Emerging Big Data Trends

Big Data is a very wide area. Unified Continuous Ingestion, Polyglot Persistence, Real Time Streaming, Context Aware Computing, In-Motion Analytics & Machine Learning are the trends we embrace, provide and plan to support.

Unified Continuous Ingestion: next generation digital analytics require delivering just-in-time data and feedback.
Streaming data requires resilient infrastructure that can move data at scale from heterogeneous data sources, process data quickly for use across several data pipelines, and serve the data to a variety of applications.

Polyglot Persistence: according to Gartner: “The growing hype surrounding data lakes is causing substantial confusion in the information management space”.
Data Lakes carry substantial risks such as inability to determine data quality, lineage of findings by other analysts, lack of security and risk control, lack of performance against optimized and general purpose-built infrastructure.
Gartner recommends that organizations focus on semantic consistency and performance in upstream applications and data stores instead of information consolidation in a data lake. The data lake risks turning into a data swamp.
We envision hdfs as a Data Stage layer, a storage area where to collect and temporarily store data. We finally embrace polyglot persistence, the right tool for the right job, or the multi-model database, documents, graphs and relational tables on a single back.

CEP & Real Time Decision: the ability to process and analyse events, applying complex logic, in real time in order to provide timely support to decision makers may be the difference between having or preventing a problem as well as winning or losing an opportunity. Enterprises will focus on analysing complex data pipelines that provide real-time information directly from their sources.

Context Aware Computing: according to Gartner: ”Context-aware computing (CAC) is a style of computing in which situational and environmental information about people, places and things is used to anticipate immediate needs and proactively offer enriched, situation-aware and usable content, functions and experiences.”
The ability to build and leverage context-enriched systems, by intercepting, ingesting and processing data from cloud, socials, mobile or sensors will give organizations, both operational efficiency and market differentiation, helping them to augment intelligence and make better decisions.

In-Motion Analytics & Machine Learning: the ability to extract live insights from real-time data streams and perform analytics in real-time as the event happens. As everything changes, faster then we think, enterprises will leverage analytics in motion in order to behave robustly against unforeseen changes in the data distribution or to dynamically adapt to new patterns in the data.