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Games need the right platform and protocol for fast reaction times. The edge offers smoother gameplay and less lag. It can also simplify management and reduce costs compared to the cloud or servers.

Let’s run through the advantages of the edge and compare TCP and UDP protocols. Then we will go over the Macrometa for Gaming solution and how you can get started.

 or data centers for scale and management. Traffic delays with the cloud or faraway servers can degrade cross-play or cross-save gaming experiences. At the 

, you can store and process data close to your players - resulting in better response times. Common cloud providers can be expensive as your data volumes increase. Backend servers need time and resources for set-up, maintenance, and updates.

TCP connects players around the world. It is a reliable, connection-oriented protocol that provides error checking and flow control. Many online games use TCP for network communication and to keep data secure. Real-time strategy, multiplayer online role-playing, chess, and puzzle video games are often TCP-based. Some multiplayer first person shooter games and 

The main difference between TCP and UDP is that TCP is connection-oriented while UDP is not. TCP requires a three-way handshake to establish a connection before data can be sent, and UDP does not have this requirement.

UDP is popular due to its low overhead and its ability to send data quickly and efficiently. It is often used for first-person shooters, racing and sports titles, and massively multiplayer online role-playing games when faster response times are required. Not all games need the same response times and TCP is still a good option in many situations.

With over 175 points of presence (PoPs), Macrometa enables multi-region gaming efficiencies. Data is automatically replicated across the Global Data Network (GDN). API requests are routed to the nearest available location. Updates at any location propagate across the network and bring all nodes to parity within milliseconds. Macrometa’s geo-distributed, real-time data layer is available within 50ms of 80% of the global population. This allows you to 

You can maintain a persistent state using different types of data within the Macrometa 

 and event & log capture to streamline development. The GDN also provides an 

 layer that’s deeply integrated directly with the data layer itself - that brings compute to where the data is originating, or the data is being consumed.

By using a single platform with analytics, you can have a clear view of all gaming activities. Spot early trends and recognize patterns to create business opportunities or prevent threats. For example, you can detect lag switches, or prevent bad actors from stealing gamers' information. Use 

 to check game health and gamer activities. Create new revenue programs and personalize gamers’ experience with real-time leaderboards and incentives.

Currently, Macrometa supports TCP for dynamic data. If UDP support is needed, a microservice can be created that connects to the Macrometa GDN.

Check out the tutorials, QuickStarts, and 

 to quickly get started with Macrometa. I mean you can actually build a streaming app in 10 mins or less! Start by requesting 

How to Create Better Gaming Experiences on the Edge

A fascinating fact about the octopus - they create these elaborate, ocean floor dens with shells and shiny treasures they find along the way. All of these pieces – bottle caps, coconuts, shells, rocks, coral, pieces of shipwrecks – are collectively known as an Octopus’s Garden. I believe some young lads from Liverpool might have talked about it once.

The interesting part is that although these little garden cities sound like a magical Octlantis, Octopus are a hell of a lot more than interior decorators. They use the shells as tools for gathering food or as a door to thwart intruders, they use them as support structures to give their dens strength and rigidity, and they’re used as landmarks - all kinds of utilitarian brilliance out of different pieces.

In a previous post, we touched a little on the similarities between the 

 For one, there’s no single point of failure. The octopus in its profound design has multiple hearts, a brain in each arm that acts independent of the others. It’s a beautifully crafted, natural redundancy that we felt drawn to. Macrometa, after all, has a decentralized architecture that makes us, like this humble cephalopod, both highly fault tolerant and resilient. 

It also reminded me of the ecosystem we’re building with our partnership community. The way Macrometa plays well with other tools. From an architectural standpoint, I think about the Global Data Mesh and the ability to filter in data from all of these little pieces everywhere. Every data source, every event, every PoP, every 

 - are all important pieces of the octopus garden. This foundation provides tools for developers to remedy their previously unsolvable problems with low latency at the edge.

[This isn’t an endorsement for intelligent design, not in the slightest, but isn’t it amazing how these parallels are all designed so intelligently]

 is a full-stack edge platform for building and running applications at the Edge. The GDN “garden” consists of a Global Data Mesh, Edge Compute, and Data Protection.

Global Data Mesh: Real-time, geo-distributed storage layer

Historically, database management and storage has been the bane of IT. As the ocean of data ebbs and flows in all directions, developers and enterprises and everyone that relies on data for decisions need a simplified way to quickly address different types of data. The 

 has all the benefits of a NoSQL database with KV, doc, and graph stores – but the garden is even richer than that. 

