Event Stream Processing

Distributed Data

Solving the Trillion-Dollar Data Problem in Edge Computing

As retail battles inflation, supply chain disruptions, shrinking consumer spend, and new shopping behaviors, legacy data systems are losing ground and leaking profits.

What if retailers shift from processing slow-big data to fast-live-data to make more informed decisions in real-time?

Now, they can. With the real-time edge that is faster and less expensive than the cloud.

In this eBook, you’ll discover how real-time data at the edge empowers retailers to:

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How Retail Can Take Advantage of Real-time Insights with the Edge

Enabling Real-time Inventory Management

What’s Next After Apache Spark and Apache Flink

How can you power applications at never-before-seen speeds if all the data requests need to go back and forth to the cloud?

The answer - turn to the edge to cache data closer to where it originates and where it’s needed. Geo-distributed cache can support use cases like real-time, dynamic advertising, social media app chat, and sentiment analysis to drive revenue opportunities, and any app with real-time user expectations.

At the edge, caching needs to operate at memory speed while providing multi-model operation, geographic flexibility and regulatory compliance, while reducing costs. What makes Macrometa different is that we also offer global synchronization - with super fast access to cache anywhere in the world.

et’s examine cache in detail - from the different types and why you need them and explore the benefits of synchronized, replicated edge caching.

The reasons why you should cache from low latency to reducing pressure on backhaul networks

Data that should be cached like static maps, metadata, and calculations that can be reused

Different cache types and their differences including local, remote, distributed, and geo-replicated caches

The requirements for building a real-time, geo-replicated cache

The components of the Macrometa Global Data Network including in-memory cache, multi-model data stores, streaming, and more

Accelerating Applications with Geo-Distributed & Replicated Cache

Edge computing treats data at the edge as one continuous, highly distributed source, enabling data to be treated in situ, and by leaving the data in place, all combined locations can be treated as one "cloud" occupying many points around the edge. The next step for data-driven enterprises is to utilize hyper distributed edge computing options rather than send data to the centralized cloud for analysis or storage.

The benefits of becoming a more data-driven enterprise include improved business processes, enhanced customer engagement, and improving employee productivity. However, challenges in integrating new infrastructure and processes with existing architectures often cause the need to reconcile data from disparate endpoints. 

Read the brief to learn more about these key business impacts: 

Data sources are multiplying from IoT and edge infrastructure

AI motivates more thorough usage of  data and closer proximity to the user allows results to be delivered in near real-time

Public cloud can create obstacles like data silos and migration challenges

Enterprises can employ the edge as a tool to convert data into business outcomes

[451 Research] Data-Driven at the Edge: Unlocking Business Insights

Library

An in-depth, technical content library featuring whitepapers, eBooks, webinars, and other online resources.

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Macrometa is the distributed backend for building global applications at the Edge. Build serverless, low-latency, stateful, geo-distributed apps & APIs in minutes.