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 - the next generation Kafka Summit organized by Confluent happening in beautiful Austin, Texas October 4-5.

Why are we attending it and you should as well?

There is no better place to learn about data streaming than Current 2022, an event where real-time technology leaders and developers will share best practices, use cases, and explore the vision and future of the technology.

Nothing more exciting than being able to be a part of this! Here’s what we’re looking forward to:

Two days of power-packed keynote sessions highlighting real-time data technology and tons of opportunities to network and collaborate.

Educate what Macrometa’s GDN platform has to offer to build apps in real-time

Network with real-time streaming data technologists and researchers

Come say howdy at the Macrometa booth, see it in action, grab a t-shirt, and learn more about this real-time streaming data stuff the kids love.

 is a Platform as a Service for building and running applications at the edge. Run regionally, globally, or both - the GDN automatically synchronizes data between locations in milliseconds. Build greenfield applications or extend existing applications to improve performance and comply with data locality requirements. We provide data and compute infrastructure from more than 175 locations globally, reaching two billion users and ten billion devices in 50ms.

Create the next generation of high-performance applications

Build APIs on top of your data to create new revenue streams in the API economy

Macrometa At Current 2022 Kafka Summit Organized By Confluent

When you think about data and event-driven analytics, it’s natural to think of stream processing as the most effective way to analyze streams of data in real time. Many organizations have used legacy event stream processing tools like Spark and 

 for the past decade. But what if your organization handles very complex events (like an insurance company processing thousands of claims on a monthly basis)?

Would stream processing still be a good fit if you were trying to track insurance scams across many different geographies? Or would 

 provide more accurate, real-time insights? With Macrometa, you can support CEP use cases like supply chain management and event stream processing use cases like orders and payments. Either way, Macrometa provides a CEP engine “out of the box” that is easy to learn with an SQL-like language and there are many extensions for different sources and sinks (Kafka, MQTT, etc.) and functions (math, pii, statistics, etc).

In this blog post, we’ll take a look at Macrometa’s CEP engine, how to implement it, and its benefits. Read on to learn more!

CEP helps you to automate certain processes, and even predict future outcomes based on your historical and real-time data. Make the most of your data with CEP - like understanding customer behavior patterns - by analyzing the events that trigger it.

In a nutshell, CEP helps you to analyze streams of events from multiple data sources on a very low-latency level to uncover patterns and derive insights. This way, you can take action on the stream of events as soon as they happen. With Macrometa’s CEP engine, you can set up CEP faster than 

traditional event stream processing tools

 offers a simple way of performing CEP. You can easily integrate streaming data and take appropriate actions to analyze and correlate data in different ways to meet your business goals. 

Exploring Macrometa Streams and the CEP architecture

 is designed to work with real-time data. This includes the data flows from any IoT devices like cameras, weather stations, and other sensors. The most important and distinguishable features of Macrometa’s Global Network (GDN) streams are as follows:

 If you create a global stream the data published will be replicated in all the regions. Also, it's possible to simultaneously publish and consume data to/from all the regions. This allows you to deliver low latency across all your locations and anywhere your customers interact with your business. The GDN also provides different ways to publish and consume stream events - REST API interface, drivers for Python, NodeJs, Java, Stream Workers, and Pulsar native clients. 

 You can create and use a stream on a collection. This means all the data inserted, updated or deleted will be published to stream. One of the possible use cases is the change data capture (CDC).

3. Easy set-up from and to different data sources:

 The Stream Worker's data could come from different sources (GDN streams, GDN Database, HTTP endpoints, Kafka, MQTT and etc.) and can be published to different sinks. This is a feature available right “out of the box.” 

 also allows you to transform data from one data type to another, aggregate streams, clean and filter data, and more. 

 Stream Workers automatically create the needed objects like streams, collections, and even tables and remote databases. The CEP engine does this transparently for you behind the scenes. 

Now, let’s look at how you can implement CEP in Macrometa within minutes.

Setting up Macrometa CEP For Simple Producer-Consumer

The best way to get started is to use the samples located beneath Stream Workers listed within the GDN. Every sample has a good description of the use case it covers and how you can get it up and running.