Edge Compute: Run real-time apps close to your customers

Macrometa’s Edge Compute puts data into action and delivers real-time and event-driven apps and data globally in <10ms.* The shiniest tools in this garden are 

. Query Workers let developers create simple REST APIs on top of the data mesh. Stream Workers enables Complex Event Processing functions and Stream Processing workloads - in minutes instead of days or months.

Customers tell us that the combination of tables and streams integrated within Macrometa and the ability to use Stream Workers to process data live is “the Holy Grail” for app development. 

Organizations need to comply with regulatory and legal compliance standards. Macrometa’s Data Protection supports geo-pinning or geo-fencing to address data sovereignty use cases. Developers and operations can easily set up 

 for regional and global data, and adjust locations with just a toggle. 

 and offers user, token-based, and API keys 

. It encompasses privacy, security, governance – the healthy tenets of a “security garden” I suppose. Hmm, maybe we should’ve called it a security garden. 🤔

 that create the gardens are considered “ecosystem engineers.” They’re called this because they create and transform their environments, improving resource availability for everyone in the community. At Macrometa we team up with partners to give our developers flexibility and speed. The Global Data Mesh connects data so that it can be stored, accessed, and processed at lightning speed, regardless of where it lives. We chose a mascot that was already doing the things we aspired to do.

Our goal is to give developers the freedom to build and run applications anywhere. We can work within their existing platforms like 

, and even get their applications over that “last mile” with partners like 

 with just a simple service API. Once you’ve built an impossible app, do Feature Flags in real-time thanks to our recent 

 partnership launching the world’s first and fastest Feature-Flag-as-a-service (from the Edge). 

As a company, we’ve prided ourselves on working well with others, and in the process we’ve created many gardens. The point is that they make things stronger, they add resilience, and we continue to explore new ways of seamlessly integrating the “shells” and “shiny objects” that make up the development landscape.

Create your own octopus garden and build the next impossible app. Request a 

We look forward to making some exciting announcements in the next few weeks and would love for you to build with us.

A Data Mesh Is The New Octopus Garden

Originally published 11/1/2022 by Corey Quinn, Chief Cloud Economist at The Duckbill Group, as a Screaming in the Cloud podcast, episode 405, “

Computing on the Edge with Macrometa’s Chetan Venkatesh.

Chetan Venkatesh, Macrometa CEO and Co-founder chatted with Corey Quinn to define the edge and how the edge is “new kind of distributed cloud.” Developers can build apps in a distributed way with Macrometa’s Global Data Network that you “just can’t do in the cloud anymore.” In his chat with Corey, Chetan runs through the need for a consistent data view, the layers of the Macrometa Global Data Network, typical use cases, and how Macrometa is helping developers lower their carbon footprint with more efficient coding and tools.

The term “edge” has been applied across many aspects of IT, especially around 5G technologies. The recent hype is not surprising, when you consider that edge-related annual revenue has been predicted to grow to nearly $274B in 2025 by 

. During the podcast, Chetan clarifies the term edge with a specific definition and indicates there are three different types.

Chetan indicated the edge is “very different from the cloud” when you consider “...that the cloud is defined by centralization, i.e., you’ve got a giant hyperscale data center somewhere far, far away...where you run things at scale and somebody else manages them for you.”

In contrast, “the edge is actually defined by location” and here are the three types per Chetan. And these three types “are now becoming important as a place for you to build and run enterprise apps.”

The first is the Content Delivery Network (CDN) edge and Chetan describes how this is historically “where we’ve been trying to make things faster with the internet and make the internet scale.” Akamai created the 

 “that allowed the web to scale….that’s the first location that defines the edge…where a lot of the peering happens between different network providers and the on-ramp around the cloud happens.” Akamai and Macrometa are working closely together to 

 and Akamai made quite a few appearances during our recent 

“The second edge is the telecom edge. That’s actually right next to you…because every time you do something on your computer, it goes through that telecom layer. And now we have the ability to actually run web services, applications, data, directly from that telecom layer, “ Chetan relayed to Corey. Macrometa works closely with telecom providers like 

The third edge is something that is ready and available at your fingertips and you take it everywhere -your mobile device! Per Chetan, “it’s your internet gateway” and “where you have some compute power, but it’s very restricted and it only deals with things that are interesting or important to you as a person.”

Consistent view of data anywhere in the world

 was originally designed for economies of scale by having data in a centralized location and to make it easy to add or change technologies. However, the cloud does present some challenges that Chetan talks about like how do you “take data and chop it into 50 copies and keep it in 50 different places on Earth.. then keep all those copies in sync?.. So you start to deal with some very basic computer science problems like distributed state and how do you build applications that have a consistent view of that distributed state?..”

 for this issue has to cover three areas: “a way for programmers to do this in a way that doesn’t blow their heads with complexity, a way to do this cheaply and effectively enough where you can build real-world applications that serve billions of users concurrently at a cost point that actually is economical and makes sense, and third, a way to do this with adequate levels of performance where you don’t die waiting for the spinning wheel on your screen to go away.”

