When synchronizing databases, ensure data persistence and availability of captured data changes while mitigating the performance penalty associated with shipping them to the target database.

This example shows how to use the Write Behind pattern to map data from Redis Hashes to MySQL tables. The recipe maps all Redis Hashes with the prefix person: to the MySQL table persons, with being the primary key and mapped to the person_id column. Similarly, it maps all Hashes with the prefix car: to the cars table.

This demo for RedisGears uses fbprophet for timeseries prediction in anomaly detection.

Content delivery performance is essentially about latency and speed. While content delivery networks (CDNs) offer image optimization solutions and the shortest hop to the user, additional byte savings can be achieved through optimizing memory usage of the image. Doing so saves data on the backend, in client’s devices, and improves delivery performance since fewer bytes require fewer network packets to be transmitted.

This example illustrates how to automatically process, transform, and model different types of data using serverless content delivery optimizer logic with Redis and RedisGears.

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Modern applications are increasingly sophisticated, and often rely on multiple databases to deliver expected results. Retrieving data from each database introduces latency, impacting user experience. Using a database that supports multi-models natively dramatically improves application performance.

This step-by-step demo starts from Redis Hash and dynamically adds new models using Redis Modules. The accompanying video illustrates retrieving and synchronizing data across multiple databases to “find the restaurants my friends recommended in my 3-mile radius” and sort by visitor load.

This application was developed as a no-touch alternative to tracking attendance in classrooms, previously done through fingerprinting. It can be applied to any situation where facial recognition is needed. Built using the RedisEdge stack, it is able to process the image data in real time, and easily scales to analyze large groups of people.

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According to the CDC, during the COVID-19 pandemic, all healthcare workers must follow strict guidelines and protocols from OSHA regarding wearing PPE. N95 masks are the PPE most often used to control exposure to infections transmitted via the airborne route. Therefore, checking the medical staff’s PPE safety protocol is especially crucial during this pandemic.

This web-based app analyzes video streams and applies a mask detection model to check that the medical staff is following established safety protocols.

The recent shift to virtual learning, for everyone from K-12 to higher education, has forced students, parents, and teachers to change how they approach learning. One area that is still evolving is how to evaluate student performance and achievement. Innovations in both learning and evaluation methods are needed.

This project includes two components. The first is aimed at students—a browser plug-in that transcribes notes from videos and auto-generates quizzes to check comprehension. The second is aimed at educators—a web-based application to upload courses, generate quizzes, and answer student questions.

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Many non-profit organizations recruit volunteers through social media. However, managing the lists of volunteers for each event is often done manually, and communicating updates via social media is inefficient. A better way of organizing volunteer activities is needed.

This application automates a number of steps in the process of recruiting and confirming volunteers for events. It also enables organizers to easily communicate with confirmed volunteers to provide timely updates.

This application is a blockchain-based ecommerce platform for buying and selling SPEC tokens without a third party. Since every transaction on a blockchain platform triggers a fee, this application abstracts common, low-value tasks from the platform and performs them within Redis in order to avoid unnecessary fees.

Internet fraud is evolving. The more you try to stop abuse, the more complex methods fraudsters use to trick you. The idea of a real-time fraud detector is not new, but getting it right is a major challenge.

This project identifies different types of fraud in an ad network, then publishes the data for further analysis and visualization.

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