Data Engineering Services

Data engineering as a service is the process of designing and building systems that allow the collection of raw data in various formats, from multiple sources, to support business goals. Beyond analysis alone, data in data engineering solutions can be used for storage, processing, visualization, integration, product development, etc. At Emerline, we provide data engineering consultancy and solutions to empower businesses to gain meaningful insights from data.

Our data engineering company delivers solutions that can help your business:

• Improve data quality by ensuring its accuracy and consistency through effective data pipelines.
• Facilitate and accelerate the decision-making process thanks to real-time access to data.
• Increase scalability, as our data engineering solutions allow handling large data sets and even ever-growing data volumes with equal efficiency.
• Increase security and maintain compliance with the help of Emerline’s professional data engineers and data engineering consultants who work closely with regulators to make sure data is compliant, thus helping customers avoid fines and penalties due to regulatory violations.
• Provide you with more business insights through real-time analysis of data sets.

Data engineers are generally responsible for building, optimizing, and maintaining data pipelines and the whole data infrastructure. On the other hand, data scientists analyze these data pipelines’ outputs to make the most of them through statistics and (or) advanced machine learning algorithms. They build complex models for analyzing data and communicate the results of their analysis to business leaders. Data science also provides the foundational techniques, methodologies, and tools that enable the development and deployment of Generative Artificial Intelligence systems. In the past, data scientists handled many more responsibilities, part of which now falls in the data engineer’s domain.

Various industries can apply data engineering solutions. Accordingly, the use cases vary depending on the industry. Some of application examples include:

• The optimization of E-commerce supply chains by ensuring seamless data flows across suppliers, warehouses, logistics partners, distributors, and other parties involved.
• Energy consumption monitoring with consumption data available in real-time.
• The ongoing traffic management and monitoring via efficient aggregation, processing, and analysis of data from different sources, such as traffic cameras, GPS devices, traffic sensors, and others.
• Financial portfolio management, where vast amounts of real-time and historical data are aggregated from multiple sources, which enables more informed investment decisions.
• The personalization of marketing campaigns via sophisticated data pipelines that collect and consolidate customer data about purchase history and consumer behavior (browsing, purchases, upsells, cross-sells, etc.) to help businesses rationally segment customer audiences, and others.