Data Engineering Solutions

Summary

With recent rapid technological changes and the use of big data, the market demand for the automated processing of massive amounts of data is increasing. Meanwhile, this industry is relatively new and still developing, so there is a lack of know-how and experience everywhere.

Experts in software engineering or cloud technology offer various solutions and services for general cases. However, offering appropriate solutions for edge cases might be difficult for them. Depending on the customer's business and the quality of the data that the customers have, we have to dig deep.

We address these edge cases and try our best to find the optimal solution for the customer's problem by digging deep into the business and data.

Example 1: Data Processing in Insurance Companies

We have experience working on data collected from one of the biggest insurance companies in the world. We designed and implemented a data processing flow to collect and convert a vast amount of raw data of millions of customers, which makes the data available to analyze the end user's lifetime events and daily lifestyle.

Example 2: MLOps and Continuous Delivery of ML Models

There are many probable reasons that big data-related projects could fail, and one of the common reasons is usability, which stands for not being able to deploy machine learning models smoothly. Companies, organizations, and individuals mostly pour plenty of time and resources into R & D projects, but sometimes they forget to improve the model's usability. To address this issue, we developed a machine learning pipeline for one of our customers, which automates the entire process from R & D to deployment.

Example 3: Mining Historical Data

One of our partners is building an extensive world history database. It is challenging to make it possible to collect a significant amount of information on such a broad field with human hands. We are working with them to solve this problem and are in charge of the automated data collection and cleansing.

Example 4: Data Pre-processing in Heavy Industry

Also, we had an end client who specialized in processing metal, an essential raw material for automobile makers and construction companies, in various ways and putting it into business. Our customer is making their entire business process data-driven, and we are working on the data-processing part by introducing the emerging concept called ELT (Extract Load Transform).