How to be a Specialist talent in Data Engineering
A brief look at the must-have technical skills, soft skills, the latest Industry trends & more.
“The world is one big data problem.”
— Andrew McAfee
What is the most valuable resource on our planet right now? It’s not oil. Data is the ‘new oil’. But data is only valuable when it is aggregated and used for pattern recognition. Even data that does not prove to be useful at initial assessment, if correlated with relevant data, can give a whole lot of insights. Contextualisation is key to making data useful from a business perspective. This is where Data Engineering comes in. At its core, Data Engineering is all about building comprehensive systems that prepare and streamline data for further analysis. The task set for data engineers is to have the data neatly acquired, processed, modelled, organized and accessible. Eventually, data engineers make way for other specialists to turn data into clear metrics that can drive decision making.
Seen through this lens, the whole world is, ‘one big data problem’. Technology has evolved to be symbiotic with human societies. In a world mediated by technology, everything is a potential mine for enormous data. From supermarkets to space stations and social media, everything generates data. Data engineering has created a lot of buzz in recent times. Let’s delve deeper and uncover important insights for professionals in the sphere.
How does Data engineering empower the tech industry and the business world?
Data Engineering enables teams to utilize data seamlessly and efficiently. It paves way for the data scientists to break down complex business issues through data. Data Engineering requires a complete comprehension of industry-related technologies & tools along with quick execution of complex datasets with reliability.
The responsibilities of a data engineer revolve around the process of designing and building frameworks for gathering, storing and transforming data at scale. It is a wide field with applications in every industry. Organizations can gather large volumes of data, and they need expert talent and advanced technology to ensure that the data is in an operable state when it is sent to data scientists and analysts.
Working as a data engineer can provide the opportunity to make a tangible difference in a world that is slated to produce 463 exabytes of data per day by 2025 (which is one and 18 zeros) and can make the lives of data scientists easier.
Increase of demand in the data engineering niche
The demand for data engineering is on the rise and it is considered the fastest-growing career option in the technology sector. The year-on-year growth in the number of open positions for data engineers continues to accelerate.
In recent research by Xpheno, In august 2021, start-up companies were found to have contributed to 13000 active data engineering jobs with a comprehensive salary package. Hikes in this particular domain are expected to be in the 50-80% range for people in the 3-6 year experience category. The key hiring sectors are IT, Products, GIC’s, IT services.
Technical skills that are of prime importance
Data engineers are expected to have profound knowledge in the following technical skills.
- Cloud Computing
- SQL and NoSQL
- Data Modelling
- Data Warehousing
- Machine learning
- Programming languages
The tried and true process that data engineers use is called ETL — Extract, Transform, Load. With the ever-increasing data pool and the availability of cloud storage, the need for ETL is augmenting more than ever. For this reason, data engineering tools that support ETL or ELT processes are critical. Some of the common data processing, streaming and ETL tools are Hevo Data, Apache Kafka, Pentaho, AWS Glue and Azure Databricks.
SQL and NoSQL
SQL is Standard Query Language that aids in querying relational databases. Structured Query Language (SQL) is the standard language for querying relational databases. The uses of SQL include modifying database tables and index structures; adding, updating and deleting rows of data and retrieving subsets of information from within a database for transaction processing and analytics applications.
NoSQL is flexible, scalable, cost-efficient, and schema-less databases. It comes in different varieties and at a high pace. In comparison with SQL databases, they are of multiple types: document-based and key-based, wide column-based and graph-based. Many NoSQL databases are designed to support seamless, online horizontal scalability without significant single points of failure. It enhances the data performance with the combination of handling larger data volumes, the reduction of latency, and the improvement of throughput.
Hence, Data engineers need SQL and NoSQL to integrate and consolidate data from various sources
Data modelling is the process of creating a visual representation of either a whole information system or parts of it to communicate connections between data points and structures. The goal is to illustrate the types of data used and stored within the system, the relationships among these data types, the ways the data can be grouped and organized and its formats and attributes. Data modelling knowledge is quite important now, in the sense that a Data Engineer needs to know how they are going to structure tables, partitions, where to normalize and denormalize data in the warehouse, etc. and how to think about retrieving certain attributes.