To begin, you can log into the Macrometa platform and then find the Stream Workers menu item on the left. Stream Workers will only appear if it is already enabled within your account. You can enable it by contacting 

 sample below is a great one to get started with since it is the most simple stream worker. It gives you a basic understanding of the Stream Workers structure and language. This stream worker generates random values every second and publishes them as events on SampleStream.

 will start to create and publish events to 

The second stream worker here is a consumer of the events published by events-generator. It reads the events from 

, collects 10 sequential events in a sliding window and processes them by retrieving the biggest one.

Finally the result of the processed data is published to 

Any of the other Stream Workers cover different use cases for reading (source), processing, analyzing, and finally writing/storing/publishing (sink) the processed data. Go ahead and apply the ones that work best for your use cases from your GDN account or the 

Now that you know the steps to implement Macrometa’s CEP engine in your organization. With Stream Workers, you can process events in real-time, trigger automated actions, and more. Overall, CEP can be an extremely powerful tool for event-driven analytics.

To get started with Macrometa and see Stream Workers in action, 

And to know more about how complex use cases require CEP that goes a step beyond just event stream, join us in this webinar 

Complex Event Processing with Macrometa: What’s Next After Streaming Analytics with Apache Spark and Apache Flink

Tutorial: Implement Complex Event Processing In Macrometa Within Minutes

This blog post is written by DevCycle who partnered with Macrometa to launch world's first and fastest Feature-Flag-as-a-Service (all from the Edge). Let's hear from them on why they chose Macrometa and how they built an ultra low-latency global feature flags solution using Macrometa.

The idea of saving user data to the server is not a new concept. It’s a tried and true archetype that is used widely. Our old architecture is built on this premise; a user is introduced into the system through our SDKs, the server receives the request, and the server then saves that user to our database and returns the user their configuration with all of the experiments and feature flags associated with that user.

, we reimagined our systems to separate that logic by creating a service that buckets users into experiments/feature flags. This service would live on 

 hosted by Cloudflare, meaning any request sent to these workers will direct you to the closest node in the Cloudflare network with a lightning-fast response (think ~50ms). This is a step up from our old response times, which averaged around the 150ms mark.

The caveat for this fast response time was that we could not store user data in traditional databases as we valued the response time as paramount. Our old system had the user data fetched from the database to use in bucketing decisions, but it would add significant time to the request.

Imagine having a feature flag gating a feature on your landing page; a fast response time could mean the difference between your user seeing the new feature or seeing a blank page while the request waits to finish.

Instead of having the user data saved, we initially opted for an approach where the developer would pass in the user data in every request, eliminating the need to keep it as it would be available in the request itself. This was a satisfactory solution, but it made using the tool a little restricted. You’d have to pass all the user data in every request to get features instead of piecemeal when the user triggers changes to their attributes. Not having to give in all their details is the explicit goal, but how do we get there?

We explored several different options when considering a solution to storing user data. We had specific criteria when researching the market for solutions, but the most important to us was having low 

A few options we boiled it down to were:


Durable Objects seemed like a good option touting a low latency key-value storage solution that was strongly consistent. The tradeoff, however, was that the Object being updated lives in the data center where it was first created. After creation, the data currently cannot be moved to a new region. This is fine if the user creating the data remains in the exact geographical location and never moves. Still, it breaks down as soon as they attempt to access their data from a location other than where they initially created it. Over time, performance, on average, would decline as users move around the world.



 boasts low latency round trips and average response times of less than 50ms, with a tradeoff of replication lag for regions around the world, typically less than 300ms. We decided this was worth the tradeoff to maintain a faster average read performance. 

 was created for our SDKs to use when a developer has 

 enabled as a feature, taking in user data as the request body and simple authorization handling by reading a client key associated with a DevCycle project from the request header. Macrometa’s library was easy to implement into our Cloudflare Workers, and their 

 were concise and easy to follow.

We’ve also added an option to turn EdgeDB on in the SDKs so that it tells the API to use EdgeDB user data in the request to get user variables and combine any user data sent up in the request. You can find the public API in our 

 to read/write user data into a geographically- replicated data store and a publicly accessible endpoint to save user data!

Okay, but how is a low latency solution useful?

A feature is only outstanding as its use-case, so how would you use EdgeDB?

Let’s imagine you’re creating a product that is a storefront to products regularly sold at a brick-and-mortar store. Maybe it could be artisanal coffee drinks. Let’s call it “Planet Dollars”.

Now “Planet Dollars” will have an accompanying app that you can download on your phone and a website to order ahead and pick up your drink at a store.

