Introduction to Data Engineering

what is Data Engineering and what is the role of a data engineer?

According to one of the many definitions you will find there I would like to present to one popular one I like, Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. The definition of the work data engineers do falls under the lines of :

i) Data engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their ultimate goal is to make data accessible so that organizations can use it to evaluate and optimize their performance.

ii) Data engineers design and build pipelines that transform and transport data into a format wherein, by the time it reaches the Data Scientists or other end users, it is in a highly usable state. These pipelines must take data from many disparate sources and collect them into a single warehouse that represents the data uniformly as a single source of truth.

Why Data Engineering ?

Data engineering as a role is a fairly new and emerging position. LinkedIn’s 2020 Emerging Jobs Report and Hired’s 2019 State of Software Engineers Report ranked Data Engineer jobs right up there with Data Scientist and Machine Learning Engineer. However, for some companies, especially those still finding their legs in data science or AI, it’s not always apparent what data engineering is, what role Data Engineers play within the analytics team and what skills are required (and should be vetted) to do the job. Careers in data engineering can or maybe influenced by passion for data, seeking being a problem solver, or even the money factor(as data engineering is a fairly new position it is not yet saturated and demand is still very high.)

Data Engineering Salary
Data engineering is a well-paying career. The average salary in the US is $115,176, with some data engineers earning as much as $168,000 per year, according to Glassdoor (May 2022) [4].

**Data Engineering Tools:-

Apache Hadoop**: is a foundational data engineering framework for storing and analyzing massive amounts of information in a distributed processing environment. Rather than being a single entity, Hadoop is a collection of open-source tools such as HDFS (Hadoop Distributed File System) and the MapReduce distributed processing engine.

Apache Spark: is a Hadoop-compatible data processing platform that, unlike MapReduce, can be used for real-time stream processing as well as batch processing. It is up to 100 times faster than MapReduce and seems to be in the process of displacing it in the Hadoop ecosystem. Spark features APIs for Python, Java, Scala, and R, and can run as a stand-alone platform independent of Hadoop.

Apache Kafka: is today’s most widely used data collection and ingestion tool. Easy to set up and use, Kafka, is a high-performance platform that can stream large amounts of data into a target like Hadoop very quickly.

Apache Cassandra is widely used to manage large amounts of data with lower latency for users and automatic replication to multiple nodes for fault-tolerance.

SQL and NoSQL (relational and non-relational databases)are foundational tools for data engineering applications. Historically, relational databases such as DB2 or Oracle have been the standard. But with modern applications increasingly handling massive amounts of unstructured, semi-structured, and even polymorphic data in real-time, non-relational databases are now coming into their own.

Programming Languages:-
Python is a very popular general-purpose language. Widely used for statistical analysis tasks, it could be called the lingua franca of data science. Fluency in Python (along with SQL) appears as a requirement in over two-thirds of data engineer job listings.

Ris a unique language with features that other programming languages lack. This vector language is finding use cases across multiple data science categories, from financial applications to genetics and medicine.
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Java,** because of its high execution speeds, is the language of choice for building large-scale data systems. It is the foundation for the data engineering efforts of companies such as Facebook and Twitter. Hadoop is written mostly in Java.
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Scala** is an extension of Java that is particularly suited for use with Apache Spark. In fact, Spark is written in Scala. Although Scala runs on JVM (Java Virtual Machine), the Scala code is cleaner and more concise than the Java equivalent.

“Torture the data, and it will confess to anything.” — Ronald Coase
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Path to become a Data Engineer
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With the right set of skills and knowledge, you can launch or advance a rewarding career in data engineering.

1. Develop your data engineering skills. Learn the fundamentals of cloud computing(could be GCP, AWS, Microsoft Azure), coding skills(most preferred Language being Python because of its robust architecture, readability and a vast community support), and database(Spark (PySPark, Spark SQL,SQL (Relational & NoSQL)

design as a starting point for a career in data science.
Coding: Proficiency in coding languages is essential to this role, so consider taking courses to learn and practice your skills. Common programming languages include SQL, NoSQL, Python, Java, R, and Scala.
Relational and non-relational databases: Databases rank among the most common solutions for data storage. You should be familiar with both relational and non-relational databases, and how they work.
ETL (extract, transform, and load) systems: ETL is the process by which you’ll move data from databases and other sources into a single repository, like a data warehouse. Common ETL tools include Xplenty, Stitch, Alooma, and Talend.
Data storage: Not all types of data should be stored the same way, especially when it comes to big data. As you design data solutions for a company, you’ll want to know when to use a data lake versus a data warehouse, for example.
Automation and scripting. Automation is a necessary part of working with big data simply because organizations are able to collect so much information. You should be able to write scripts to automate repetitive tasks.
Machine learning: While machine learning is more the concern of data scientists, it can be helpful to have a grasp of the basic concepts to better understand the needs of data scientists on your team.
Big data tools: Data engineers don’t just work with regular data. They’re often tasked with managing big data. Tools and technologies are evolving and vary by company, but some popular ones include Hadoop, MongoDB, and Kafka.
Cloud computing: You’ll need to understand cloud storage and cloud computing as companies increasingly trade physical servers for cloud services. Beginners may consider a course in Amazon Web Services (AWS) or Google Cloud.
Data security: While some companies might have dedicated data security teams, many data engineers are still tasked with securely managing and storing data to protect it from loss or theft.
Read more: 5 Cloud Certifications for Your IT Career

1. Get certified and learn from communities A certification can validate your skills to potential employers, and preparing for a certification exam is an excellent way to develop your skills and knowledge. Options include the Associate Big Data Engineer, Cloudera Certified Professional Data Engineer, IBM Certified Data Engineer, or Google Cloud Certified Professional Data Engineer. Learn as community also offers that group kind of support and learning together pushing and encouraging each other(this could be boot camps). Currently here in Kenya Data Science East Africa and Lux Tech Academy are running a boot camp dabbed “Data Engineering Mentorship program by Data Science East Africa”

Check out some job listings for roles you may want to apply for. If you notice a particular certification is frequently listed as required or recommended, that might be a good place to start.

1. Build a portfolio of data engineering projects. A portfolio is often a key component in a job search, as it shows recruiters, hiring managers, and potential employers what you can do.

You can add data engineering projects you’ve completed independently or as part of coursework to a portfolio website (using a service like Wix or Squarespace). Alternately, post your work to the Projects section of your LinkedIn profile or to a site like GitHub — both free alternatives to a standalone portfolio site.

Brush up on your big data skills with a portfolio-ready Guided Project that you can complete in under two hours. Here are some options to get you started — no software downloads required:

Create Your First NoSQL Database with MongoDB and Compass
Database Design with SQL Server Management Studio (SSMS)
Database Creation and Modeling using MYSQL Workbench
Read more: How to Build a Data Analyst Portfolio: Tips for Success

1. Start with an entry-level position. Many data engineers start off in entry-level roles, such as business intelligence analyst or database administrator. As you gain experience, you can pick up new skills and qualify for more advanced roles. See an example of a possible learning journey with this Data Engineering Career Learning Path from Coursera.

Do I need a degree to become a data engineer?

It’s not necessary to have a degree to become a data engineer, though some companies might prefer candidates with at least a bachelor’s degree. If you’re interested in a career in data engineering and plan to pursue a degree, consider majoring in computer science, software engineering, data science, or information systems.

Next steps
Whether you’re just getting started or looking to pivot to a new career, start building job-ready skills for roles in data engineering. I hope this article gave some perspective and insight to enable you kick start you journey.