One of the biggest issues is to load files or other type of formats in a standard database, before doing something with the data. Although this can be achieve using an automated way, sometimes business requirements leads to creating a specific workflow for data loading.
One approach to maximize the income revenue of a data-driven company is by selling it's own data. If done right, this is painless and - for the record - the way that all business should approach their data, to sell or not. We can help you monetize your data and insights. This is how we start...
Out of order events is a challenge not just in stream processing but also in tradicional ETL systems. Because in a normal ETL you process your data in a batch oriented way, this can be controlled, but in a multiple sources and real time environment this needs to be deal with.
Machine learning is here and there's a lot of missing steps that hinders the adoption by data engineers - and by customers. This is the first post that I will be kicking off with local environment setup and with a quick example that compares algorithms for the same dataset.
Power plants are challenged to generate value from their data, but this can be a tedious and slow process, with uncertain outcomes. Now, as shown in these use cases, data analytic solutions can put innovation in the hands of process engineers and experts for rapid and useful insights.
Netflix is the world's leading internet television network. That didn't happen by accident or simple fortune - we are data-driven as part of our culture, and have built the tools needed to navigate the unchartered waters of delivering internet video at scale and becoming the first truly global storyteller in movies and television.
Data as a service (or DaaS) builds on the concept that the product (data in this case) can be provided on demand to the user regardless of geographic or organizational separation of provider and consumer.
Traditionally, most organisations have used data stored in a self-contained repository, for which software was specifically developed to access and present the data in a human-readable form. One result of this paradigm is the bundling of both the data and the software needed to interpret it into a single package, sold as a consumer product. As the number of bundled software/data packages proliferated and required interaction among one another, another layer of interface was required. These interfaces, collectively known as enterprise application integration (EAI), often tended to encourage vendor lock-in, as it is generally easy to integrate applications that are built upon the same foundation technology.