Data egineer may not be as popular as a data scientist, but it is one of the prestigious roles in data science. The role of data engineers may not be as glamorous as data scientists, but they form an integral part of data science operations. They are paid handsomely and enjoy good respect in the industry. The average salary of a data engineer in the US is $92, 160, according to PayScale. So it’s not a surprise that big data engineer certifications, courses, and training programs are mushrooming.
Well, there’s money and growth, so it’s understandable that the role is in high demand. Data engineers aren’t heard of as much as data scientists. So let’s discuss who data engineers really are, what they do, and how can one become a data engineer, but before that let’s understand what data engineering is.
What is data engineering?
As the name suggests, data engineering is applicable to areas where data is involved. The advent of big data has pushed organizations to analyze data and see patterns and gather insights, but before organizations can do that, data needs to be collected and stored. This is precisely what data engineering is about.
In simple words, data engineering can be defined as a gamut of actions intended to store, process, and deliver data to their respective stakeholders – data scientists, data analysts, etc. These actions are performed by data engineers. The major task of data engineers is to build and maintain a reliable infrastructure for data.
What does a data engineer do?
It’s not that companies didn’t collect or store their data. Earlier companies hired database experts for ETL roles. Informatica ETL, Pentaho ETL, and Talend are a few tools for which companies still hire to perform ETL processes. With the advent of big data, the ETL processes have become more complex and programmatic, which now requires an advanced skill set. A data engineer, therefore, is mainly responsible for managing data workflows, pipelines, and ETL processes.
Major skills that companies look for in data engineers are:
– Strong knowledge of SQL and Python
– Prior experience working with cloud platforms, preferably, Amazon Web Services
– Good knowledge of Java / Scala
– Excellent understanding of SQL and NoSQL databases (data modeling, data warehousing)
As you may gather from the above skills, data engineering has a close relationship with software engineering and backend development background. Let’s say a company starts generating data from multiple sources. So your task as a data engineer would be to collect, process, and store the generated information for use.
Most companies don’t generate data as much as big data. So to collect and process this data, a small centralized database repository is sufficient, popularly called a “data warehouse”. You can use the SQL database (Postgre SQL, MySQL, etc.) to store data and use scripts to drive data to the repository. However, in the case of enterprises like Google, Facebook, Amazon, Dropbox, the amount of data generated is exponential. So the list of tools to work on data is different. The use of tools mainly depends on the volume of data, the speed of data arrival, and heterogeneity. Consequently, working with this amount of data requires advanced skills.
Advance data engineer roles require:
-Knowledge of Python, Java or Scala
-Experience with big data: Hadoop, Spark, Kafka
– Knowledge of algorithms and data structures
– Understanding the basics of distributed systems
– Experience with data visualization tools like Tableau, ElasticSearch
How can you become a superior data engineer?
To excel as a data engineer, you will need to get your fundamentals strong. The following areas are extremely crucial to do well in data engineering.
1. Algorithms and data structures – The knowledge of data structure and algorithm is crucial to all data related roles. The better knowledge you have of data structures, the more easily you can understand an algorithm. Once a data scientist has built a model, it’s the responsibility of data engineers to put into production. Good knowledge of algorithms and data structures will help here.
Algorithms and data structures are the foundation of all computer science-related roles, so knowledge of these always comes handy. Globally-recognized big data engineer certifications and courses recognize algorithms and data structure as an essential skill for data engineers.
2. Learn SQL
As a data engineer, you will deal with several SQL and No SQL databases in production and otherwise. Plus, you will fetch and maintain extensive databases, which often requires querying data to extract. Good knowledge of SQL will allow you to do your job swiftly. All popular data warehouse applications – Amazon Redshift, Oracle, HP Vertica, SQL Server, and more use SQL.
3. Python, Java, and Scala programming
All commonly used big data tools to utilize these programming languages. Let’s take a look –
Apache Kafka (Scala)
Hadoop, HDFS (Java)
Apache Spark (Scala)
Apache Cassandra (Java)
Apache Hive (Java)
All big data storing and processing tools are written in Java or Scala, so knowledge of Java is mandatorily required for data engineers.
4. Cloud platforms
Currently, three cloud platforms are widely used in the industry – AWS ( Amazon Cloud Services), Google Cloud Platform, and Microsoft Azure. Though there are three, AWS is the most popular in the industry. Companies hiring data engineers, look for knowledge of at least one platform, preferably AWS.
5. Distributed systems
Working with big data means the presence of clusters working on independently working computers. The connection between these computers is weak and precarious. To work with clusters, it’s important to understand the complexity of connection, challenges, and existing solutions. As a data engineer, you will encounter cluster related challenges. Having a solution ready in advance will help you emerge as a good data engineer.
6. Data pipelines
Data pipelines are the lifeline of data operations. Most of the time of data engineers are spent building pipeline data, which is creating a process to deliver data from one place to another.
This could include scripts that trigger custom API from external source or SQL the query that extracts data and delivers it to a data warehouse (structured data) or a data lake (unstructured data).
Where can you get the knowledge to become a data engineer?
Unlike data science, there are no formal degrees in data engineering. Most data engineers today are self-taught who came from a database or software engineering background. Fortunately, now, the growing popularity of data engineering has led to the development of structured programs like DASCA’s big data engineer certifications, which allow aspiring and experienced data engineers to learn data engineering inside out, along with getting their skills vetted by an extremely stringent competency assessment framework. Alternatively, you can go for following popular data engineering certifications –
1. IBM Certified Data Engineer – Big Data
2. Cloudera Certified Professional ( CCP) – Data Engineer
3. Amazon Web Services ( AWS) Certified Big Data – Specialty