Introduction to the Macrometa Global Data Network

 is based on cutting edge computer science incorporating new ideas and research in the areas of messaging, event processing, data consistency, and replication. The Macrometa GDN has 

: a Global Data Mesh that is a real-time, geo-distributed storage layer, an Edge Compute layer to process data to run real-time apps close to your customers, and a Data Protection layer to help organizations comply with regulatory and legal standards.

 has all the benefits of a NoSQL database with KV, doc, and graph stores - plus Macrometa Streams with Pub/Sub and messaging queues. What separates Macrometa from other data storage solutions is that it brings compute closer to people and where data is generated, regardless of where that is. 

Organizations need a consistent view of data, even if multiple copies are sitting all around the world. Say you are a large retail distribution company and folks are ordering from your site, you need 

Chetan explains to Corey that programmers need to be able to operate, modify, and replicate that data with consistency guarantees and high levels of performance. Edge computing and the Macrometa platform “literally puts data and applications within 50 milliseconds of 90% of humans and devices on Earth.” This may seem like a new paradigm, but Chetan indicates it is more of a continuum. The Macrometa platform “feels very much like 

Data comes in different forms and developers don’t have the time to reformat data. Macrometa “provides continuity by looking like a key-value store like Redis” and has “a streaming graph engine built into it that kind of looks and behaves like a graph database, like Neo4j.” Macrometa can distribute and sync data across hundreds of locations but “it looks like a conventional NoSQL 

Plus, Macrometa provides an ACID-like consistency and can support changes (like read and update requests) when you’ve apps running in 100s of locations and each of those places is modifying the same record.

Chetan explains a typical example of organizations with multiple locations. “We have the ability to connect data sources across all kinds of boundaries; you’ve got some data in Germany and you’ve got some data in New York. How do you put these things together and get them streaming so that you can start to do interesting things with correlating this data?”

Another example is when organizations need to collaborate with supply chain or other partners in real-time to address a supply shortage or distribution issue. With the Global Data Mesh you can, “very quickly connect data wherever it might be in legacy systems, in flat files, in streaming databases, in data warehouses,” per Chetan. Macrometa has “500-plus types of connectors.”

But it is not only about getting the data 

 - it is making the data fungible with APIs. “Because the minute you put an API on it and it’s become fungible now that data has actually got a lot of value. And so the data mesh is a way to very quickly connect things up and put an API on it. And that API can now be consumed by front-ends, it can be consumed by other microservices, things like that,” Chetan describes the instant benefits of building APIs on top of the Global Data Mesh.

As Chetan relays to Corey, the GDN also “provides an [Edge] Compute layer that’s deeply integrated directly with the data layer itself. So think of it as Lambdas running stored procedures inside the database… We’ve built a very, very specialized compute engine that exposes containers in functions as stored procedures directly on the database.” You can easily build apps in Python, Go, or the 

 that lets developers create simple REST APIs on top of the data mesh and 

 enables Complex Event Processing (CEP) functions and Stream Processing workloads. 

Macrometa has a compute runtime “that is Docker compatible, so it runs containers,.. it’s Lambda-like,.. so it runs functions.” You can build stateful microservices that can interact with data locally and “then schedule them along with the data on our network of 175 regions.” Chetan goes on to provide some examples of building distributed apps on the GDN.

With a cloud provider, your microservices back-end for a banking, HR 

 app may run in a specific region or two like us-east-1 and Virginia. In the Macrometa GDN, it will potentially run in 15, 18, or even 25 cities - where your end-users are located. If you are ingesting data from an electricity grid in 15-20 cities for 

, you can do all of that locally with Macrometa.

“So that’s what the edge functions does, it flips the cloud model around because instead of sending data to where the compute is in the cloud, you’re actually bringing compute to where the data is originating, or the data is being consumed, such as through a mobile app,” Chetan explains the key benefits of Macrometa vs a typical cloud model.

 layer lets you have fine-grained control of your data. Chetan describes the region-specific benefits of this layer to Corey. With Macrometa, “you can build an app once, and we’ll provide all the necessary localization for region processing with the tokenization of data so you can exfiltrate data without violating potentially 

Macrometa helps organizations comply with different privacy and regulatory frameworks and helps keep data secure in each region with geo-pinning or geo-fencing. Developers and operations can easily set up 

 for regional and global data, and adjust locations with just a toggle. Macrometa is 

Of course, almost all customers have an infrastructure already in place and the question may be how to begin with Macrometa. With Macrometa, you can simply plug a set of serverless 

Chetan explained how customers can get started, depending on the application, “Some of your applications work great in the cloud. Maybe there are just parts of that app that should be on our edge. And that’s usually where most customers start; they take a single web service or two that’s not doing so great in the cloud because it’s too far away; it has data sensitivity, location sensitivity, time sensitivity, and so they use us as a way to just deal with that on the edge.

