Big Data and Analytics

How to structure a Big Data Ecosystem in an organization?
Friday, September 9, 2016 3:50:43 PM

 When it comes to designing as well as creating a Big Data ecosystem, challenges and expectations on the basis of the industry trends as well as user requirements are quite obvious. At times, businesses are aware of the value of their data but they are unaware of the process of optimizing it. At times, the outcome remains unclear even after the establishment of a processing pipeline. Even, at times, organizations cannot decide the data inputs required to reach their predefined goal.

While working with Big Data, people usually expect that the input volume to the process might be unknown. However, the process should be predictable so that it does not need any further modifications to work as expected.

When it comes to data-driven management, reporting is considered the cornerstone because a business’ nature is to sum up metrics and slicing as well as filtering them using various variables. For example, various machine-learning algorithms that are complex at the time of diving into various combinations of variables while looking out for clustering, classification of criteria, or patterns. Besides, they are generally developed for various stand-alone implementations by different areas’ subject matter experts. This can result in keeping distributed processing, high availability, and performance predictability out of any development opportunity.

The major challenge to Big Data experts is to translating these algorithms into the possibility of an appropriately scalable process, which is known as a totally auto-adjusted process. It is important for the system to adjust rapidly and stay predictable no matter whether it is an unexpected expedition of data volume, the changing data input schema, a rise in customers’ number, or the hardware failure unexpectedly. 

Most of the time, various business constraints, which are mainly set by senior management or product owners in a business, drive a company to invest significantly in Big Data Analytics.  The architect is mainly responsible of taking technology-related decision in view of all business requirements and project problematic/challenging parts following the implementation of the process.

Discovery as well as Business Mapping

Discover is an important success key to each Big Data process. It comprises a common assessment propelled by the questions that cover both the business and technical aspects pertaining to the company.

  • What is the projected result of our effort? Is there any?

  • What are the predictable sources of data?

  • What and who will be the data consumer?

  • What are the business challenges and maturity in terms of data governance?

Creating smart solutions related to Big Data

Data sources are mainly considered as a constraint to the business. They may emerge from enriched content, APIs, social media, plain text files, and relational databases. Therefore, data ingestion brings several strategies and languages on the table. This means for organizations that various areas should work, in addition to Big Data architects, to ensure the appropriate integration. No matter whether it is Java, Python, SQL, or other specific solutions, it is challenging for the IT company in change to understand that each piece is important.

Products and outcomes are variable extensively. However, different trends have come up over a period of time. At present, several interactive dashboards and recommendation engines following various behavioral patterns are available. On the other hand, content-based machines make an analysis of various customer attributes and then, compare these attributes with several products available in the market. Further, the use of search engines is also growing and these engines mainly focus on sematic search and lexicographic analysis.

Various optimization problems call for a Big Data framework and they come with various complexity levels—real-time processing, batch processing, and so on. It’s therefore important to group all problems into same categories, such as Data Quality, Storage, and Analytics and exploitation.

If we consider from the architectural perspective, this does not seem very clean. Many tools overlap when it comes to functionality and many tools may require customization for the application. Indeed, the ecosystem is so big that two totally identical implementations are yet to be seen.