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Macrometa Query Workers offer a powerful solution for developers aiming to reduce development time, streamline data management, and optimize query processing capabilities. In this blog, we will examine the essential aspects of Query Workers, highlighting their core features and the numerous benefits they bring to developers.

 consist of named, parameterized C8QL or SQL queries stored within the 

. Accessible through a dedicated REST endpoint, they offer a versatile and scalable approach to executing custom data processing tasks and queries at the network edge. Utilizing Query Workers enables developers to construct the back-end of their applications in mere minutes, rather than days.

Designed for easy set-up and configuration - at the edge

 are designed with you, the developer, in mind. They offer a simple and straightforward experience, requiring minimal setup and configuration to get started. To create a new Query Worker, all you need to do is define a query that performs your desired task. From there, you can easily generate an 

One of the key benefits of Query Workers lies in their ability to run directly at the network edge. This feature significantly reduces latency times for your apps, enabling you to deliver near-instantaneous responses to user 

. Unlike traditional cloud-based data processing solutions, which can be slower and less responsive due to latency in data transmission between the edge and the cloud, Macrometa Query Workers ensure a seamless and efficient experience for you and your customers.

Creating and utilizing Macrometa Query Workers

Creating and updating Query Workers is a breeze with the Macrometa GDN web console, CLI, 

, or by directly interacting with the REST API. Each Query Worker consists of specific query text and may include a parameter set (bind variables). You have the option to set default values for query parameters, making them optional during the execution of your Query Worker. Alternatively, you can require mandatory parameters for each run, with failure resulting in an error.

To illustrate, let's create data for an imaginary shop inventory using the Macrometa console. The dataset we intend to save is represented by the JSON below.

You will be adding a single data point to the inventory, as demonstrated in the image below.

The initial step involves logging in to the Macrometa platform. Once logged in, select the Query Worker tab to access the query editor. If utilizing a 

 to insert data, the query should appear exactly like this:

Enhancing the flexibility of queries, the @ symbol indicates that we're using a field as a parameter, which we refer to as a bind variable. To test the query, simply click the "Run Query" button. If the query performs as expected, you can save it and generate an API endpoint for use in your application. Creating an endpoint is as easy as clicking the "API Endpoint" button, and just like that, you've built the Create operation from the CRUD!

To view the newly added data, query the entire inventory using the following:

This represents the Read operation from the CRUD framework. Converting this query into an API endpoint is a straightforward process, as illustrated in the image below.

Each new query worker has its own endpoint on  Macrometa GDN. Query Workers are organized by 

, enabling developers to have different Query Workers for different geo-regions as well as for different fabrics within the same region.

Macrometa’s integration with Akamai EdgeWorkers

Macrometa Query Workers into a native Akamai EdgeWorker

 and automatically deploy it onto Akamai’s edge. Simply supply your Akamai credentials and with the click of a button, the API gets generated as an Akamai EdgeWorker and is automatically deployed to 1000s of Akamai edge PoPs that query and serve data from adjacent Macrometa regions on Linode. Check out the

 (like a Netflix or a Hulu) that runs on the Akamai edge and Linode leveraging Macrometa, Linode and Akamai EdgeWorkers to see it in action!

Macrometa Query Workers offer a versatile and scalable solution for performing custom data processing tasks and real-time queries. With a straightforward setup process that allows you to focus solely on crafting queries, deployment is effortlessly handled by the Macrometa GDN.

By leveraging the flexibility and scalability of Query Workers, developers can optimize their data management and query processing workflows and incorporate them into existing workflows and processes. This can help improve 

 and minimize latency, allowing developers to execute complex 

 and computations on large datasets quickly and efficiently.

