about big data and careers in big data comes in handy. The guide below outlines the path you should take to become a data engineer.
Learn Programming
Understanding the association between data science and software engineering is very important for data engineers. To become an accomplished data engineer, learning to code and other software engineering skills are essential. Fortunately, you won’t have to learn all the programming languages apart from the basics. Data engineering primarily revolves around two coding languages, Python and Scala.
You should learn how to write Python scripts and create software. This will enable you to leverage the right algorithms to create good, well structured, and performing software. As such, you should find good learning courses to understand the basics or advance your knowledge of Python.
Similarly, various tools used in data engineering revolve around Scala. Scala runs on Java Virtual Machine, making it compatible with various Java libraries available. Getting an introductory course on Java is thus prudent.
Master Data Scripting and Automation
Data automation is crucial for data engineers for many reasons. Excellent automation helps in handling the growing amounts of data and repetitive tasks. For instance, automation comes in handy when you have to clear some tasks on hourly or daily schedules. Achieving this requires the input of various automation tools as well.
Essential automation tools include shell scripting tool, which informs a UNIX server of what and when to complete a task, CRON, which is a crucial time-based task scheduler that marks when specific tasks should be executed, and Apache Airflow, which relies on the available scripting capabilities to schedule data workflows.
Understand the Databases
As a data engineer, you will be primarily working on databases. As such, you should begin by learning the basics of SQL. SQL is an established language used widely in data engineering. Just like programming, SQL has multiple dialects. However, you don’t need to learn them all. You should prioritize MySQL and PostgreSQL to understand the basics.
Besides SQL, you should also learn how to model data. This defines how various data entities interact within the system and how constructive information is drawn from it. For this, learn essential techniques such as star schema and database normalization. You should also learn to differentiate databases optimized for transactions and analysis.
Learn Data Processing Techniques
At this stage, you should have understood the fundamentals of programming, automating tasks, and leveraging SQL. With these basics, you can proceed to learn how to deal with big data presented in batches. As mentioned, current data comes from various sources and in different sizes. Getting and organizing such data is called data processing.
Data engineers handling small datasets might ease data processing using Python, dplyr, or SQL engines. However, if you have gigabytes and terabytes of data, you can opt for parallel processing. The benefits of parallel data processing are that you can process using more power, and you can make better use of memory in all the data processing units.
That said, a commonly used parallel data processing engine is the Apache Spark. Spark outperforms old parallel systems such as Hadoop, as it is written using Scala and helps interface with other programming languages and other tools such as Dask. Data processing is often done in batches. However, if you have huge amounts of data, you should learn how to process in streams.
The last phase of data processing is loading the results to a target database. In most cases, data engineers use the MPP database to store processed data. Regardless, the database uses parallel processing to complete analytical queries.
Schedule Workflows
Having created tasks that process data using Spark or any other engine, you should schedule regular performance. For this, you can use CRON or Apache Airflow, depending on the tool that suits your workflow. CRON is best for small tasks, while Apache Airflow handles advanced and interdependent data.
Learn Cloud Computing
The importance of cloud computing in data engineering cannot be avoided. Initially, data-driven companies, such as banks and hospitals, would use hired data centers or rent server racks in a data center. However, such setups had various drawbacks, including wastage of a lot of server time and the unavoidable need for geo-replication.
Such impracticality with the use of data centers is what led to the inception of cloud platforms. Unlike data centers, cloud platforms centralize processing power. For instance, if one of the platform’s customers has an idle time, the other might be having a peak performance. Cloud platforms also distribute processing power equally.
That said, data engineers should learn how cloud platforms work. Popular cloud platforms include the Microsoft Azure, Google Cloud Platform, and Amazon Web Services. More like data centers, cloud platforms perform several services, including cloud storage, computation, cluster management, and data processing.
Follow Industry Trends
Data engineering, like any other career, is a field that is constantly changing. Therefore, it is important that you always have the right tools for your work and avoid complicating big data solutions. To achieve this, you should keep tabs on recent developments in the data engineering space. Among the best ways to follow industry trends include;
- Be on the lookout for new software and services, such as Rockset, Koalas, and Metaflow.
- Watch data engineering-related conference talks on ApacheCon and other platforms.
- Listen to podcasts such as the Data Engineering Podcast.
- Review curated lists of data engineering tools on relevant platforms, such as GitHub.
Should You Become a Data Engineer?
The decision to become a data engineer solely relies on your career plan and interest. Basically, data engineers prepare and make data accessible for data analysts and scientists. If this is what peaks your interest, you shouldn’t hesitate to get yourself into data engineering. Data engineering is also among the fastest-growing technology jobs in the U.S and globally. With an increased demand for data engineers, you can be sure of direct employment after training.
To become a data engineer, you should complete a degree in computer science or any other related field. However, this is a relatively expensive course, which prevents most people from joining it. Fortunately, you can search for financial aid, such as the G.I. benefits, to complete your studies.