Written by Arnab Majumdar
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In the modern times that we live in, any interaction done on a digital platform leaves a data trail. This becomes an invaluable source of information for a company or a brand – which can use data to run its business and build strategies based on the evidence.
Data Engineering is the discipline which helps build processes allowing an organization or entity to collect and analyse the raw data which is scattered across multiple sources and is available in multiple formats (unstructured, semi structured or structured). Since the data sets are large and complex, it is often referred to as Big Data. The process entails collection, storage, formatting and analysis of Big Data by Data Scientists to derive insights.
The task can be challenging if the source data is managed by different technologies – for example in the case of an online store, all the data pertaining to billing, shipping, customer support or third party is available to the business but they are unable to draw inferences since the data is stored and managed by different system. Data Engineering streamlines this process and helps the business take decisions based on the customer data.
Data Engineers are in high demand. Previously they worked with data warehouse schemas with tabular structures and indices to process information quickly. With the advent of data lakes they work with significantly higher unstructured data volumes. Such data needs be cleansed and formatted for use by stakeholders in analysis and decision making.
The key duties that a Data Engineer performs can be enlisted as:
Data acquisition – sourcing the data
Data cleansing – identifying and rectifying errors in data
Data conversion – extraction of all data in a common format
Data disambiguation – making sense out of the data
Data deduplication – removing any repetitions in the data.
To undertake their job, Data Engineers create end to end pipelines which move data from the source to the target destination with the use software which automate the process. Some of the tools and technologies deployed in Data Engineering are:
1. ETL(Extract, Transform, Load) tools which transform the data to make it easy to analyse.
2. SQL(Structured Query Language) which is a domain specific programming language used for storing and processing data in a relational database management system in a tabular form with rows and columns.
3. Python is an advanced programming language used in Data Engineering for performing ETL tasks. Several other tools are used, e.g. PostgreSQL, MongoDB, Apache Spark, Apache Kafka, Amazon Redshift, Snowflake, Amazon Athena, Apache Airflow. Data Engineers need to work with the tools best suited to their organizational needs.
4. Cloud Data Storage - The data is stored remotely in offsite servers and maintained by third party service providers who host, manage and secure the data. Some examples of Cloud Storage are Amazon S3, ADLS, Google Cloud Storage etc.
5. Query Engines - Several query engines are used in data engineering, where queries are run to return answers.
Data is often referred to as the new oil – which serves as the basis on which business decision making is done. Data Engineering is fast emerging as a career choice for many. Until few years ago there was hardly any demand for Data Engineers - but now as the industry has matured, the requirement has skyrocketed.
The key skills required for a career in Data Engineering are:
1. Technical Skills
a. Programming Skills: The data engineer needs to be proficient with the widely used programing languages like Python, SQL and keep updating their skills during their career span
b. Data Warehousing: Database design, Query Optimization and Schema modelling knowledge is required with working knowledge of data engineering tools like Amazon Redshift, Google Big Query, Apache Spark, Tableau etc.
2. Soft Skills: Along with the technical knowledge of programming and tools, a Data Engineer needs to possess the necessary soft skills to perform his duties. The top essential soft skills include:-
a. Problem Solving: identify and address problems.
b. Empathy and adaptability: needs to understand the client perspective and respond.
c. Time Management: adhering to deadlines.
d. Documentation and presentation skills
e. Effective communication
f. Agile and continuous learning
In the days ahead companies are increasingly investing in data driven initiatives and thus the future of the industry looks extremely bright. Data is also used to train the adaptive Machine Learning (ML) algorithms and thus plays a pivotal role in predictions and decision making in Artificial Intelligence (AI).
As per Industry specialists few of the emerging trends in data engineering include:
Cloud-native approaches e.g. server-less architecture, implementation of advanced cloud based analytics
Data mesh implementation which will improve data discovery, ownership and capability to manage more complex data systems
AI and ML integration will seamlessly embed Machine Learning models in data pipelines and drive automation and data governance
Real-time continuous data processing
Low and no-code data engineering is allowing non coders to design data pipelines
The world will unequivocally be driven by decision making backed by data analysis. Thus data engineering is going to remain as a most sought after skillset. Data Engineers play the pivotal role of building a robust and scalable data pipelines and laying the architecture though which an organization is able to extract and analyse data and make decisions based on the analysis. As the industry continues to evolve and new tools and technologies are introduced, the role of the Data Engineer will increasingly be more critical. It will continue to be the most sought after skillset in the job market.
In order to build a career in the fascinating field of Data Engineering, a candidate needs to research any of the career courses being offered in the field. For any Bengali student who wants to understand the concepts in a lucid way in their native Bangla language, Vidyastu offers a training program in Data Engineering. Apart from theoretical lessons students can also get hands-on experience and placement assistance in the course from Vidyastu.