Tutorial: Experience The Power Of Macrometa Query Workers

The manufacturing industry is facing significant challenges, driven by increasing complexity in global supply chains, rising inflation, a pressing need for sustainability, and keeping up with new technologies. In response to these challenges, manufacturers are turning to 

 to transform their operations. The IIoT, 5G, and edge computing are transforming how data is collected, processed, and analyzed, paving the way for smarter, more efficient factories. Digital twins, predictive maintenance, robots and cobots, and blockchain/NFT are driving innovation and efficiency in various applications.

At the core of these technology-driven transformations lies the 

, which facilitates seamless data integration, real-time monitoring, and data analytics. This powerful, scalable infrastructure streamlines operations and enables data privacy and fraud detection, while AI and machine learning (ML) augment the capabilities of digital twins, predictive maintenance, and robotics, among other applications.

Seamless data integration for manufacturing efficiency

 from multiple sources is critical for manufacturing companies in today's competitive landscape. By consolidating data from various systems like Enterprise Resource Planning (ERP), Manufacturing Execution Systems (MES), Supply Chain Management (SCM) and Quality Management Systems (QMS), manufacturers can gain a comprehensive understanding of their operations, leading to informed decisions, optimized performance, and enhanced innovation. The importance of data integration in manufacturing lies in several key areas, including enhancing visibility and efficiency across the organization, facilitating improved decision-making, and supporting agility and adaptability with real-time insights.

By consolidating data from multiple sources, manufacturers create a rich and comprehensive dataset that can be leveraged to train and deploy AI and ML algorithms. These advanced technologies drive innovation and efficiency in various applications such as IIoT, robots and cobots, automated factories, blockchain and NFT, augmented reality-based 

The integrated data serves as the fuel for AI and ML models, providing them with the necessary information to learn from patterns, make predictions, and optimize processes. For instance, AI-powered 

 can analyze real-time data to improve equipment performance and predict maintenance needs. Data integration also plays a vital role in enabling AI-driven predictive maintenance, allowing manufacturers to minimize downtime, extend the life of equipment, and reduce costs.

Real-time analytics and tracking to optimize operations

In today's competitive and fast-paced manufacturing environment, "just in time" has evolved into "real-time," emphasizing the importance of 

 for businesses to stay ahead of the curve. Real-time tracking and analytics enhance overall efficiency and reduce waste, including in the area of digital manufacturing.

End-to-end visibility for optimal production 

One of the primary reasons real-time tracking analytics is essential for manufacturing is its ability to provide end-to-end visibility into the production process in their own factories or contract manufacturers. By monitoring production processes in real-time, manufacturers can quickly identify bottlenecks, adjust workflows, and maintain optimal production levels.

Real-time analytics also enables manufacturers to be more agile and responsive to changing market demands. By continuously monitoring customer orders, 

 levels, and production capacity, manufacturers can better align their production schedules with customer needs.

Real-time tracking and analytics help improve quality control in the manufacturing process. By collecting and analyzing data from 

, RFID tags, and other sources, manufacturers can quickly identify and address quality issues more quickly, reducing the likelihood of defects and enhancing the overall quality of their products.

Accelerating automation and emerging technologies

, 5G, and edge computing are transforming how data is collected, processed, and analyzed, paving the way for smarter, more efficient factories. However these technologies will require unprecedented speed and performance across the globe to support all factories, warehouses and other business locations.

Digital twins can minimize the need for physical prototypes and reduce development time and costs. Augmented reality can also provide real-time information and guidance to workers, enabling them to perform complex tasks more efficiently and accurately. 

, driven by advanced analytics, lets manufacturers predict when equipment will fail and take preventative measures, reducing downtime and maximizing asset performance. Moreover, the adoption of robots and collaborative robots (cobots) is automating factories, freeing up workers to focus on higher-value tasks.

As manufacturers increasingly rely on digital technologies and collect vast amounts of data, they become more susceptible to data breaches and cyberattacks. Ensuring data privacy and 

 with regulations is critical to maintaining customer trust and avoiding costly fines and penalties. Industrial cybersecurity is critical in the manufacturing sector to prevent potential dangerous risks. Real-time fraud and anomaly detection can help stop fraudsters in the act - whether it is inventory, procurement, intellectual property, or cyber fraud.

