While financial and e-commerce companies have already gone completely digital and are using advanced technologies, other industries such as retail and CPG are catching up. Traditional organizations that were hesitant to monitor anything apart from transactional data are now pumping resources to leverage data to optimize customer interaction and provide them with a personalized experience by understanding their preferences and responding quickly. They are also driving operational efficiency and innovating with business models and products to emerge better than their competitors. Much of this could be attributed to the lowered data processing costs and the emergence of newer platforms that have made tracking customer behavior quite prevalent for these companies. the availability of such a massive amount of raw data has left companies struggling to use it effectively. This data of no use until one derives insights to make business decisions. As the world consumes more and more data, businesses increasingly require experts to handle large volumes of customer information, conduct competitor research, and product performance results.
is a leading data solutions company enabling Fortune 1000 companies to become data-driven using data engineering, data science, and AI/ML. The company was recently in Inc. 5000’s list of fastest-growing private companies in the US.
Speaking about Sigmoid’s data engineering capabilities,said: “Our goal at Sigmoid has always been customer success. However, when we started working in this space, we realized that most customer projects failed not because their ML models were unsuccessful or the dashboards were not useful, but because they utilized outdated data engineering practices or traditional software engineering practices. Sigmoid has developed data agile processes that enable customers to reduce the risks while building data pipelines and make the project successful.”
In our Analytics India Magazine delved into how Sigmoid stands firm on the pillars of business consultancy, data science, and analytics. In this article, we will turn the spotlight on its data engineering capabilities and offerings.
Data Engineering: Moving Beyond Just Software Engineering
Software engineering has been popular for the programming languages it offers, object-oriented programming, and developing operating systems. However, as companies are witnessing a data boom, the conventional wisdom of software engineering fails to process big data. With a new set of tools and technologies, data engineering allows companies to collect, generate, store, process, and manage data in real-time or in batches while building data infrastructure.
Traditional software engineering practices involve designing, programming, and developing software that is largely stateless. On the other hand, data engineering practices focus on scaling stateful data systems and dealing with different levels of complexity. Additionally, there are also differences in the complexity of the two fields in terms of scalability, optimization, availability, and agility, which are mentioned below:
- Data engineering addresses scalability in the form of three V’s – velocity, variety, and volume of data, which software engineering doesn’t focus on.
- The next difference is the optimization of code. The exponential increase in performance of modern computing enables software engineers to focus on writing cleaner and more understandable code rather than hyper-optimized one. However, for data engineers optimizing code can reduce the data processing cycle from days to hours.
- The third difference between data engineering and software engineering is availability. It means that even if one loses an hour of data processing, the data pipeline would now have to work at two times the speed and much higher volume.
- Lastly, while the traditional software engineering process works in an agile fashion, data engineering approaches the problems at a micro-level, involving practices like DataOps to focus on data management practices that improve quality, speed, and accuracy manifold.
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