You want to give members an incentive to keep spending money, giving them stars on every purchase. Once they accrue a certain number of stars, they’ll achieve a “Platinum” status on their account, unlocking new functionality in the app. The user orders on the website, and the request is sent to your backend:

Reinventing The Wheel: How DevCycle Made Saving User Data Fast With Macrometa

Digital experiences are the new normal, and understanding both customers and their intent is paramount for businesses to stay ahead of the competition. Consumers are increasingly transacting online for everyday experiences, from telehealth appointments to digital banking, online shopping, and even attending virtual events. Every online transaction can be thought of as an event, and organizations are capturing, comparing, and analyzing these events from a variety of sources to predict customer behavior and improve their experiences.

 isn’t trivial and there are a variety of solutions out there that claim to be the best, but how do you choose the right one for your use case? In this blog, we will introduce Macrometa, the new kid on the block in the world of CEP (you can even 

 and try it out - in minutes), and see how it stacks up against two mature offerings for streaming analytics, 

 and Apache Flink. We will provide a high-level overview of CEP in the next section, but check out the chapter 

 to learn more about the two stream analytics platforms. 

Understanding Complex Event Processing vs Event Stream Processing

Before we continue, let’s set the record straight on the differences between CEP and event 

 (ESP), also known as streaming analytics. In short, CEP can be considered as a more complex version of ESP and generally involves detecting patterns or correlations between multiple incoming streams of events that may or may not arrive in the order they were sent, to derive real-time insights.

An example of ESP would be comparing the number of items added to Amazon shopping carts with the number of items that were actually ordered, to ultimately improve the order completion percentage over time. With CEP, you may correlate the zip codes of customers with items in their shopping carts with national weather data to notify customers that deliveries may be delayed. Aggregating a variety of events, both real-time and historical, allows you to more accurately forecast delivery estimates, providing an improved customer experience.

 (GDN) offering a Global Data Mesh, Edge Compute and a CEP engine with native Data Protection. Within the Macrometa GDN is an incredibly powerful component called 

 which help enable CEP use cases. Macrometa’s CEP capabilities are similar to those of Spark and Flink, but we will go into more detail about how Macrometa can help simplify your architecture and expedite your deployments. Take a look at the chart below that provides a high-level overview of the three data processing engines.

Macrometa, Spark, and Flink feature comparison

Stream Workers are only one component of the Macrometa GDN and work seamlessly with the rest of the platform to expedite and simplify the creation of event-driven architectures. To put this into context, imagine how much time and expertise it would take to write stream processing jobs to aggregate a real-time stock quoting stream via a variety of intervals (by second, minute hour, day, etc.) in Spark or Flink. You’ll need to specify and integrate a backend datastore for persistence, then repeat the setup process within multiple regions as close to your end users as possible for low latency. This could take several months! With Macrometa, you could deploy a local or global Stream Worker to process and aggregate the data in as little as five minutes.

Check out the code snippet below to see how easy it is to aggregate, process, and store the data within a Macrometa database.

Today’s challenges increasingly require solutions that can process, analyze, and act on data in real-time. Detecting fraudulent credit card transactions for a global customer base is one such challenge that can be addressed with a 

 engine like Macrometa, Spark or Flink. With low-latency being critical for anomaly detection, a centralized architecture simply won’t work. Instead, you will need to have a processing engine deployed in a variety of regions as close to your customer base as possible.

If operational simplicity is important to you, then you’ll love Macrometa due to its 

. You can choose to deploy Stream Workers into one or more PoPs around the world at the click of a button, ensuring data will be processed as close to where it is created as possible, while at the same time abiding by any 

. On the topic of regulatory requirements, you can geo-fence your data to ensure it stays within a particular region as well as anonymize/tokenize it to ensure access is only granted to those for whom it is intended.

Accelerate Time to Market With Developer Velocity

When processing real-time streams of data, your system will generally contain three components; a source (or sources) where the data originates, a stream processor which will analyze the data (cleanse, transform, enrich, correlate, etc.), and finally a destination where the newly processed data is consumed. Spark and Flink serve as the stream processing engines, thus it will be up to your development teams to specify and integrate the tools for the source and destination.