The benefits of a hyper distributed cloud for manufacturing - Macrometa

Manufacturers may struggle with their current platforms as technologies become more complex and soon will require 

. With Macrometa's hyper-distributed cloud, you can avoid the speed and scalability limitations of a centralized cloud or edge servers respectively and achieve the necessary performance for your advanced manufacturing operations.

Behind the scenes, Macrometa can help you navigate a labyrinth of 

 to provide a holistic view of your data and perform real-time data analytics and ML algorithms across different data sources and formats for actionable insights. Easily uncover real-time data insights to identify and address potential issues, such as supply delays, power outages, or changes in customer demand, before they become major problems.

Manufacturers often have factories in different regions to diversify their supply chain, reduce the risk of disruptions, access local markets, and take advantage of lower production costs. 

Macrometa’s hyper distributed cloud platform

 enables manufacturing companies to access and process data from anywhere in the world, providing real-time insights into their operations and enabling seamless collaboration and communication with partners and customers across the supply chain.

 in-region and globally with data encryption, access controls, and data anonymization features, as well as regular data snapshots to ensure data resilience in the event of a breach or system failure.  With Macrometa, you can track production, supplies, and package safety protocols, and prevent fraud with anomaly detection, alerts, and continuous risk assessments.

 provides low-latency and high-throughput processing capabilities that can handle the large amounts of data and event processing required for smart technologies like digital twins or robots. With its real-time operations, analytics, and event processing capabilities, the Macrometa GDN can enable faster and more efficient implementation of smart technologies and IIoT, allowing companies to stay competitive in a rapidly evolving market.

For more information about our Manufacturing Solutions, 

 with one of our solution architects, we look forward to speaking with you!

Accelerating Digital Transformation in Manufacturing With A Hyper Distributed Cloud

Imagine you're running an online shoe store that caters to a global customer base. Your customers expect a seamless, low-latency experience when browsing your catalog, and your development team needs an efficient way to build and manage the backend. Macrometa, a 

In this blog post, we'll discuss the advantages of using Macrometa over traditional centralized cloud platforms and walk you through the process of building a shoe store catalog API that provides CRUD and search functionalities.

 architecture simplifies the process of building and managing a geo-distributed application. It provides a single API to access key-value, document, and graph data models, along with built-in search, pub-sub, queueing, complex event processing, and general-purpose computing capabilities.

This stands in contrast to traditional centralized cloud platforms, where developers must design, build, and manage applications that are typically using many various tools and single-purpose databases to achieve similar functionality. Macrometa's platform ensures data consistency and synchronization across multiple regions using proprietary CRDT technology, providing significant advantages for developers working on applications with a global audience.

 is an advanced full-text search engine designed to support key values, documents, and graphs as data models. Unlike traditional full-text indexes, Macrometa Search offers a highly configurable and customizable experience, combining Boolean and generalized ranking retrieval techniques to refine search results. It ranks all Boolean-approved results by relevance using the Vector Space Model in conjunction with BM25 or TF-IDF weighting schemes.

Macrometa Search is equipped with state-of-the-art indexing and search capabilities, enabling sophisticated search operations on 

Search views are virtual collections that enable fast full-text searching across multiple linked collections.

Analyzers parse input values and transform them into sets of sub-values for tokenization, language-specific word stemming, case conversion, and removal of diacritical marks.

Macrometa Search features are integrated into C8QL as a SEARCH operation and a set of C8QL functions. This integration allows for a wide range of use cases, such as performing federated full-text searches over product descriptions in a webshop, retrieving and ranking research database information by relevance, or querying 

 for specific titles and sorting the results by best matching while favoring movies with longer durations.

Key features of Macrometa Search include:

 Perform intricate search operations using Boolean operators to combine multiple conditions and refine results.