And there are other applications where it’s completely what I call edge native, i.e., no dependency on the cloud and runs completely distributed across our 

 and consumes primarily the edge’s infrastructure, and just maybe send some data back on the cloud for long-term storage or long-term analytics.”

Chetan provides further details and an example here, “The edge is a speciation….of the cloud, into a fast tier where interactions happen, i.e, the edge.” This may be credit card companies checking for fraud patterns, to shut down transactions before they lose money, or other time-sensitive transactions. Chetan describes the Macrometa GDN as a “system of interaction, not a system of record.” The three layers of the GDN - the Global Data Mesh, Edge Compute and Data Protection - “are the way that our 

 are shaped to help our enterprise customers solve these problems,” per Chetan.

One way to understand the full capabilities across Macrometa is to “imagine ten years from now what DynamoDB and global tables with a really fast Lambda and Kinesis with actual 

 built directly into Kinesis might be like. That’s Macrometa today, available in 175 cities,” Chetan elaborates on Macrometa functionality.

 (PoPs) around the world. With cloud computing, latency and cost are issues, as data has to travel to and from a centralized location and the cost of exporting data can be high. Macrometa, in contrast, is set up to reach 2B users and 10B devices in 50ms, at up to 70% less than typical cloud costs.*

Chetan describes the unique way the PoPs network was created,“What we have built is a network that is completely independent of the cloud providers. We’re built on top of five different service providers. Some of them are cloud providers, some of them are telecom providers, some of them are CDNs.”

“And so we’re building our Global Data Network on top of routes and capacity provided by transfer providers who have different economics than the cloud providers do. So our cost for egress falls somewhere between two and five cents, for example, depending on which edge locations, which countries, and things that you’re going to use over there. We've got a pretty generous egress fee where, you know, for certain thresholds, there’s no egress charge at all,” Chetan explains to Corey as he questions him about egress charges.

 based on the distribution, scale, and support you need for your organization.

Democratizing latency with the Macrometa GDN

“We stop this whole ping-pong effect of data going around and help create deterministic guarantees around 

, around location, around performance,” Chetan provides more detail about latency. ”We’re trying to democratize latency and these types of problems in a way that programmers shouldn’t have to worry about all this stuff. You write your code, you push publish, it runs on a network, and it all gets there with a guarantee that 95% of all your requests will happen within 50 milliseconds round-trip time, from any device, in these population centers around the world.”

An Edge as A Service Platform delivering value

The Macrometa platform reduces complexity and really accelerates developer velocity. Chetan walks Corey through different use cases. Macrometa can support the targeted and personalized approach required by today’s subscription economy with low latency, in-region processing, while helping you comply with data regulation laws. For example, one of our customers “is a SaaS company in marketing uses Macrometa to inject offers while people are on their website browsing. Literally, you hit their website, you do a few things, and then boom, there’s a customized offer for them,” as Chetan describes to Corey.

Chetan provides another use case example where low latency is required to deliver real-time personalization ads and offers, “in 

 you’re making your minimum payments on your credit card, but you have a good payment history and you’ve got a decent credit score, well, let’s give you an offer to give you a short-term loan. So those types of new applications, you know, are really at this intersection where you need low latency, you need in-region processing, and you also need to comply with data regulation.”

Lowering your carbon footprint by being conscious of code efficiency

Chetan talks about ways that Macrometa “cares a lot about helping make developers more aware of what kind of carbon footprint their code tangibly has on the environment.” Macrometa has “started a foundation that we call the Carbon Conscious Computing Consortium—the four C’s. We’re going to announce that publicly next year, we’re going to invite folks to come and join us and be a part of it.”

Macrometa is also “building a completely open-source, carbon-conscious computing platform that is built on real data that we’re collecting about, to start with, how Macrometa’s platform emits carbon in response to different types of things you build on it,” according to Chetan. “So for example, you wrote a query that hits our database and queries, maybe 20 billion objects inside of our database. It’ll tell you exactly how many micrograms or how many milligrams of carbon—it’s an estimate,” per Chetan.

The cost of carbon will vary in different places and with different technologies but the estimate will be a great starting point. Developers can decide when to throttle down and they can know how much micrograms of carbon a specific query will emit. Per Chetan, “there’s a cost to run code - whether it is a function, container, a query, every operation has a carbon cost. And we’re on a mission to measure that and provide accurate tooling directly in our platform… ” Stay tuned and be sure to follow us on 

 to get the most up-to-date information on our Carbon Conscious Computing Consortium. 