Macrometa on the other hand is a complete development platform that can dramatically simplify your architecture, unleashing developer velocity to focus on building great products rather than integrating disparate systems. Stream Workers can be thought of as an application blueprint, specifying data ingestion, processing your data, and finally writing to a 

 in whatever data model makes sense for your use case. Oh, and if you need to 

 on your data, Macrometa can do that too.

Macrometa GDN, Spark, and Flink are all fantastic stream processing engines, and which tool is right for you depends on your goals and needs. Spark is more mature, while Flink is likely more suitable for CEP due to improved latency and performance. If a managed service is more your thing, 

 may be able to help reduce complexity of your Spark jobs. 

However, if you need to process globally distributed events with as little latency as possible, or want to reduce your time to market and unleash developer velocity, Macrometa is absolutely the way to go. 

Macrometa offers real-time, stateful CEP close to where the events are occurring - where your customers are creating data - to provide fast, relevant insights. 

Want to see for yourself? Be sure to watch our 

 to quickly begin setting up your first Stream Worker.

Choosing A Complex Event Processing Engine: Macrometa Vs Apache Spark And Apache Flink

The cloud has been changing the way we think about technology for years now and it’s no secret that cloud services are becoming increasingly popular with both users and businesses alike.

Cloud gaming is a growing segment of the video game industry, with some companies reporting more than double-digit growth year-over-year. In its recent 

 on cloud gaming trends, Intricately noted that cloud gaming as a service could reach an estimated value of $5.8 billion by 2022. This high valuation is indicative of the rapid adoption of cloud technology in general and cloud gaming in particular. 

As we enter 2022, the video game market will continue to expand, with emerging concepts such as microservices, 

, and blockchain continuing to drive innovation across hardware, software, and services.

But what does this mean for cloud gaming? How will these trends impact it going forward? Let’s take a look at Intricately’s report in more detail and see if we can predict where cloud gaming is headed in the future.

Cloud Gaming Market Predictions and Trends

The Cloud gaming industry to grow over 300% while adding new ways to engage with gaming

According to the report, in the next two years, the cloud 

As streaming services revolutionize, the way we watch television cloud gaming is changing, and how we play, well, I would say cloud gaming is offering us new ways to engage with our gaming addiction. So we don't think it's changing how we play, but it's adding new ways for us to actually go and play computer games.

Are streaming games going to be a reality?

According to the report, the future of gaming is in the cloud, making streaming games a reality, and you no longer need an expensive gaming console. All you need is a fast internet connection.

However, it is not that you no longer need an expensive gaming console. Cloud gaming does not offer the same experience as gaming on a high-end PC. Gamers typically have both PCS and consoles, and handheld devices, they don't pick between them. We've also seen that all you need is a fast, internet connection isn't strictly true, you can have a great ISP connection put to the internet for sure.

If the cloud is too far away as we often say or wherever the game cloud gaming is being hosted, a fast internet connection will not help you at all. You're still going to have problems and because it's video, it's even more sensitive. So most Consoles and PC gaming is running at anywhere from 1080p, which is a high-definition video, up to 4K which is, you know, obviously 4x of 1080 P. So 4x the resolution is an ultra-high-definition quality that requires a huge amount of bandwidth. But gamers will lower the resolution because they're looking to play a multiplayer game and the 

 between the player and the game is really important.

And cloud gaming is not targeted toward those types of experiences. So yeah, this is not about switching people from playing games on other mechanisms. This is about adding to the repertoire of how you can consume games and cloud gaming, which opens up the world to more casual gamers.

Buy-and-play vs. subscription model for games

It's a shift in the model from a buy-and-play or free-to-play model to more of a subscription model where you have access to a catalogue of games.

If you're on a commute and you just want to try out some different games and the connectivity is strong (but even regardless of the internet connection), cloud gaming generally is better if it's actually hosted at the 

 and on an operator network. So it's a great business for Consumer Edge application where you are taking advantage of the proximity of the edge but also the fact that you're on a managed Network. 

So all those challenges are delivering video over the open internet with a very short latency. The requirement is to be able to control a game that's being read in. The cloud is greatly improved by actually deploying it at the edge.

Concluding our thoughts on this report, we agree cloud gaming is expected to become one of the most important industries in the next decade. As augmented reality (AR) and virtual reality (VR) continue to grow in popularity, so will the need for cloud gaming services.