 Automatically rank search results based on their relevance to the query.

 or partial matches to enhance the accuracy of results.

 Customize the ranking algorithm and fine-tune the relevance of search results to suit specific use cases.

 Configure analyzers for tokenization, language-specific word stemming, case conversion, and removal of diacritical marks.

Retrieval of both documents and projections of documents:

 Retrieve complete documents or specific projections based on search criteria.

Combinable search queries across multiple data models and access patterns:

 Seamlessly perform search queries across various data models, such as key-value pairs, documents, and graphs.

Geo-replicated search indexes for instant results:

 Benefit from low-latency search results through the geo-replication of search indexes.

 offers a powerful, flexible solution for building advanced search functionality into applications. Its extensive features, configurability, and seamless integration with C8QL make it an invaluable tool for developers looking to create responsive and engaging applications for global audiences.

To demonstrate how easy it is to create a geo-distributed shoe store inventory API with Macrometa, we'll guide you through the following steps:

Create a document collection called inventory:

 In the Macrometa console, create a new document store collection named inventory.

 Import the JSON file containing shoe catalog data into the inventory collection.

 Write your first query to fetch all documents in the inventory collection:

 Name your Query Worker saveProduct and write an INSERT statement to add a new product to the inventory collection. The @ symbol in this query example is a `binding parameter`. They allow you to leave values as wildcards, so you can pass values along when you call the API to dynamically write and read your data.

 Enable high-performance search on the inventory collection by creating a search view named 

 by shoe name. Use the ANALYZER function to perform various search operations and filter results based on size and price range.

 Save the search query as a Query Worker named inventory-search, and now you have a fully functional search API!

By following these steps, you'll have fully functional, geo-distributed shoe store inventory APIs for CRUD and Search that provide a low-latency experience for users around the world.

Macrometa offers a better developer experience and makes building distributed applications easier compared to traditional centralized cloud platforms. Its global data platform, simplified data management, and built-in functionalities such as 

 and Search Views enable developers to easily build applications like a shoe store catalog API, providing a seamless experience for end-users.

 a 30 day trial to get started today, or 

; in my next posts, I’ll be showing you how to use Graphs to create a recommendation engine and 

 (complex event processing) to implement real-time inventory management!


Building a Hyper-Distributed Shoe Store Inventory API with Macrometa

Data is the new gold, and like gold, it has to be carefully mined and transformed to uncover its true value - data insights. But not all data is of equal value, and it can be challenging to separate the gold from the dust. For businesses, becoming a data-driven enterprise involves overcoming several barriers, such as disparate data systems and large volumes of data, choosing the right technology for processing data quickly, and developing a technology framework that supports the next wave of technology for AI and ML.

Legacy systems, faraway data centers or clouds, and data coming from various places and devices on the edge can impede the transformation of data into a valuable asset. It's also crucial to differentiate between operational and transactional data since operational data - such as customer browsing behavior and preferences - provides insights into customer behavior, predicting future trends, and staying ahead of the competition.

The time value of data is especially crucial in situations like autonomous vehicles, 

 where the ability to process data in real-time can mean the difference between life and death, preventing fraudulent activities, optimizing delivery routes, monitoring the temperature, and humidity levels of goods during transportation.

Accelerate insights with a hyper distributed cloud

To become a data-driven enterprise, businesses need a hyper distributed cloud with 

 options to process and analyze data where it is generated, reducing latency and improving response times. That's where Macrometa comes in. Macrometa is a hyper distributed cloud platform that collects, processes, and analyzes real-time data from various sources, including legacy systems, 

, mobile devices, and social media. With Macrometa, businesses can build APIs in minutes to connect data from multiple systems and accelerate insights.

 involves overcoming several barriers, differentiating between operational and transactional data, and utilizing edge computing options. Macrometa's hyper distributed cloud platform can help businesses collect, process, and analyze data from various sources to make informed decisions in real-time. 