Are you ready to dive deeper about the edge and Macrometa? Your organization may be considering the edge because they are challenged by latency and higher costs in cloud applications and workloads. Check out this 

 written by Durga Gokina, CTO and Co-founder of Macrometa that discusses how to reduce write costs and the importance of data models and platforms. The paper explains how applying multiple data models on a single copy of data reduces storage and processing costs and how Macrometa's coordination-free architecture works for data-in-motion and data-at-rest use cases.

You can also get started with Macrometa in minutes with so many many tutorials, QuickStarts, and easy-to-follow example 

. I mean you can actually build a streaming app in 10 mins or less! Start by requesting a 

Computing On The Edge: Screaming In The Cloud Podcast Recap

We're excited to share with you that we have launched a public Slack for the Macrometa Community. You can sign up for the Slack 

The Macrometa Community Slack is not replacing our existing world-class technical support. Any urgent issues should still be directed to our support team, and we can help you out.

The Community Slack is for Macrometa users to engage with each other and with some of us at Macrometa. I’m hoping it will come to be a place where our community helps each other solve problems and share best practices.

Of course, any actual problems with the product are for Macrometa to fix, but sometimes you just “get stuck” trying to implement something and benefit from a fresh perspective. In the Macrometa community Slack, we hope you will find that fresh perspective. It’s also a place for the Macrometa community to just “hang out” and get to know each other. I hope to see you there!

See you all soon in the Macrometa Community Slack :)

Macrometa Launches A Public Slack For The Developer Community

Excerpts from this article were originally published in the Datanami article “

Has Macrometa Cracked the Code for Global, Real-Time Data

There is an old saying that time is money. Data has a 

time, location, concurrency, and actuation value

. However, trying to combine real-time and historical data often ends in complex or expensive solutions that may not be able to keep up with the required low 

 demands. In this blog, we will explore the recent 

 about how Macrometa’s architecture and features deliver real-time data value. The article highlights how Macrometa offers a Global Data Mesh, provides a unique use of conflict-free replicated data types (CRDT)s, and integrates the Global Data Mesh with Edge Compute and Data Protection in a “three-layer cake” within the Global Data Network (GDN).

Macrometa CEO Chetan Venkatesh kicks off talking to Datanami about a typical customer example, Cox Communications, and “how the amount of data they were sending in, the scale at which they had to build and integrate these systems made it too complex and slow” with a cloud provider. Once Cox Communications engaged Macrometa, they were able to “reduce costs by 90% and have a picture of reality within the hundreds of milliseconds of latency” vs minutes to hours delay. Let’s run through the Macrometa components that provide these real-time benefits.

A solution for big and fast data - Global Data Mesh

The issue today is that data has to make the roundtrip to a centralized data center - whether on premises or in the cloud - and this is far away from customers, systems, staff, partners, and suppliers. This delay can lower the value of data, if a customer goes to another website with a better response time, or if a logistics company doesn’t see a traffic accident notification. Chetan explains to Datanami how Macrometa addresses this problem with a 

, that is a distributed resource, not a centralized resource like typical cloud providers.

In Macrometa's data mesh model, data can be stored, accessed, and processed where it originates and/or where it is needed. The result is a flexible, ultra-low-latency data layer purpose-built for global, real-time, and event-driven use cases. Data is automatically replicated across the Macrometa GDN, and API requests are routed to the nearest available location. There are over 175 

 around the world. With Macrometa, developers have the tools to build exciting new applications and they can use the data model that makes the most sense for their application. 

“The storage engine is a very small but integral part of our stack,” Chetan said to Datanami. “The real value is in the approach we have for the way we ingest data into the log, the way we collapse that log into objects in real time, and most importantly, the way we can replicate that data across hundreds of locations with transactional and consistency guarantees. That’s always been the missing piece.”

In developing Macrometa future data volumes were a consideration - which could easily be ingesting trillions of events per second per the article. At its heart, Macrometa's architecture is a geo distributed event source with a materialized view engine and 

. Macrometa manages changes at the atomic field level and merges changes made across the network to maintain a single version of the truth via CRDTs, showcasing the power and versatility of CRDTs as a regular JSON database and API.

The article depicts how Macrometa transforms all data changes into a CRDT operation, which is then replicated to all the Macrometa locations using a vector clock. “With a vector clock and a causal tree of changes, Macrometa ‘s system essentially serializes and provides serialization ACID-like guarantees,” as Chetan explains in the Datanami article. “This ensures consistency without having to exchange a lot of messages with different apps.” Macrometa can store, process, and serve data within milliseconds to multiple locations around the world. In contrast, the internal messaging required within 

 can significantly lower performance if they expand past four or five locations around the world.

 ”three-layered cake”: a real-time, geo-distributed storage layer, a compute layer to process data to run real-time apps close to your customers, and a data governance layer to help organizations comply with regulatory and legal standards.

Base layer to store and ingest data - Global Data Mesh

As big and fast data ebbs and flows in all directions, developers and enterprises and everyone that relies on data for decisions need a simplified way to quickly address different types of data. The Global Data Mesh has all the benefits of a NoSQL database with KV, doc, and graph stores - plus Macrometa Streams with pub/sub and messaging queues.

Middle layer bringing it together - Edge Compute

“In our platform, streams and tables are the same things,” Chetan explained to Datanami. “It’s just that streams are real time user tables. And so you can interact with your data in both real time, in-motion fashion with streams, via pub/sub, or you can query it using request-response with SQL.”

What separates Macrometa from other data storage solutions is that it brings 

 closer to people and where data is generated, regardless of where that is. 

 lets developers create simple REST APIs on top of the data mesh. 

 enables Complex Event Processing (CEP) functions and Stream Processing workloads - in minutes instead of days or months. With Stream Workers, data could come from different sources (GDN streams, GDN Database, HTTP endpoints, Kafka, and MQTT etc.) and can be published to different sinks or stores. 

 also allows you to transform data from one data type to another, aggregate streams, and clean and filter data. This CEP engine automatically creates objects like streams, collections, and even tables and remote databases. 

Top layer keeping data separate and safe - Data Protection

 supports geo-pinning or geo-fencing to address data sovereignty use cases. Developers and operations can easily set up 

The icing on the cake - a complete Edge-as-a-Service platform

Chetan summarized for Datanami, “At the end of the day, the Macrometa platform essentially deliver a full database, a 

 system like Kafka, and a complex event processing system 

 along with compute engine so you can build a real-time data application, completely distributed using us” via the 175-site GDN. 

With many tutorials, QuickStarts, and easy-to-follow example 

, you can get started with Macrometa in minutes. I mean you can actually build a streaming app in 10 mins or less! Start by requesting a 

Has Macrometa Cracked The Code For Delivering Data Value?

Wireless communication is so fundamental to our existence nowadays because it's at the very core of our digital lifestyles. The mobile phone has powerfully seduced and reprogrammed us socially, environmentally, and culturally. We all agree that the last ten years have unleashed a revolution of innovations via the mobile phone as it converged many disparate devices into one (a phone, a watch, a calendar, a contact book, a camera, a web browser, email, chat and so on) and then with the advent of faster cellular connectivity and programmability of the phone via general purpose developer tools, all kinds of amazing digital tools and services. With a few taps on the screen, we can summon literally anything – from infinite varieties of streaming video and gaming entertainment to food from our favorite restaurants, transportation to take us anywhere we want, reserve rooms and even houses in exotic locations around the world, and more. We can very literally perform almost all of the essential tasks required of us at home and at work on these magical devices. And all of this is only possible because the mobile phone is programmable using general-purpose tools and well-understood programming languages and frameworks. Basically, almost anyone with an interest in programming can build an app for the mobile phone if they are so inclined.

Now while the mobile phone has an obvious value to us because it's right there with us (all the time) as our most essential personal tool - the thing that makes it do these magical things is actually the cellular network that connects our magical mobile phone to the even more magical cloud where the backends for these apps run. Now developers love building apps on the cloud and developers love building apps on the mobile phone yet no one ever thinks about the network that connects the two unless it stops working. So the question that begs to be asked is: why is the network so tactical and why don't developers care about it as they care about the cloud or the mobile phone? I’ll tell you why - it's because you can't program the network to save your life if you needed to. The telecom network is literally the last important black box left in the technology world, that is opaque, unprogrammable, unfriendly, and resistant to change. While this has historically been justified as necessary to make it a critical infrastructure that can reliably deliver voice services to millions if not billions of users across a continent reliably and cheaply - the truth is that it's just not an infrastructure that can be leveraged to create new meaningful value unlike the cloud and the mobile phone.

 - the first of its kind cellular cloud that is literally breaking all the arcane and bureaucratic rules of the stodgy and slow telecommunications world by building an entirely new architecture for cellular networks that is cloud native. 

, founder, investor, wireless technologist, radical provocateur, entrepreneur, and now Vice President of the marketplace and app design at DISH Wireless explains it as “At DISH Wireless we are not building a wireless network, we are building a wireless cloud. One of the first killer apps for this wireless cloud is a 5G network”.

Put in more direct terms, what Brian is saying is that 5G is an application of a distributed hyper-cloud. What he is implying is that 5G is not the enabler of distributed hyper cloud, but rather that 5G (and all the Gs that come after) is the consequence of building a wireless infrastructure on cloud-native principles of agility, scaling from zero to infinity, programmability and making every part of the wireless network stack fungible via composability powered by SDKs and APIs. And it’s not just 5G that is the killer app but the ability to put apps and web services right on the radio so that they can programmatically access that radio network that makes the hyper-distributed wireless cloud the ultimate platform for app developers. And that's where our collaboration with Brian and DISH Wireless begins.

The DISH Wireless and Macrometa Partnership

In the relatively near future (early 2023 in a few months), Macrometa and DISH Wireless will provide a joint integration that enables app developers to not just build apps and APIs on 

 but also compose a wireless cloud on top of the DISH Wireless U.S. network on the fly, to deliver those apps directly from thousands of cell sites around the country - literally a millisecond or two away from almost everyone in the USA. 

This looks like a table on our poly model database that ingests or stores some data, then developers build a few APIs using 

 to ingest, transform, analyze and actuate the data. Once this is done, the network menu can be fired up, and you can select the locations around the country that you want to deploy the app on, configure the wireless stack by comprising a private LTE network from best of breed parts (via API or via our visual console), You can also configure network characteristics such as slicing and QoS, choosing a MEC instance size and provisioning a fleet of them right on the Radio Access Network that bind to your newly created private network. After this, click deploy - and your app lights up across the United States milliseconds later powering radical new apps and digital services, because now the entire cloud is not behind the telecom network but IS THE TELECOM NETWORK. 

Now everything is programmable - the backend edge cloud, the network, and the mobile device - and that is how all the amazing things we believe are in our digital futures will be realized. We at Macrometa refer to this as a state of “wait-lessness” - or no waiting. Imagine when you walk up to your rideshare uber car and the app and the network authenticate you instantly, unlocking the door, or when you check into a hotel and go up to your hotel room, your preferences for climate, lighting ambiance and even your credentials to various video streaming services and your corporate network secure access via a zero trust connection are all automatically configured as your door unlocks. You rock up to the gym and the machine pulls up your workout and automatically sets the weights and records your reps and pushes into your health app. The possibilities are endless and tantalizing.

Watch this fireside chant with Chetan and Brian for 

Ultimately, all this may just lead to this crazy thing the world is excited about - the metaverse - an augmented reality overlaid on top of our physical world that makes our world just a little more friendly, easy and pleasant to live in.

The future of the cloud is an edge, and the edge is already here on Macrometa and DISH Wireless. Come build on the cutting edge with Macrometa and friends. Request a 

The Rise Of The Wireless Cloud: How DISH Wireless And Macrometa’s Collaboration Will Make The 5G Wireless Network And Edge Powerful

If you’ve ever been frustrated at the age-old problem of coding products with unwieldy legacy tools while facing the relentless pressure of not going over time and over budget, we've got something for you.

Tangle is an intuitive open source platform that lets developers build enterprise applications in hours - with reusable components, drag and drop tools, secure architecture, and code extensibility. More importantly, it also provides developers a path to enhance quality further by contributing high quality, bespoke components - if and when needed. So, developers have complete control along with the powerful tools they need to save up to 90% of development time.

At Macrometa, we love everything user-friendly. So, we talked to Tangle about their low code/no code stream building capabilities and how that could apply to Macrometa’s powerful data processing platform.

With our combined expertise, what could developers and other data-obsessed teams do? Real-time collaboration experiences, real time data and management interfaces, analytics - quickly, simply, and without having to reinvent the wheel.

While we’re unveiling some exciting concepts and ideas today, the real deal’s on the way - and it will only be available at Macrometa.

Wayne Byrne, CEO at Tangle sat down with Michael Ridley, Head of Product Marketing at Macrometa to discuss low code at the edge. Take a peek into the future of edge development.

Curious what the combination of Macrometa GDN and Tangle’s low code technologies might look like? Watch the Macrometa+Tangle concept video to see new possibilities open up.

Stay tuned as we explore all the possibilities of the edge in partnership with Tangle.

Is Low Code The Future of Enterprise Edge?

, and today we're focusing on customer stories, more specifically, what people are building today on the GDN to solve real business problems that need a real-time edge to solve them.

The era of big data is now behind us, and the era of fast, global, and real-time data has begun. Coupled with data now becoming a phenomenon that polarizes with its aspects of privacy and exploitation, data is also becoming an area that nations and states are seeking to regulate, to protect themselves from big tech.

There are 3 business drivers behind the enterprise going beyond looking for tooling on historical data and looking at real time data tooling. In this post, we discuss how Macrometa’s customers are taking advantage of the 

Enterprises have realized that the hundreds of dashboards and thousands of reports that their tools produce every day are ineffective and will never enable better decisions, revenue or improve their cost efficiencies. For enterprises to be able to take data to the bank, they need data and their tools to enable better decisions by dealing with “what’s happening in the now” and not by analyzing what happened last month, quarter, season and year (except for compliance reasons).

But there are structural barriers to this adoption of real time tools. The tools that an enterprise puts to work on data today – like data warehouses, data lakes, and databases, are backward looking data infrastructure and are great at telling you what happened at noon last week Tuesday but have no ability to tell you what’s happening now. 

 can’t tell you what to show a visitor when they hit a website and is going to bounce 1.8 seconds later because they didn’t see anything interesting. It's also impossible for 

 to analyze the energy data coming from PG&E grid and correlate it with electric car data in order to help Tesla or Rivian figure out when it would be the least risky time to charge their vehicles during a heat wave in California without bringing the entire power grid down by oversubscribing.

Data monetization requires a tool chain that is “purpose built” for ingesting vast quantities of data in the “now” and making business decisions (analytics and AI) and actions (actuations that trigger automated workflows).

Resulticks - a SaaS disruptor in real-time Customer Data & omni-channel marketing

 (short for - Real Time Results from Clicks) is doing exactly this by building a massively concurrent omni-channel customer behavior analytics and attribution technology layer on Macrometa. Resulticks is one of the fastest growing SAAS companies in the Omni Channel Marketing & Customer Data Platform category. They came to Macrometa a year back, building a small real time web service for real time 

 (CEP) for a verticalized marketing web service for financial service customers that would make real time offers. Having had enormous success with Macrometa, they are now looking to build substantially new real time capabilities into their Customer Data Platform and data stack on Macrometa to gain a differentiation with real time as the lead value proposition. But more importantly, forward thinking SAAS disrupters like Resulticks are recognizing that data monetization needs new approaches to connect the dots of data into monetizable insight in 100 milliseconds or less and Macrometa’s real time CEP framework is probably the only such platform in the market to date. 

The team at Resulticks also has the hefty task of complying with a broad range of data handling and privacy regulations across different industries and geographies. Resulticks found that the Aerospike solution they deployed didn’t provide the speed and performance demanded by the marketers that use their real-time conversation platform worldwide. That’s when they turned to Macrometa.

“Our customer’s profitability and growth depend on our platform’s ability to go from data ingestion to action in milliseconds,” says Resulticks technical leadership and architect MS Kumar. “This requires a data platform that’s not just ultra fast but also versatile enough to handle complex data sets that combine streaming and structured data, enable real-time and batch queues, indexing, and data pipeline support as well as messaging in one integrated architecture.”

Business Driver #2 - Operational Analytics

Data lives fossilized as transactional data of record across hundreds if not thousands of silos in an enterprise. But data changes all the time in the real world. A website visitor bounces or becomes a prospect, a prospect becomes a customer with a click of a button that adds a payment to a subscription. And inside these silos, only the last action of a transaction and the state change between payment and order is captured, and all the metadata of what led to the transaction is lost forever. Transaction data fossilizes, frozen permanently in the amber resin of static transactional metadata and losing all relevance to the present and future because of the loss of operational metadata.

For real time operational analytics to be a reality, developers need tools and stacks for connecting operational data and metadata across boundaries (and silos). A technology platform that provides a streaming causal graph for tracking data and metadata changes, with a built-in complex event processor and real time pipeline that recalculates second and tertiary implications of this changing state as it happens. And finally, the technology platform must be able to put APIs on dynamic data to propagate it globally, across all the boundaries we discussed above. It should be able to make data flow between disparate silos and systems, across data centers and clouds, and across supply chains of information between teams, departments, divisions and partners.

AppSecEngineer - Delivering AppSec threat simulation and remediation training in real time

 provides labs, cyber-ranges and lessons on security focused areas like Application Security, Cloud Security, Kubernetes Security and more. Thousands of application security professionals use the AppSecEngineer platform everyday to train on threat scenarios in the constant battle to beat bad actors who may take advantage of bugs, exploits and vulnerabilities in these complex cloud environments.

To be able to provide a real time and highly realistic threat simulation and remediation lab, AppSecEngineer needs to ingest all kinds of data and enrich it for possible security threat events. Their platform captures high velocity logs and events, and transforms them into operational data that can be queried and investigated in milliseconds from the time the event is generated to the time it needs to be acted on. AppSecEngineer initially used a full AWS stack composed of 

 for log streaming, processing alerts and so on.

However, they quickly realized that this was neither developer-friendly nor scalable. 

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