high concurrency. You obviously wouldn’t want to force fit Postgres (or even Elastic) in uncomfortable positions. But what about scale-out cloud data warehouses? Doesn’t elasticity = scale = high concurrency? Of course, but elasticity without insane compute efficiency is going to be an expensive app. Next comes the desire to unlock the value of streaming data with analytics. Businesses are adopting event-streaming platforms like Apache Kafka. Our friends at Confluent, the creators of Kafka, have built a data mesh that puts data in motion. With data swirling around constantly, what better use of it than to analyze it for continuous, real-time insights? Companies such as Netflix are doing this and their developers are creating a huge competitive advantage by bringing together Apache Kafka and Druid to build an analytics app that enables a high-quality, always-on, user experience. With an eye on real-time analytics, several things must be taken into account. Is analyzing streams enough or does the use case need to compare streams against historical data? For intercontinental exchange, it’s the full spectrum from present to past that gives them the right security visibility. Does ingestion scalability matter? Do you need to process millions of events per second? What about latency or data quality? Number four, more companies want to give their customers analytics. Analytics of the past were about making better decisions for the business. While still relevant and an opportunity to create more value, we are seeing companies build analytics apps to deliver insights to their customers. Companies including Twitter, Cisco ThousandEyes and Citrix are doing this and driving material revenue from it. They’re giving their customers visibility and insights, and that in turn creates big business for them. But it can be a pretty hairy outcome to use any database to build a customer-facing analytics app. There’s way more on the line than internal use cases when you think about SLAs and the customer experience. It’s in these apps where microseconds of latency make a difference. Downtime is costly, and concurrency and money go through the roof. And last but not least, the digitization of everything is built with analytics. At this point in tech, I think we all see that every company is becoming a software company. But with everyone having easy access to the cloud, simply building cloud software and services isn’t enough to sustain an advantage. That’s why companies such as Salesforce and Airbnb build analytics apps to optimize how they build their products. Developers at the best software companies are building analytics apps to help them create the best product experiences. Whether it’s nextgen observability, user behavior insights, live A/B testing or even recommendation engines, an analytics app is at work. There you have it, our prediction for this year. We see the world of analytics expanding rapidly to modern analytics apps with developers becoming the new analytics heroes in organizations. o David Wang is vice president, product marketing for Imply.io. 9 JANUARY - FEBRUARY 2022 | CHANNELV ISION Connect with Blackfoot Carrier Services! Click to schedule a meeting today! ILLINOIS WYOMING NEBRASKA ARIZONA CALIFORNIA TEXAS FLORIDA GEORGIA NEW YORK VIRGINIA OREGON IDAHO MONTANA WASHINGTON NORTH DAKOTA SOUTH DAKOTA UTAH COLORADO KANSAS MINNESOTA IOWA Todd Twete Director, Sales and Marketing GoBlackfoot.com/Todd
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