In today’s data-driven world, the ability to analyse vast amounts of information quickly and effectively is crucial for businesses to remain competitive. Traditional business intelligence (BI) tools, often limited by on-premises infrastructure, are increasingly giving way to cloud-native analytics solutions. This evolution is charged by the need to manage the 4 V’s of big data: Volume, Velocity, Variety, and Veracity.
Understanding the 4 V’s of Data
Volume: The sheer amount of data generated daily is staggering. From transaction records to social media interactions, businesses are overloaded with information. Cloud-native analytics platforms can handle massive datasets that on-premises systems struggle to process. Excel has limits or rows, Power BI semantic level can crush, and Pandas in Python may be unable to process big data too quickly. A tool that will work with this volume needs to be developed.
Velocity: In an era where real-time insights are non-negotiable, the speed at which data is generated and processed is critical. Cloud-native solutions provide real-time analytics, allowing businesses to react promptly to market changes and customer needs. For example, at a satellite communication company, the velocity is massive: hundreds and thousands of rows of data rolling to the data lake every second, while for a media agency company, especially the out-of-home media, this velocity will be much smaller.
Variety: Data comes in various formats, including structured, semi-structured, and unstructured. Cloud platforms can integrate and analyse diverse data sources seamlessly, enabling organisations to derive insights from multiple pieces of information—some platforms, such as Splunk, process only machine data in human-readable format.
Veracity: The accuracy and trustworthiness of data can significantly impact decision-making. Cloud-native analytics tools often include advanced data cleansing and validation features, ensuring businesses rely on high-quality data for their strategies. For example, on DBT, as an analytics engineer, we can set up different tests to test the data before injecting it into the model.
The Role of Cloud Platforms
Cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud provide the infrastructure for cloud-native analytics. These platforms offer scalable storage solutions, powerful computing resources, and analytics tools that empower organisations to perform complex analyses without physical hardware limitations. They enable businesses to directly leverage advanced technologies like machine learning and artificial intelligence within their analytics processes, enhancing data-driven decision-making.
Benefits of Cloud-Native Analytics
Adopting cloud-native analytics offers numerous advantages:
Scalability: Organizations can quickly scale resources up or down based on demand, ensuring they only pay for what they use.
Cost Efficiency: Businesses can significantly reduce operational costs by eliminating the need for extensive on-premises infrastructure.
Accessibility: Cloud-native solutions provide access to analytics tools from anywhere, fostering collaboration among teams and supporting remote work.
Integration: These platforms enable seamless integration with other cloud services, creating a unified ecosystem for data storage, processing, and analytics.
Real-Time Insights: The real-time analysis of data allows organisations to make timely, informed decisions that drive competitive advantage.
Migration from On-Premises to Cloud
At Inmarsat, I was key in a complex data migration project, transitioning data from an internal legacy system like SDP and Peoplesoft to the Google Cloud Platform (GCP). The work was positioned between the Data Engineering and Network Intelligence teams. I worked on the data migration strategy by extracting data from BigQuery, transforming it using DBT (Data Build Tool), and loading it into the appropriate new tables. My responsibilities included the entire migration lifecycle, from the initial work assessment and preparation stages to the execution of data extraction, transformation, and loading into new assets. I also managed validation and testing, ensuring that all new data assets were functional and aligned with the needs of the Network Intelligence team.
One of the critical tasks I handled was User Acceptance Testing (UAT), where I coordinated with stakeholders to ensure the new data systems met business expectations. After the migration, I provided post-migration support, helping resolve any outstanding issues and ensuring a smooth transition to the new system.
Conclusion
Cloud-native analytics is undoubtedly the future of business intelligence, offering businesses the agility, scalability, and power needed to success in a data-centric world. By embracing the cloud, organisations can harness the 4 V’s of data more effectively, driving innovation and competitive advantage.
