Data Architecture & Engineering
DAIN Studios' Services
Data Agility
Data architecture and engineering are foundational elements in making data easily accessible. To keep up with changes in today’s business environment the data architecture needs to allow monitoring of your vital business operations to the right level of detail and be adaptive to changes in the business environment.
Data architecture and engineering
Building the Modern Data Architecture
DAIN Studios’ architecture and engineering services design and build modern data architectures using tested technologies and reevaluate and recommend improvements to existing architectures.
Data & Solution Architecture
Efficient data utilization requires the gathering, processing, and analyzing of data. An efficient data and solution architecture processes different types of data in a modular and flexible fashion. Different technical solutions fit smoothly to one another. We help you design a future-proof data and solution architecture to fit your use cases.
Technology & Tool Selection
We help you set up your data and AI platforms. This includes the evaluation of best technologies, service models, and cost structures and choosing the most suitable technology stacks for you. We are technology agnostic and work with all technology and tool providers.
Data & ML Engineering
We develop, maintain, and evaluate the data infrastructure and data models for data scientists and BI developers. Our data engineers integrate raw data from various source systems into analytics environments to support data discovery, analytical model development, and BI needs. After the Machine Learning (ML) models have been developed, our ML Engineers optimize and automate them. We apply our extensive knowledge of databases and best engineering practices.
Data Management & Data Cataloging
Solid data management practices ensure that data is of high quality, reliable, and usable. This includes having common standards and definitions in various operative and analytical systems as well as solid metadata, taxonomy, and cataloguing practices. Data models are defined, and master data practices implemented. We follow the FAIR (findable, accessibility, interoperability, usability) principles in data management.
Self-Service Data Platform
No one knows your business better than you and your employees. To enable 'citizen data scientists', it is necessary to build platforms that enable you to derive new insights from data in a self-reliant way. We build the platform and train your employees to perform queries and generate reports on their own - with minimum support by your IT department and analytics teams.
How to succeed in Data & AI transformation?
Introducing the DAIN Data & AI Maturity Model.