Data is used in many ways, and the volume, variety, and velocity of that data increase every day. Because of this, organizations rely on lots of different data technologies.
One way to think about the technologies in a data platform is to divide them into three categories based on the kind of data they work with. Those categories are:
Operational data, such as transactional data used by a banking system, an online retailer, or an ERP application. Today, working with unstructured operational data can be just as important.
Analytical data, such as the information kept in a data warehouse. This data is typically read-only, and it usually includes historical information extracted over time from other data sources, such as operational databases. Analytical data is commonly used for things such as business intelligence and machine learning, and like operational data, it can be either relational or unstructured.
Streaming data, such as data produced by sensors. The defining characteristic of streaming data is velocity; if the data isn’t processed quickly, it can lose a large share of its value. Many streaming scenarios today relate to the Internet of Things (IoT), where the focus is on interacting with data provided by lots of devices. Streaming data is also used in other situations, such as analyzing financial transactions as they happen.
The Microsoft data platform provides technologies for all three categories, along with connections among the three. The below figure summarizes the platform’s offerings
Cloud Kinetics has been able to address variety of Customer use cases ranging from Web Analytics for a Live Video Streaming Company, to Consumer Loyalty Analytics provider for a leading airline in South East Asia, to Plan Effectiveness Analysis in Container Repositioning for a leading Shipping Liner.
'One of our reference architecture involving HDInsights, DataFactory, SQL Datawarehouse and Power BI for Weblog Analytics use case
Our value adds:
Architect with the right Azure Big Data components based on available skills with Customer team in mind, incorporating best practices of security and scalability.
Architect & Provision appropriate Big Data infrastructure to implement the solutions
Automate Data Orchestration and Administrative work efforts in Production