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Shelf-Life Zero

The difference between yesterday’s data and today’s analytics? Everything.

In a world where streaming and real-time data analytics are becoming increasingly important, most firms worldwide are recognising the ‘shelf-life’ of the data in their possession: the quicker they can turn raw data into insights, the more value they can extract from it.

Ain’t no time like the Present

Real-time analytics has already been found to have myriad business prospects, such as in fraud detection or credit analyses by financial institutions. Through the monitoring of customers’ card usage, AI algorithms can automatically detect the presence of fraud and send out appropriate alerts in real-time. Erez Alsheich, co-founder and CPO of US-based big data ingestion and integration platform Equalum, notes: “organizations can offer immediate, detailed credit assessments, so they can reject or approve loans in real-time. This could revolutionize the consumer loan market.”

Several other kinds of global websites and applications may also monitor prospects’ locations and offer potential services and products for usage: such as displaying an ad for travel insurance when the client is at an airport, personalising each student’s individual progress in a hybrid AI-based education model etc. With an increasing number of firms such as Google, Facebook, Amazon and LinkedIn using integrated real-time analytics in their business models to leverage big data and maximise sales, the importance of the aforementioned cannot be stressed enough.

In fact, in a recent interview with technologist Bernard Marr, Alsheich even opined: “Analytics is about getting insights from your data – but the vast majority of companies are using batch technologies to load data into their analytic platforms. Every night, they pull a bunch of data all at once, then load it into the analytical environment. The next day, the rest of their team can do analytics on that data, but the problem is that the information is already outdated. It’s yesterday’s data.”

With the ever-increasing stress laid on firms’ legacy architectures owing to the massive volume and velocity of incoming data, the ability to process it both efficiently and effectively has been substantially reduced. While transformation and optimisation of these large swathes of data in near real-time is not without its own complexities, the true gateway to streamlining real-time analytics, experts agree, lies in ‘modern multi-modal change data capture.’

C-D-C for the W-I-N

Compared to traditional batch processes, CDC is a much more prudent option in terms of a low-overhead and low-latency method of extracting data: intrusions are limited only to the source, and data is continuously ingested and replicated through the tracking of changes. In databases and its most basic form, CDC is essentially a set of software design patterns that can be used to determine and track dynamic data changes, so that actions can be taken directly using the altered data.

When designed and implemented effectively, it is the most efficient method we have today in meeting requirements such as efficiency, scalability, promptness and low overhead requirements. Changes can be pushed directly into streaming analytics systems, thereby enabling businesses (i) to take faster and better decisions, (ii) design smarter products and services; (ii) improve recommendation services for consumers and (iv) improve, and even automate existing/legacy business processes. According to Marr:

“While streaming real-time data is a vital component of any modern architecture, there will most likely still be a place for batch data processing in the years to come. Monthly reports, data with minimal changes, historical assessments, and more can still be processed using a batch approach and may not need to be delivered in real-time.

Additionally, many organizations that have invested in a CDC streaming ingestion tool will need data replication abilities as well that their current technology cannot accommodate. On the flip side, those organizations that have invested in a CDC replication tool will often find deficits with real-time transformation, data manipulation, aggregations, and correlation capabilities within the ingestion pipe. This leads to multiple tools, high cost, architectural complexity, and a real barrier to achieving streaming analytics and a streamlined data architecture that can scale.

Finding a Data Integration solution that offers CDC Replication, Streaming ETL, and Batch in one single pane of glass platform is the ideal scenario as you look to incorporate streaming into your Data Architecture to drive streaming analytics.”

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