The infrastructural challenges of working with data modernization tools and products has often mandated a simplicity in the overall deployment. Consider an application such as a Streaming Data Platform and its deployment on-premises includes several components for the ingestion store and the analytics computing platform as well as the metrics and management dashboards that are often independently sourced and require a great deal of tuning. The same applies to performance improvements in data lakes and event driven frameworks although by design they are elastic, pay per-use and scalable.  

The solution integration for data modernization often deals with such challenges across heterogeneous products. Solutions often demand more simplicity and functionality from the product. There are also quite a few parallels to be drawn between solution integration with the cloud services and the product development of data platforms and products. With such technical similarities, the barrier for product development of data products is lowered and simultaneously the business needs to make it easier for the consumer to plug in the product for their data handling, driving the product upwards into the solution space, often referred to as the platform space. 

Event based data is by nature unstructured data. Data Lakes are popular for storing and handling such data. It is not a massive virtual data warehouse, but it powers a lot of analytics and is the centerpiece of most solutions that conform to the Big Data architectural style. A data lake must store petabytes of data while handling bandwidths up to Gigabytes of data transfer per second.

Data lakes may serve to reduce complexity in storing data but also introduce new challenges around managing, accessing, and analyzing data. Deployments fail without properly addressing these challenges which include:  

• The process of procuring, managing and visualizing data assets is not easy to govern. 

• The ingestion and querying require performance and latency tuning from time to time. 

• The realization of business purpose in terms of the time to value can vary often involving coding. 

These are addressed by automations and best practices.