The 2022 cloud gaming report by Intricately predicts the increase in cloud gaming industry by 300% and we add to this that the cloud gaming industry will be more empowered if built at the edge to help reduce latency. Additionally, we also predict the shift from buy-and-play gaming model to a subscription model where gamers can choose what they want to play.

How Macrometa can help the gaming industry

The Future Of Cloud Gaming: Thoughts On Intricately’s 2022 Cloud Gaming Report

Data is like inventory - the longer it sits, the less valuable it can become. Backend systems don’t always sync in time to provide updated inventory changes. Data traveling to centralized clouds and data centers only get delayed further. Retailers are now tracking inventory across channels and touchpoints, including online, in-store, and in-app. The goal of this omnichannel retail experience, in theory, is to provide a convenient experience for customers everywhere in the retail journey and to increase sales opportunities for retailers. To create a frictionless omnichannel experience, retailers need a real-time view of all of that data.

Data at the edge sits right next to customers, stores, and distribution centers. Instead of customer data being sent to a centralized cloud, data can be processed wherever customers and retail staff are, anywhere in the world or online. When retailers adopt edge technologies, they gain the ability to see a real-time view of inventory and other data at any access point. Retailers can move inventory across channels to the stores, warehouses, and mini-fulfillment centers. Website and application performance improves for shoppers and staff with faster response times.

Real-time offers not only benefits for inventory tracking but across all levels of the business. Buyers can analyze sales patterns and trends to adjust purchasing decisions. Marketing can offer social and email promotions based on inventory counts. Real-time data allows websites to recommend personalized, curated deals and product recommendations with greater context and relevance.

Deliver a globally visible, locally curated inventory

Many retailers operate globally or across many regions. In 

, it’s not unusual to have over a million SKUs across the world. Each geography will have a select set of products from the global inventory. These product inventory subsets add operational complexity. Mismanaged inventory can result in unnecessary mark-downs or other losses.

 change feeds provide an exact count of assortments and locations. It also simplifies the complex logistics of just-in-time inventory and dynamic pricing for the most profitable use of space. Retailers can determine what merchandise to move and what products to order with 

Customers and staff can seamlessly access available inventory by region with geotagging for product subsets. Plus, 

 is local on the edge so it can meet any and all privacy requirements.

Expand fulfillment options online and in-store

Extending the buyer journey over apps, websites, and in-store is now the industry standard. When we discuss topics like the future of retail, we’re speaking plainly about the ability of retailers to streamline inventory logistics and adopt the real-time connections between shoppers and stores. Omnichannel is the new reality of retail, and the companies that do it well will be the ones that see the most success.

Keeping store and online inventory separate is no longer an option. Traditionally, retailers kept in-store inventory separate from direct-to-customer (D2C) warehouses for online orders. Now, the line between online and in-store is permanently blurred. Shoppers can browse in-store and buy online for at-home delivery, or they can buy online and pick up in-store (BOPIS). Customers can buy online and ship-to-store (BOSS). Some retailers can cap shipping costs with buy online, and return in-store (BORIS). Customers prefer the convenience and if they come to the store, they may buy other items. Retailers need to be prepared for all the ways that customers browse, purchase, receive, and return inventory.

In the past, most distribution was handled through a large, centralized distribution center. Shipping times from these distribution centers largely depended on the customer’s proximity to that particular warehouse, which resulted in inconsistent shopping experiences. Now, retailers are moving away from this centralized model (Amazon, for example) and creating hubs. Stores now act as their own mini-fulfillment centers for retailers to ship from stores (SFS). In the past, retailers would mark down slow-moving inventory and lose profit. Now a shopper in Seattle can place an order that is fulfilled from a store in Seattle. If that store doesn’t have the inventory, it will be fulfilled by the next closest location. All these changes happen in real-time, enabling faster inventory turns and better use of selling space.

Retailers offer expedited shipping to compete with e-tailers. Fulfillment networks near the customer can use standard shipping instead. This lowers shipping costs and has a positive environmental impact while delighting the customer with a fast turnaround.

Data is also following this model. When we talk about 

, it’s really just examining where we send and store and process data. Are you using a centralized fulfillment center (cloud computing) or are you using local stores and mini-fulfillment centers (edge computing)?

The Edge of Retail. Sign up for the webinar.

Keeping data close to the customers and staff delivers faster response times to improve customer journeys and increase sales.

