
Covers design of data platforms and architecture for handling large data volumes in complex data environments.
Focuses on developing solutions for processing and analysing large datasets across systems and platforms.
Covers establishing and structuring data lakes where large volumes of raw data are collected and stored for analysis.
Covers development of data pipelines and integration processes where data is extracted, transformed and loaded into data platforms.
Focuses on developing solutions that collect and manage data from devices and sensors in Internet of Things environments.
Covers implementation and optimisation of data platforms based on Snowflake for cloud-based data analytics.
Data platforms and IoT play a central role in organisations working with large data volumes and aiming to use information from multiple sources.
When data is collected from systems, applications and devices, it becomes essential to have a structure that enables efficient storage, processing and analysis.
Many organisations work with data from a wide range of sources, including business systems, digital platforms, sensors and external data sources.
As these sources grow in number and complexity, there is a need for a central platform where data can be consolidated and structured.
Data platforms make it possible to create a shared foundation for analysis, reporting and data-driven decision-making.
Data platforms require a clear architecture that can handle storage, integration and processing of data.
Big data architects and data engineers define how data should be structured, while ETL developers build pipelines that move and transform data between systems.
When data architecture and integration are planned together, it becomes easier to create stable and scalable platforms.
The Internet of Things enables data collection directly from devices and sensors. This data can provide insight into processes, usage patterns and operational conditions.
When IoT data is integrated into data platforms, organisations can analyse it alongside other data sources and create new opportunities for optimisation and automation.
This requires a robust technical structure to handle large volumes of data and real-time processing.
Building and developing data platforms often requires specialised expertise in data architecture, integration and cloud-based platforms.
Many organisations therefore complement internal teams with external specialists. A big data architect can define platform structure, while developers build pipelines and integration solutions.
IoT developers and platform specialists can work with sensor data integration and cloud-based platforms.
Bringing in the right expertise makes it easier to establish solutions that can handle growing data volumes.