Designing and building a big data ecosystem comes with challenges as well as expectations based on user needs and industry trends. Here are a few examples of what we commonly face when companies come to us with big data ecosystem projects:
When working with big data, it’s assumed that the volume of the input to our process may be unknown. But the process needs to be predictable enough that it doesn’t require any additional modifications to perform as expected.
Reporting is a cornerstone of data-driven management, as it’s in the nature of business to summarize metrics while slicing and filtering them through different variables. Think about different machine-learning algorithms that not only are more complex while diving into different combinations of variables on the lookout for patterns, classification of criteria, or clustering, but also are usually developed for standalone implementations by subject-matter experts of different areas. This can leave performance predictability, distributed processing, and high availability out of development scope.
The problem that big data experts are facing is how to translate these algorithms into the scope of a perfectly scalable process, which we define as a completely auto-adjusted process. Whether we are talking about an unforeseen acceleration of data volume, an additional accumulation of unprocessed data, a change of the data input schema, an increase in the number of consumers, or even unexpected hardware failure, the system needs to adjust quickly and remain predictable.
What drives an organization to make a large investment in big data analytics are business constraints that are set by product owners or higher management within the business. The architect is in charge of making the technological decisions that will accommodate all of the business’ needs and anticipate everything that can possibly become a problem once everything starts to run.
Discovery is one of the keys to success in every big data process. It consists of a general assessment driven by questions that address both the technical and business aspects related to the organization:
The data sources are usually a business constraint. They may come from relational databases, plain text files, APIs, social media, and enriched content. Due to this, data ingestion puts a number of languages and strategies on the table, and for organizations this means that different areas will have to work along with the big data architects to achieve the right integration.;