Big data is not the biggest change in the IT industry but data usage. To become more data driven and to succeed with their digital transformation, organizations are using their data more extensively to improve their business and decision processes. Unfortunately, it is hard for current data delivery systems to support new styles of data usage, such as data science, real-time data streaming analytics, and managing heavy data ingestion loads. This session discusses a real big-data-ready data architecture.

Target Audience: Data architects, data warehouse designers, IT architects, analysts, enterprise architects, solutions architects
Prerequisites: Some understanding of data architectures
Level: Advanced

Extended Abstract:

It is not big data that is the biggest change in the IT industry, it is data usage that has changed most drastically the last ten years. To become more data driven and to succeed with their digital transformation, organizations are using their data more extensively to improve their business and decision processes. This may mean that more data needs to be collected resulting in big data systems, but the goal remains to do more with data. Unfortunately, current data delivery systems don't have the right characteristics for supporting the requirements of big data systems, such as supporting data science, handling massive data streaming workloads, managing heavy data ingestion loads, and so on. Additionally, many organizations that are developing big data systems, are developing isolated data delivery systems, such as an isolated data lake, an isolated data streaming system, an isolated data services system, and so on. This avalanche of data delivery systems is not beneficial to the organization. The wheel is invented over and over again. It's time to design real big-data-ready data architectures. This session discusses such an architecture, including the supporting technologies, pros and cons.