Fast and Furious – Shifting from The Slow Data Lane
It’s a sunny afternoon, you’re on the highway with the top down and stuck in traffic. You fumble through your navigation trying to figure out which is the best and fastest route to your destination. You can take the side streets, where you will encounter traffic lights and eventually get to your destination. Alternatively, you can switch to the carpool lane, provided you have met the requirements– if there are two or more passengers and if the carpool lane is operational, typically during peak driving hours– then you can drive the fast lane. Now you have a decision to make, do you rely on static data (what you can see in front of you) to continue your journey, take the side street for a slower journey or switch to the fast lane when it’s safe to do so.
Similarly, organizations are looking to manage growing, varied and fast data. The constant generation of data from cloud, IoT devices, social applications present an enormous opportunity for data-driven organizations to drive greater profitability, accelerate product and service innovation, and deliver exceptional customer experiences.
Traditional (batch) data processing frameworks such as MapReduce provided a “rear view mirror” into the company’s past, yielding three distinct historical data analytics: descriptive analysis, diagnostic analysis, predictive and prescriptive analysis. Analysts can query relevant historical data based on a specific period– hours, days, weeks, months or years to understand past patterns. But, data-at-rest loses value over time thus time-critical business decisions are often made using stale data.
As organizations transform to become data-driven, a modern workload approach of managing continuous data is required to perform actions on real-time data. To do so, organizations adhere to the requirements of fast data, which are velocity and variety to implement a real-time analytics solution.
Essential to real-time analytics are two components: first, stream processors continuously collect and parses data from event sources such as cloud, IoT devices, social applications as the event occurs and delivers to a streaming transport system. Secondly, streaming analytics solutions consumes data from streaming transport systems over a temporary time-based window that allow for data manipulation, enrichment, refinement and analysis, ultimately delivered for a variety of uses such as alerting, real-time visualization or persisting to Hadoop for historical analysis.
Riding the fast data lane may not be for all organizations, however as data generation increases via IoT devices and data requirements increase, understanding and implementing strategies on managing continuously generated data should be a key imperative for organizations undergoing digital transformation.
To shift to the fast lane, look into Informatica’s Streaming Solution, which allows organizations to prepare and process data in streams by collecting, transforming data from a variety for sources scaling for billions of events with a processing latency of less than a second. Informatica’s Intelligent Streaming leverages prebuilt transforms which run natively on Spark Streaming and leverages Apache Kafka as the data transport across mappings and data replay for recoverability.
In the next blog, we will discuss how you can design for the fast data lane starting with stream processors (or data collectors) and how Inforamtica’s Streaming Solutions can get you into the fast data lane.
This blog is part of the series on stream processing and analytics. Catch the series: