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Financial institutions are constantly at risk of losing money due to fraudulent activities. The cost of online payment fraud alone is estimated to 

 between 2023 and 2027, making it a significant issue that requires the attention of financial institutions.

Fraudulent activities can range from stolen credit cards to more sophisticated cyber attacks aimed at exploiting vulnerabilities in financial systems. Financial institutions face numerous challenges in their efforts to detect and prevent fraud, ranging from identifying false positives to dealing with high data volumes and ever-changing fraud complexity.

Real-time analytics to thwart fraud in real time

 use a combination of technologies and processes to detect fraud in real-time, ranging from behavioral analytics and rule-based systems to machine learning models. 

 enable businesses to detect complex event patterns and isolate threats, resulting in security alerts that allow them to respond quickly to any potential fraud. By using real-time analytics, financial institutions can also improve accuracy with advanced algorithms and machine learning techniques to detect fraud, reducing the risk of false positives.

One of the advantages of using Macrometa for fraud detection is the ability to process data at the edge. By bringing data processing closer to where the data is generated, financial institutions can 

For example, gas station credit card skimming is a growing problem in the financial industry where thieves install skimming devices on gas pumps to steal credit card information from unsuspecting customers. With edge analytics, financial institutions can quickly detect and respond to gas station 

 in real-time by monitoring the transaction data at the edge of the network where it's generated. 

By processing data at the edge, it reduces 

 and bandwidth usage, enabling faster response times and reducing the risk of false positives. This edge advantage can be particularly useful for financial institutions that operate in areas with limited connectivity or high network latency, where traditional cloud-based solutions may not be practical or effective.

Macrometa Real-time Per-event Fraud Detection

Macrometa's hyper distributed cloud provides 

 that helps organizations stay ahead of threat actors. With Macrometa, financial institutions can detect complex event patterns and isolate threats, resulting in security alerts that enable them to quickly respond to potential fraud. With data manipulation functions, Macrometa allows businesses to identify early trends and respond to opportunities and threats before they become a bigger issue. Macrometa makes it easy to collect data from multiple sources and turn it into rich, contextual insights in real-time.

 and filter it in real-time, businesses can extract signal from noise and mitigate potential problems with immediate notifications when system behavior changes or anomalies occur. Macrometa's advanced algorithms and machine learning techniques help financial institutions detect fraud more accurately, reducing the risk of false positives. Additionally, Macrometa's knowledge 

, scalability, and cost reduction benefits help financial institutions stay ahead of the curve when it comes to fraud prevention. To learn more about Macrometa's fraud solutions, 

 with our solutions architect, we look forward to chatting with you.


Minimize Financial Services Fraud In Real Time With Macrometa

 continue to grow in popularity, the pressure to increase ad revenue for these services is also increasing. In fact, over

comes from ads. For new challengers and disruptors in the space, it's especially important to embrace a multi-channel monetization model that includes subscriptions and ads. But how do you optimize online advertising delivery and provide dynamic, personalized experiences that can compete in a saturated OTT service environment?

The answer lies in implementing sophisticated approaches like ad cohort mapping, personalized entitlements, and hyper-scale serving. These approaches are crucial to deliver tailored content experiences and increase 

Hyper-targeted ad cohorts and dynamic ad matching

One important approach is hyper-targeted 

 and dynamic ad matching. Ad cohort mapping is the practice of segmenting viewers into groups, or cohorts, that have shared characteristics such as demographics, psychographic data, financial value, and location. The more granular the cohorts, the more opportunities for advertisers to reach and engage interested prospects, which leads to greater monetization for the content in terms of generating ad revenue. Cohort mapping can become more challenging when done at scale for large events or new popular content releases.

Managing the complexity of personalized entitlements

Personalized entitlements are a key part of ensuring that viewers receive a seamless and customized experience when using OTT video streaming services. Entitlements refer to the specific content and features that a user has access to based on their subscription level, geographic location, language preferences, and other factors.

For example, a viewer who subscribes to a particular service may have access to a different set of content and features than someone who subscribes to a different service or has a different subscription level. In order to ensure that each viewer sees the appropriate content and features, streaming services need to be able to keep track of these entitlements in real-time and serve content based on the viewer's entitlements.

Hyper-scale serving is a critical capability for OTT video streaming services. When millions of viewers access content at the same time - like for a popular show premiere - services need to be able to deliver content quickly and efficiently across different locations. Hyper-scale serving enables services to deliver content and ads to viewers from the closest available server, reducing latency and improving the overall viewing experience.

Everything all at once …. plus data enrichment

Streaming services are challenged to keep track of these entitlements and serve viewers according to their subscription model, language, etc. Then there is 

 that has to happen in real time to incorporate new signals, features, and metadata about customers, enabling streaming services to enrich a cohort's data. Some of these data points can include new product categories, brands of interest based on a new location, or previous content they consumed.

In today's OTT video streaming services competitive ad environment, it's not enough to be proactive; services need to deliver the most relevant ads at the right time. With current economic stressors, ad sponsors will demand more hyper-targeted and dynamic ad matching to increase their ROI.

Macrometa's Dynamic Ad Cohort solution enables OTT video streaming services to map viewers to ad content cohorts in real-time and change cohort assignment throughout the viewer's session. Serve ads in milliseconds to more than 2 billion humans and 100 billion devices anywhere on Earth with the 

. With Macrometa, streaming services can gain an edge over their competition and immediately apply real-time ad cohort matching, entitlement checks, and dynamic enrichment to serve the most relevant ads while they have their viewers' attention.

Download the white paper to find out more about how to 

Serve Ads to Millions of Viewers in Real-Time

. Learn more about how to increase ad performance by

Next Generation OTT Video Services: Ad Cohort Mapping And More

As the demand for over-the-top (OTT) video streaming services continues to rise, service providers are focusing on delivering personalized content, search functionalities, and recommendations to keep users engaged and satisfied. By leveraging sophisticated algorithms and data analysis techniques, OTT video streaming services can deliver highly personalized content recommendations and effective search results to each individual viewer, which can ultimately lead to increased engagement, retention, and revenue.


Increase streaming minutes with content personalization

Personalized content recommendations are one of the key features that enhance the 

 experience. Netflix is a prime example of a company that uses machine learning models to analyze the viewing history of users, the time of day, the device being used, and the user's location to predict what the user is likely to watch next and suggest relevant content. Netflix has reported that 

 is based on algorithmic recommendations, demonstrating the importance of content personalization to viewers.

 and leverages machine learning to analyze user data and personalize content recommendations that aligns with the viewer's interests. Search and recommendations play a critical role in enhancing the OTT video viewing experience. 


Keep your subscribers engaged with more relevant results

 functionalities are also crucial for OTT video services to enable users to easily find content that matches their interests. Users can search for specific titles, actors, genres, and other criteria to find content that aligns with their preferences. Sometimes search responses are not fast enough to keep viewers interested - especially when they can’t find new or relevant content. The search functionality can also be enhanced with machine learning algorithms that analyze user queries and provide more accurate results.

With its distributed architecture, Macrometa provides faster access to data by bringing compute resources closer to end-users, reducing latency and improving response times. Unlike a centralized cloud, which can become a bottleneck when handling large amounts of data traffic, Macrometa's hyper distributed cloud platform allows for easy scaling and high availability of data across multiple regions. This ensures that users can access data and content faster, even during peak usage times.

, which enables users to quickly and efficiently query large datasets using a range of search parameters. This makes it an ideal solution for OTT video service providers looking to manage and analyze large amounts of data in real-time.

In addition to its advanced search functionality, Macrometa's hyper distributed cloud platform can provide recommendation capabilities that leverage machine learning and data analysis techniques to provide highly personalized content recommendations to each individual viewer. By analyzing user viewing habits, preferences, and other data points, Macrometa can enable OTT services to suggest content that is relevant and engaging, keeping viewers satisfied and more likely to continue using their service.

In today's fast-paced world, viewers have limited attention spans and won't wait for slow search or recommendation results. To keep them engaged and prevent them from leaving, OTT services need to use advanced search and real-time analytics and algorithms to provide them with accurate content recommendations. Contact a 

 to learn how Macrometa can help to speed up recommendations and searches and reduce administration time and costs.

Increase Viewership With Better Search And Content Results

In this blog post, I will outline how Postman helps us build and maintain our tools. We will also discuss how Macrometa uses it to create a better internal and external developer experience. Our platform has various tools like SDKs and CLI that enable developers to utilize the power of a hyper distributed cloud architecture.


Postman is primarily used as an API testing tool, it takes the burden off the developer to create client HTTP requests. That is just one aspect, Postman can also be used for documentation, automated tests, and even aids in the API design phase. We will cover additional options where Postman really shines with its intuitive GUI and sharing capabilities.


Macrometa takes an API-first approach when we roll out new features, so we follow these three steps. The first step is to update our Open API specification, the second step is to update our SDKs, and the third step is to update our documentation. Our goal is to create a seamless and consistent experience for every interaction developers have with the Global Data Network, including related materials.


When we update our Open API specification and deploy a new version of the platform in a test environment the next step would be to support new features in our SDKs. At this point we don’t jump straight to the code, we take a little bit of time to create Postman collections for all new endpoints. After, we do the first initial tests with Postman manually. The goal of that step is to see how the endpoints are designed, what parameters they are using, how the response body works, and how to build a path with parameters. Any response to requests that we get from the API we record in our Postman collection.

Since we have multiple test environments with different features, the first thing that makes our life easier is using environmental variables. Instead of having collections with hard-coded URLs, we can just turn them into variables. Let’s walk through an example.

https://play.macrometa.io/_fabric/_system/_api/function/metadata

Here is an easy way to change test environment:

We only need to create an environment in Postman for this to work.

That leads us to other important environment variables like user access.


Most of the APIs need some kind of authentication, it would be tedious work to add API keys or tokens to each request manually. Postman can save credentials in environments, on the collection level, or a combination of both.

Now that the basics are set up we can start to add requests. Recently we added Edge Functions to our platform, so we updated our collections accordingly.

Let’s take a look at one of the endpoints created. Deploying the Query Worker to the Edge Worker endpoint will deploy the Macrometa Query Worker to Akamai Edge. Endpoint has four query parameters, some of them are mandatory and some of them are set to default. The benefit is that we can turn on and off parameters with one click and see the API response.

How would you go about it in code? We would delete method parameters, add new ones, and run the whole code. It depends on the level of abstraction in the SDK but there is always a doubt if my code is working fine or if the request I am sending is wrong. That can be time-consuming if the structure or code has to be redone. If the Postman collection request is working at least I know the request structure is ok and it is time to check the code. I would argue that it is better to prepare everything before we start to code.


The same idea can be applied to the body. We can change it on the fly and quickly test responses.

In addition to that some endpoints have additional credentials, we can set them up as variables so that we don’t accidentally leak confidential information while sharing collections. You can find more on how to be on the safe side in this article 

Using variables | Postman Learning Center

Now that collections are set up and working for us, we can run them as many times as we want. Postman offers us the option to save every response as an example.

That is beneficial for many reasons. For example, now I can just copy the response and create methods in our tools (SDKs and CLI) that can parse the response object. No need to run a debugger in the IDE and watch the response variable, and no need for 

We know exactly what we need to get. Postman can save multiple examples, a good practice would be to save unsuccessful requests as well. That way, we can show the developers how to handle error requests.

Now it is just a matter of creating an abstraction of endpoints in the SDK. That should be easy work since we know exactly what and how to code.

You can debate after reading this, that this is just a double work, but that is only if you do not consider the added benefit of sharing. It is possible to share collections with your teammates but we went one step further, we are sharing our collections with all the developers using Macrometa.

For developers, it is perhaps easy to read blocks of code used for client requests, but team members are not only developers. Technical writers would benefit directly from Postman collections since they can interact with the platform, check the saved responses, and be confident that they write consistent documentation. The QA team can use Postman collections to build automated tests. When maintained correctly it can be a single source of truth for the whole organization.

We have been using Postman to create the first draft of collections for new features. The GUI that Postman provides helps us to see inconsistencies between various endpoints that support the same feature.

Get started today with Macrometa and Postman

To sum it all up I want to highlight what we are getting with just a little bit of extra effort before we jump into our preferred IDE. Here are five great reasons to implement Postman in your workflow:

While building Postman collections, you can learn how the API works

You can save request and response parameters and be sure of what we need to implement in SDK code

You can share collections with other team members and users of the platform

Your team members that have less technical backgrounds can easily work with the API

Since Macrometa has recorded various responses, it is a good starting point for documentation (ex. document all error requests)

, set up credentials in Postman, and test the platform quickly and easily and check out our 

. Let us know how you are using Postman on our 

Streamline Macrometa API design and testing with Postman 

Developers can use the Redis-like interface with the Macrometa platform

Redis is one of the most popular open-source in-memory data stores today, named the “Most Loved” database by Stack Overflow for five consecutive years. This year it lost that position to PostgreSQL.

Because of its fast performance, Redis is a popular choice for:

Developers can use the Redis commands with Macrometa and still automatically synchronize data between various locations worldwide in milliseconds.

It can interact with the Macrometa platform in various ways, using a console, an API call, or an SDK. Redis supports both JavaScript and Python SDK.

In the example below, we are using Python SDK to SET string key-value pairs:

In our application we need to retrieve data, we can do that with GET Redis command as shown below:

In this case data is stored on the platform collection in this format:

Keep in mind that expireDate is an optional parameter.

The Redis collection that we created is automatically synchronized across various locations around the world, up to over 175 locations. This leads to low latency times. Developers don’t need to worry about infrastructure and they can use well-known Redis commands they are already familiar with so there is no learning curve.

, and documentation to quickly get started with Macrometa. Sign up for a 

Macrometa Tutorial: Redis As An Interface 

Enterprises are challenged to deliver fast response times when data requests travel to and from the faraway cloud. Bringing your data to the edge and caching data closer to where it originates and where it is needed can provide lower latency and reduce costs. This blog will outline key sections from our 

, including why and what you should cache, the key components of a real-time, geo-distributed, and replicated cache, and how the Macrometa Global Data Network (GDN) supports caching.

With edge computing, information can be cached at 

Caching can reduce your database load and decrease overprovisioning instances to drive costs down significantly, as many databases charge for throughput. Processing and computing can be done closer to the originating device by caching data at the PoPs, with a lot less backhaul network to the cloud.

The question should be: what does the PoP need to avoid sending a request to a central hub before replying to a user request? A few common choices are specific rows of recent data from 

, API results to access globally shared data, and configuration files for user applications. If we look at food delivery as an example, you may want to cache static map information, route calculations, and any metadata about the deliveries that are frequently accessed.

Key components of a real-time, geo-distributed & replicated cache

It makes sense that a real-time, geo-distributed, and replicated 

 has to be fast and have a wide geographic reach, but let’s go over all the key attributes in detail. 

 that can handle data in any format from traditional databases and data streams for unstructured data in a unified manner.

 for real-time information that needs to be transmitted almost immediately - an edge cache needs to allow a PoP to enable quick responses for time-critical tasks.

 - since the stateless and distributed concepts of 

 complement the edge's enhanced reachability and networking capabilities.

that can handle a geographical subset of clients anywhere in the world.

 and limit where data travels, thus conforming to regulations like GDPR.

 was designed to support a real-time, geo-distributed, and replicated edge cache. The image below shows how two PoPs communicate through streams handled by Conflict-Free Replicated Data Types (CRDT) resolution. At the top, various queries enter in different formats and protocols. The in-memory cache in each PoP responds quickly to client requests. Let’s walk through other features that help enable a real-time, geo-distributed and replicated cache.

 and enables users to access the same copy of data via multiple interfaces. You simply use different databases at the same endpoint - so it is much easier to integrate different data streams into an application.

In-memory side cache with disk-based persistence

Applications can retrieve data immediately without needing even to access a disk, much less send a request over the network. The data is written to disk so that it will survive in the case of a hardware failure, and the cache can be restored to a new or restarted node. 

Hyper-distribution across several regions

Macrometa maintains more than 175 PoPs scattered through many different regions. Macrometa also 

 with telecom companies to use its facilities to provide geographic flexibility to its clients. Two key features in the GDN that enable rich hyper-distribution of cache across several regions are:

CRDT model: The Macrometa GDN uses a CRDT update model. Each PoP logs all the changes it makes to its cache and sends these changes out to every other PoP at regular intervals. These data transfers are called streams, and instead of hub-and-spokes, CRDT creates peers. PoPs send out these compressed and encrypted streams frequently, and each stream contains just data for a few seconds, so it is not a network burden.

Coordination-free garbage collection: Each PoP can independently send, receive, and process data at its own rate without acknowledging or waiting for other points of presence. The coordination-free architecture can be leveraged for efficient garbage collection - timely truncation of logs to remove old entries.

 feature allows you to use a subset of regions, all based on your own criteria and not pre-generated (such as US, Europe, or Asia). GeoFabrics helps you comply with local data regulations (such as GDPR) and reduce read/write costs associated with global replication. GeoFabrics offers more options for storing or accessing data - with a simple on/off switch.

Standardized “Redis-like” cache interface

The GDN cache interface is similar to common caching solutions, so you can quickly get started and implement real-time, geo-replicated caches for your applications. If your application presently uses a cloud-backed cache to serve users, you can easily extend your use cases with the GDN to make your apps ready for the edge.

A geo-distributed and replicated cache can support use cases like real-time dynamic advertising, or any app with real-time user expectations. Download this 

for more details about caching data closer to your customers, partners, suppliers, and staff at the edge.

Faster Apps At The Edge With A Geo-Distributed & Replicated Cache

, data is processed closer to the source of the data at the edge of a 

. This can be particularly useful for gaming companies in a few different ways:

Reduced latency: By processing data closer to the source, edge computing can help reduce latency and improve the overall performance of games.

Improved network performance: Edge computing can help reduce the load on central servers and improve network performance, especially in areas with a high density of players.

Enhanced security: Edge computing can help protect against cyber threats by processing and analyzing data locally rather than sending it over the internet to a central server.

Better scalability: Edge computing can help gaming companies scale their systems more efficiently, allowing them to add processing power closer to the data source.


Take Your Gaming to the Edge: Unlock the Ultimate Gaming Experience with Macrometa

Edge computing is rapidly emerging as the go-to technology for businesses requiring the highest performance, scalability, and security levels. This includes gaming applications requiring the best 

, response time, and data processing power. Macrometa’s edge computing platform is leading the way in 

 on edge, allowing gamers to unlock the ultimate gaming experience. Here’s a look at how edge computing is revolutionizing gaming and how Macrometa is taking it to the next level.


Macrometa’s edge computing platform is leading the way in gaming on edge. The platform is designed to provide gamers with the ultimate gaming experience by reducing latency, improving response times, and providing a layer of security.


It provides gamers with the highest levels of security. Gaming applications can keep data secure by processing data at the edge and preventing it from being shared with third parties. This can help protect gamers’ data and ensure that it is only used for the purposes intended.

To learn more about Macrometa’s edge computing platform and how it can revolutionize gaming, check out 

 on YouTube. In this video, Alan (our Principal Technologist) showcases a first-person shooter game demo with real-time insights and a liveops dashboard on the Macrometa platform.


From this demo, you’ll learn how edge helps gaming and esports companies keep gamers engaged and maintain gamers' experience at a global scale while managing in-game data. You can also know more about:

Gaining insight into the gamer journey and experience

How to maintain gamer experience at scale

Gain insight and control game mechanics in real-time

At Macrometa, we’re committed to providing gamers with the best in gaming on edge. Our edge computing platform is designed to provide gamers with the ultimate gaming experience by reducing latency, improving response times, and providing a layer of security.


Check out the tutorials, QuickStarts, and 

 to quickly get started with Macrometa. You can actually build a streaming app in 10 mins or less! 

Enhanced Gaming Experience on Edge: Macrometa in Action

No one’s an expert at anything right away. Whether learning a new language, programming, or learning how to navigate a new platform (like Macrometa), practice makes perfect.

At Macrometa, we’re helping developers build real-time apps and 

 in minutes. If you want to become a Macrometa expert, we have these resources that can help.

One of the easiest ways to learn a new skill is to talk to others. That’s why we created our Macrometa Community Slack for anyone to come and chat about projects, ask questions, and much more. We have super users and people from the Developer Experience, Product, and Customer Success teams ready to help.

We’ve designed a Quickstart Guide in our docs to help jumpstart your Macrometa journey. It’s designed to help you learn the basics of creating a collection and running a query using some sample data. In less than 15 minutes, you can learn about Macrometa Query Workers and build an address book with full search capabilities.

We have a whole library of sample applications demonstrating what you can do with the Global Data Network. Build a streaming ETL application, a crypto trading bot, an online store, or an app that detects credit card fraud. Step-by-step tutorials with sample code hosted on GitHub.

Some of us are visual learners, so we’ve started building some great video content and tutorials on Youtube for easy access. We have 10-minute demos and tutorials, case studies, and webinars, so don’t forget to smash that like button, comment, subscribe, and click on the bell.

Macrometa unlocks amazing workflows around real-time data. If you’re a developer looking for a NoSQL database that does a whole lot more or someone wanting to get real-time insights from data around the globe, request a 

 and let us know on the community slack. There might be a t-shirt in it for you.

How to become a Macrometa Expert

2022 has been an eventful and exciting year for Macrometa. From product launches to new strategic partnerships, SOC 2 compliance, and a virtual Developer Week, we’ve had a lot of achievements to celebrate with our customers.

In this blog post, I’ll outline Macrometa’s 2022 year in review, our many successes, our plans for 2023, and our ambitious vision for the future to help our customers move their business forward.

The year 2022 has been an excellent success for Macrometa and its customers. We developed our ground-breaking edge computing platform, which has enabled us to provide powerful, secure, and reliable data solutions to our customers. Additionally, we released our enterprise-grade 

 solutions, which are designed to help businesses better manage their data and quickly gain insights. Here are some highlights:

One of the proudest moments for Macrometa from 2022 was achieving the SOC 2 certification.

In a fully-distributed world that processes information milliseconds, we must 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.

Learn more about how we're committed to keeping data safe in the SOC 2 

announcement from Macrometa CISO Eddie Garcia.

Current 2022, the next-generation Kafka Summit

 in Austin, Texas, October 4-5. This was the first-ever data streaming industry event, and we were grateful to have met real-time data experts worldwide.

Akamai and Macrometa partner to create an Edge Cloud Super Platform for Enterprise Developers. Akamai cloud computing based on Linode and EdgeWorkers edge computing technology combined with Macrometa's Global Data Network (GDN) will help enterprise developers build, deploy, and run global apps and APIs seamlessly across edge and cloud.

DevCycle partnered with Macrometa to launch the the world's first and fastest Feature-Flag-as-a-Service (all from the Edge). 

 about why they chose Macrometa and how they built an ultra-low-latency global feature flags solution using Macrometa.

Hosted a virtual Developer Week and released demo videos

Macrometa hosted a week-long Developer Week from November 7th - November 11th, 2022, where developers could see real-life use cases in action, build a streaming app in minutes, and explore the 2023 roadmap.

This week-long online virtual event focused on enterprise developers interested in building the next generation of globally-distributed, real-time applications and APIs. It features tutorials and guides from partners like Akamai and Dish Networks and customers AppSecEngineer, and DevCycle, to name a few. During Developer Week, Macrometa hosted technical content workshops and demos for real-time apps and APIs for web, mobile, IoT in SaaS, Web Services, developer tools, multiplayer gaming, video streaming, e-commerce, manufacturing, and more.

Watch all the demo videos and developer week content

We launched a public Slack Community for our users to engage with each other and with product experts at Macrometa. We’re hoping it will become a place where our community helps each other solve problems and share best practices.

Throughout 2022, we’ve been focusing on creating content to help our prospects, developers, and customers learn more about Macrometa and its solutions.

Some of the webinars that we launched in 2022:

Complex Event Processing: What’s After Apache Spark and Apache Flink

 we compare Macrometa's CEP and streaming analytics platforms like Spark and Flink to help you determine which solution best matches your use case. Learn about the difference between streaming analytics and CEP and how to get the most value from your data.

The Edge of Retail: Enabling Real-time Inventory Management

 to learn more about how real-time inventory management can boost profits and improve customer experiences. The experts at Macrometa explain how edge computing brings the power of real-time visibility to retail operations. 

Watch any of these webinars on demand at your convenience and reach out if you have any questions.

Unraveled Macrometa’s New UI And Design Process

Macrometa Head of Design - Dustin Larimer shares a peek into his team’s effort to craft a new visual aesthetic, brand system, and design system. Read more about the new UI 

At Macrometa, our vision is to be a leader in GDN & edge computing, and data management solutions. Our goal for 2023 is to continue to drive innovation in data management solutions and to provide our customers with access to our powerful and reliable data solutions. We also want to continue expanding our strategic partnerships, exploring new opportunities, and building on our existing partnerships.

We released our product roadmap highlights during Developer Week. 

 as our Product team gives a preview of some of the exciting things to come in 2023. 

We are confident that 2023 will be an exciting and successful year for Macrometa. We look forward to continuing to provide our customers with the utmost powerful solutions.

 to get started. If you're interested in testing new features, reach out to me at justin@macrometa.com.

Inside Macrometa 2022, And A Blazing Start To An Excitingly Futuristic 2023!

Macrometa is the hyper distributed cloud platform for enterprises building global applications at the Edge. 

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