Customers form an opinion about all company channels based on their website experience. Page load time determines organic search results and could impact future opportunities. Improved 

 can reduce cart abandonment and deliver the experience customers expect.

Immersive customer experiences need real-time interactions and millisecond automation to support analytics, 

, and omnichannel touches. When a customer comes into a store, they can use virtual mirrors to try on clothes or makeup. Sales associates can upsell or assist customers based on real-time inventory suggestions.

Customers can also receive instant promotions and be directed to self-serve technologies like smart vending machines for faster checkout. After the transaction, they can receive a coupon for future purchases, or be shown where they can find a matching accessory.

Time and space are “money” for retailers. Real-time data and analytics - powered by the edge - are powering the most important business decision. Join our 

 to learn how your business can boost profits and customer satisfaction with real-time inventory management.

Data is active everywhere retailers do business with the Macrometa 

. Retailers can build a real-time inventory view for in-store, in-warehouse, and fleet management. Data is replicated in milliseconds and reconciled with automatic conflict resolution. Local copies of inventory data are kept in any of the Macrometa GDN 175+ points of presence.

Macrometa lets you store, serve, and process your data within 10ms of 80% of the global population.

How Retailers Can Drive Sales And Better Customer Experiences With Real-Time Inventory

Shopping behaviors during the pandemic have accelerated the need for a unified customer experience across various channels. 

 expect to receive consistent service on every channel. In order to win business and customer loyalty, shoppers require a hyper-personalized 

The same survey indicates that 32% of customers expect personalized content and product recommendations based on their past interactions with the business. 

 gives retailers the power to monitor and respond to customer behavior in-store, on social media, or web browsing by implementing personalized ads and offers. But “real-time” requires processing data close to the customers, anywhere they happen to be. 

This level of coordination and distributed computing adds complexity to retail development teams that are already stretched thin. Businesses that are unable to capitalize on real-time insights are the ones that get left behind. Retailers need to leverage the power of edge computing.

 either have deployed workloads or plan to deploy workloads at the edge. The goal is to bring data, computing, and storage near to customers, stores, distribution centers, and staff. This move to the edge of the network supports real-time analysis and personalization efforts closer to where data is generated. 

But in-store edge servers require frequent maintenance and security upgrades, and the initial set-up can present logistics challenges. For a retailer with thousands (or even just hundreds) of locations across the country, it’s cost-prohibitive to deploy on-prem data processing and analytics 

. While cloud computing is an option, millions of terabytes of data will need to travel over the network, adding egress costs and delays.

 can handle applications and workloads at scale by computing data through network nodes and data stores that are close to where the data is generated, eliminating data transfer delays to the centralized cloud. Many organizations deploy a hybrid model of edge, cloud, and on-premises data centers to deliver low-latency and the fast response times that 

 (AI) applications and real-time supply chains require - across all data sets. 

With the acceleration of new technologies to draw customers to the store and online from AR/VR experiences to self-service technologies, the edge lets retailers develop and update new applications much faster. This allows retailers to offer new services to customers ahead of the competition while reducing resource costs. Additionally, 

shoppers and staff can experience faster response times

 on their website or mobile apps because of the close proximity of data on the edge.

Process data close to customers to deliver a unified experience

AI/ML is required to accurately predict demand and even in some cases to create personalized experiences with 

 and other technologies. Having data and advanced analytics at the edge lets retailers deliver mass personalization in real-time and compete on a level field with large e-tailers. Product leaderboards, real-time data analysis, and historical customer behavior data support 

 pricing capabilities to let retailers set the best price as real-world conditions change.

Incorporating legacy back-end systems can be a challenge so many retailers are taking advantage of headless e-commerce and using APIs to build a seamlessly interconnected ecosystem for 

. It is much easier to build APIs that allow retailers to connect to all of their systems without worrying about syncing issues or migration. With headless e-commerce, retailers can easily provide an identity-aware customer experience across any channel with access to commerce, rewards, and promotion functions.

Inventory is the backbone of any retail org. Sales, profits, and customer satisfaction are all directly linked to effective and efficient inventory management. Retailers require global visibility — as well as the ability to support different local and regional assortments. Inventory is always in motion: Deliveries, pickups, and returns, shoppers in-store, and third-party retailers. Delayed response times or inventory issues can lead to discrepancies, bad customer experiences, and cart abandonment. Fun fact: ecommerce stores 

Traditional data systems already struggle to provide a real-time view of inventory, making it near impossible for retailers to meet customers’ demand for fast responses and consistent experiences across every channel. As retailers support more interactive online and in-store experiences, data speed becomes even more critical with the gamification of apps, personalized paths, virtual mirrors, and smart shelves.