A data warehouse is a type of data management system that is designed to enable and support business intelligence (BI) activities, especially analytics. Data warehouses are solely intended to perform queries and analysis and often contain large amounts of historical data. The data within a data warehouse is usually derived from a wide range of sources such as application log files and transaction applications.
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. It is also important to understand machine learning models and algorithms for big data processing.
Python is the top programming language utilized for Data Engineering. Java is generally used in data architecture. Scala is an extension of the Java language that is interoperable with Java as it runs on JVM (a virtual machine that enables PCs to run Java programs). Scala is used to break down information and to set up statistical models and dashboards.
What role do soft skills play in the data engineering domain?
Aside from the strong technical skills, data engineers need to acquire some essential soft skills. More than 4,000 software engineers, developers, managers, and executives surveyed in LinkedIn’s ‘Workplace Learning’, chose soft skills as their number one priority for talent development for data engineers and those in related fields.
Following are some of the soft skills that every data engineer masters to be a specialist talent:
- Business Acumen
- Presentation skills
- Passion for data
- Critical thinking
Business acumen skills are all about the ‘know-hows’. It is knowing how markets work, having the right insights on trends or technology that are reshaping markets. With Business Acumen, Data Engineers can accurately acquire datasets that align with business needs. By developing algorithms that transform data into actionable information, data engineers can help organizations streamline performance, grow market share and outperform the competition. Data professionals need to develop a clear picture of how their work can achieve objectives and translate to business value. When they don’t understand the business, they cannot effectively optimise data for actionable insights.
In addition to communication skills, data engineers should be able to work with groups, individuals from various backgrounds and personality types. This skill includes understanding a variety of perspectives, managing priorities from everyone in the group, and meeting expectations as a reliable member of a team. Successful collaboration requires a cooperative spirit and mutual respect.
Communicating and interacting with others effectively is a routine job of businesses today. Presentation skills are the abilities that enable an individual to interact with the audience, transmit the messages with clarity, and interpret and understand the mindsets of the listeners. Having good presentation skills adds great value to the entire organization.
Passion for data
When a person has passion for data, they will never stop staying curious about the trends and will always keep updating themselves in the particular field. This in turn will make them grow professionally.
Data engineers must learn a lot on their feet. As data volume grows, organising it becomes a challenge. There are no one-size-fits-all solutions for tackling projects. When given a problem to solve, one should be able to break down the problem into the basic elements by asking questions, analyzing and coming up with solutions is one of the key principles of problem-solving.
Alternative career options that demand a similar skillset:
The alternative career paths one can explore with similar skillsets and knowledge are,
- Data Scientist
- DevOps Engineer
- Data Architect
- Solutions Architect
- Machine learning Engineer
Certifications one can pursue to learn data engineering
There are plenty of popular courses that are available online. To master data engineering and succeed in this niche, taking up courses can add more feathers to the hat. Listed below are some power-packed courses to start your learning journey.
- Data Engineerings Foundation Specialization (Coursera)
- Microsoft Azure for Data Engineering (Udemy)
- Data Engineering, Big Data, and Machine Learning on GCP Specialization (Coursera)
- Data Engineering Essentials Hands-on – SQL, Python and Spark (Udemy)
- Cloud Data Engineering (Simplilearn)
- SQL Concept in Data Engineering (Udemy)
- Python for Data Engineering Project (Coursera)
Communities for Data engineering
- Data Council
- AWS Developer Community
- Stack Overflow
To put it concisely, with rapid technological evolutions, data engineering is headed for complete transformation. It is one of the most promising technologies which will make headway into the future. Data engineers will also have a fantastic opportunity to own the shift towards treating data as a product, building operational, scalable, observable, and resilient data systems.