To take the first step toward data-driven success, download the 451 Research Business Impact Brief: 

Data-Driven at the Edge: Unlocking Business Insights

The Journey To A Data-Driven Enterprise

Logistics is an ever-evolving industry that presents numerous challenges, including the ongoing pandemic, supply chain disruptions, bottlenecks, inflation, and the need for sustainability. From global corporations to niche players, logistics companies must adapt quickly to the changing landscape.

 by setting new standards for faster and more efficient logistics services, focusing on supply chain transparency and real-time tracking. In this blog, we will explore how technology enables logistics companies to become more innovative, efficient, and agile. Data integration and real-time analytics are leading the transformation, and emerging technologies like autonomous vehicles are on the horizon.

Data synchronization and integration with a unified view

 is essential for an accurate view of their operations - across all data sources and types - from package sensors, senders, transporters, recipients, and to other stakeholders.

 companies need to integrate and synchronize data with various systems such as transportation management systems (TMS), route optimization tools like GPS, and customer relationship management software (CRM). Macrometa can help make each step of the logistics process more efficient with real-time data integration. It allows logistics companies to monitor the journey - from package weight, ETA, weather conditions to maintenance issues.

” view that serves as a centralized hub for monitoring and managing logistics. The dashboards show key data from multiple sources like IoT sensors, TMS, CRM, or route optimization tools. This single view of data, combined with real-time operational analytics, enables them to personalize the customer experience, ensure compliance, and prepare for changing conditions well in advance.

Fraud prevention and data privacy are critical concerns for logistics companies as they have a lot of personal data in multiple systems. Macrometa's built in 

 helps you comply with regional regulations such as GDPR and CCPA with data pinning, vaulting, encryption, access controls, and data anonymization features for reporting across regions. Real-time 

 alerts you to any planned route deviation or any other anomalies to thwart fraud or theft.

Logistics companies need to be prepared to support 

 data-intensive, high-speed transactions and algorithms as they adopt autonomous vehicles and drones. Macrometa can help logistics companies analyze vast amounts of data in real time, identify patterns and trends, and make more informed decisions about the optimal routes, schedules, and modes of transportation for each shipment. Logistics companies can gain predictive insights into potential disruptions or delays, enabling them to take proactive measures to mitigate them.

 platform helps logistics companies overcome system integration challenges by providing real-time data analytics and ML algorithms across different data sources and formats. The platform enables logistics companies to access and process data from anywhere in the world, providing real-time insights into their operations and enabling seamless collaboration and communication with partners and customers. Learn more by downloading this 

, about Transforming Deliveries in the Digital Age. 

 to find more about how Macrometa can streamline operations with speed and efficiency, improve customer experiences, fortify data protection, and easily embrace new technologies.

Simplify Logistics By Making Shipments Happen In Real Time 

The hospitality industry is a complex and fast-paced world that requires seamless integration of different systems to provide a personalized guest experience. However, managing and processing large amounts of data from different sources such as bookings, operations, and customer preferences can be challenging for hotels and other accommodations. The COVID-19 pandemic has further highlighted the need for hospitality providers to accelerate their digital transformation and leverage technology to increase revenue opportunities and operational efficiency. In this blog, we will explore the new real-time requirements of the hospitality industry and how a hyper-distributed cloud can help meet these demands.

The journey to a data-driven guest experience

Accessing and managing customer data is a complex challenge for hotels, resorts, motels, bed and breakfasts, home sharing/rentals, or even local attractions, given the variety of systems involved in the booking process. For example, when a customer makes a reservation through an 

, the reservation data is transmitted to the property’s Channel Management System (CMS), which updates the 

This data has to be bi-directional; if a traveler comes to the hotel without a reservation, the front desk needs to update the Property Management System (PMS), or Central Reservation System (CRS), which then needs to update inventory on the CMS so that online OTA inventory remains up-to-date.

 accommodations require a unified and real-time view of data to make instant changes across all data points to accommodate competitive demand, customer preferences, or weather and property issues.