The real-time edge can support retail inventory management with low latency across multiple geographies at significantly less cost than the cloud - without the hassle of maintaining servers. The edge also improves omnichannel experiences with faster performance and time to market.

to learn more about real-time inventory management at the edge.

. Retailers can build a real-time inventory view for in-store, in-warehouse, and fleet management. Data is replicated in milliseconds and reconciled with automatic conflict resolution. Local copies of inventory data are kept in any of the Macrometa GDN 175+ points-of- presence.

Data is close to the point of consumption - around the world - near stores and customer locations. Retailers can meet 

The Future Of Retail Depends On Real-Time Inventory Tracking

We're thrilled to announce that Macrometa is SOC 2 Type 2 certified.

The SOC 2 auditing and certification process looked at Macrometa's Security and Availability to ensure that the Macrometa 

 (GDN) is operating in a secure, controlled, and audited environment. To achieve SOC 2 certification, we demonstrated to the third-party auditors that the Macrometa GDN has these two security common criteria in place:

Systems and data stored by the company are protected against unauthorized access and unauthorized disclosure.

Information and systems are available for operation and use.

Organizations trust us with their most important data, and that responsibility is one we take seriously. We recognize that trust is earned by allowing third parties to objectively test the controls and procedures we've put in place. The team at 

 conducted the audit of Macrometa’s ISMS and controls on the SSAE-18 Trust Service Controls of Security and Availability over the course of several months. The certification enables our customers to comply with Information Security programs but also requires vendors to demonstrate adherence to industry-standard security certifications.

, just 10 short months ago today in fact, I saw that our Global Data Network had the all the right ingredients for developers to create next-gen technology. In a fully-distributed world that processes information in milliseconds, we have to instill confidence in our data policies and security. This certification is a huge milestone for Macrometa and the culmination of months of hard work and dedication toward the security of our platform.

The executive report is available upon request under NDA by contacting 

Macrometa Is Now SOC 2 Type 2 Certified

Some situations call for more complex search patterns. In this blog, we will cover range queries, faceted search, and geospatial search. Thank you for joining us again for the last blog in our search patterns series. If you haven’t seen the 

 and exact value matching examples. And if you are interested in prefix matching, full-text token, or phrase and proximity search, you may want to view the 

. We have covered seven different search patterns in the series to help application developers realize the full benefits of Macrometa search.

If you already downloaded the dataset from the 

 blog, you can skip this step. The dataset can be downloaded from this 

. After downloading the JSON file, replace <DATA> in Listing 1 with the content of the JSON file in the following command. The dataset can be imported to your GDN federation issuing the CURL command on a terminal as shown in the Listing 1. Before executing the following CURL command you need to first create a 

 named Hotels in your GDN federation and then create a document collection called hotel_reviews within that fabric.

The range query can be executed considering a set of numeric items. For example, the following query finds all the reviews which had ratings 3, 4, or 5.

Listing 10: Range search query which involves comparing strings

The execution of the above query results in a list of items (2037 items in total)¹ as follows.

Faceted search allows for combining aggregation with search queries to retrieve how frequently values occur overall. We need to first define a view using the identity analyzer as follows below.

The following query can be used to look up how many times a review carries the title "Very good". Note that here the case of the term "Very good" is exactly matched. Hence, although the title “very good” in all lowercase appears five times across this hotel review data set, only three reviews are listed as the result for the below query.

Geospatial search is a less frequently found but very useful feature when it comes to implementing geography-related information processing in general data analytics applications. Traditionally a special class of information systems called Geographic Information Systems (GIS) have been used to deal with spatial data extensively. However, 

 such as Macrometa GDN have the capability of storing and searching on spatial data which has become very useful when developing applications where geospatial search is required. Macrometa GDN supports geospatial queries such as finding coordinates and shapes within a radius or an area.

In order to try out geospatial search capabilities of Macrometa GDN we have selected a dataset from the Seattle metropolis in the USA. The 

 corresponds to public schools in Seattle. This dataset was obtained from the 

Listing 17: Get all the schools which are located within 1km distance from the Saint George Church

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