Going beyond points programs with content personalization

The hospitality industry was a pioneer in the beginning of modern personalization by starting loyalty programs that could be used with frequent flier points over 

. However, they face a significant challenge in managing the vast amount of 

 that is generated from different sources, including search results, parameters, and getting access to third-party data from affiliates.

You don’t need alchemy to change an ocean of complexity with a thousand small rivers into a sea of gold. By leveraging this data with real-time data synchronization and analytics, hospitality organizations can gain valuable insights into guest behavior, preferences, and needs. This information can be used to 

, optimize pricing strategies, and improve operational efficiency while leaving you poised for more revenue opportunities.

Navigating global data privacy and compliance requirements

Hospitality entities are increasingly concerned with data privacy and security with regulations such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Personal Information Protection Law (PIPL), particularly when it comes to guest data. Macrometa can help you adhere to data governance regulations globally with 

, data encryption, access controls, and data anonymization features.

Real-time personalization, data synchronization and protection for guest-centric experiences

Personalization, real-time data synchronization, and data protection are critical to providing guests with exceptional experiences. Macrometa offers ready-to-go hospitality solutions built upon the three components of the 

 provides an interconnected and distributed way to store, access, and process data wherever it’s needed. By using Macrometa's solutions that apply real-time data and low-latency 

, hospitality entities can analyze information on guest preferences, behaviors, and needs to provide highly personalized recommendations to help increase revenue growth. With advanced 

 measures, hotels can comply with regulatory requirements and safeguard guest data while increasing operational efficiency. To learn more about how to apply real-time data to revolutionize guest experiences, download this 

. For more information about our ready-to-go 

 with a solution architect, we look forward to speaking with you!

Transforming Guest Experiences with Real-Time Data

” by Chetan Venkatesh, CEO and Co-founder of Macrometa, originally published by The Fast Mode in February 2023.

Cloud computing has revolutionized the way businesses operate, offering cost-effective and scalable solutions that can be accessed from anywhere in the world. However, managing multiple cloud environments can be complex and time-consuming, requiring businesses to use different tools and technologies to manage each platform. Application development can also be time-consuming and expensive and often siloed by departments.

Enter low-code and poly clouds. These two technologies offer a simplified way for businesses to manage cloud environments and streamline application development and deployment.

 offer a simplified approach to application development, with pre-built templates and drag-and-drop interfaces that make it easy for developers to build and deploy applications quickly. This can help to reduce the time and cost of application development while also improving the speed and agility of the development process. Low-code platforms are becoming increasingly popular among businesses of all sizes, as they offer a cost-effective way to build and deploy applications without the need for extensive coding knowledge.

The article lists that low code is accessible even for non-technical users, letting developers focus on more complex projects and therefore provide a better use of developers’ time. The key perk - a faster time to market, or as the article states, “they who build faster win.” Of course, if you don’t need as many specialized developers, you can reduce costs, and, “time is money.” Lastly, the article indicates low code provides total visibility and makes collaboration easier.

Be ready for data transformation initiatives

“We can unlock the power of multiple cloud service providers with a polycloud strategy. This approach offers unparalleled efficiency, leveraging the unique strengths and capabilities of each vendor to maximize your business potential - not just saving resources but gaining them,” per Chetan Venkatesh in the article.

, which involves managing multiple cloud platforms separately, poly clouds allow businesses to build applications that can run on multiple cloud platforms from a single platform. Combining low-code and poly clouds offers even greater benefits, enabling businesses to take advantage of the strengths of different cloud platforms while also streamlining the application development and deployment process.

Low-code and poly clouds are poised to become essential tools for developers and IT professionals alike. If you're interested in learning more about low-code and poly clouds and Macrometa - the hyper distributed cloud for the next generation of applications - 

Low-Code And Poly Clouds in 2023: The Future of Cloud Computing

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 

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