April 2022 Summaries

The General Availability of RedisTimeSeries 1.6 is announced, with several major new features now available. The update supports time series multi-tenancy and can serve multiple clients accessing the same time series simultaneously. Two new aggregation types are introduced, allowing for more flexibility in querying time series data. Additionally, the `FILTER` clause is supported by `TS.MRANGE` and `TS.MREVRANGE`, enabling filtering of samples by specific timestamps or values. The `ALIGN` feature allows users to align buckets with a requested start, end, or timestamp, improving query performance. Many optimizations were implemented, resulting in faster execution times for most queries. RedisTimeSeries is now part of Redis Stack and can run on Redis on Flash, while RedisInsight provides a visual tool for exploring time series data during development.

Redis version 7.0 has been made generally available after being in development for almost a year and with three release candidates preceding it. The new version includes significant improvements to various aspects of Redis, such as Redis Functions, ACLv2, command introspection, and Sharded Pub/Sub, which represent a major evolution of existing features based on user feedback and lessons learned in production. Version 7.0 adds almost 50 new commands and options to support this evolution and extend Redis' capabilities, including the addition of new data types like bitmap, list, set, sorted set, and stream. Additionally, improvements have been made to make Redis more performant, stable, and lean by optimizing its performance vis-à-vis resources used, with some optimizations enabled by default and others requiring configuration. The development team is already working on making Redis 7.2 a reality.

In an interview with The Data Economy CXO podcast, Bülent Kiziltan, Head of Causal and Predictive Analytics at Novartis' AI and innovation lab, discusses the company's mission to improve drug development efficiencies using data, analytics, industry partnerships, and diverse talent. The goal is to shorten the period for drug development and discovery by creating additional evidence through data science methodologies. Novartis has been transforming into a data science company over several years, with its AI innovation lab focusing on causal and predictive analytics, natural language processing, and image analytics. Kiziltan emphasizes the importance of investing in both R&D-driven core capabilities and real-world impact strategies for sustainable value creation from data science and AI.

The Bonsai distributed business rules management system is an open-source application that enables businesses to automate processes, maximize productivity, and gain real-time insights into performance. It utilizes Redis as its underlying database, allowing for fast and efficient rule evaluation. The system consists of a namespace-based architecture, where rules are organized by logical separation, and entities are evaluated against these rules in real-time. This allows businesses to free up resources, reduce manual labor, and make data-driven decisions with speed and accuracy. By leveraging Redis's capabilities, Bonsai provides a scalable and reliable platform for managing business rules, making it an attractive solution for organizations looking to streamline their operations and improve productivity.

RedisDays New York concluded with a day focused on the latest developments in artificial intelligence (AI) and machine learning (ML). The event emphasized the importance of building modern data stacks with a performance-first approach, supporting AI/ML integration. Key takeaways from the sessions included the need for companies to rethink their applications using new design patterns like microservices or event-streaming architectures, and how Redis Stack can help deliver advanced AI/ML-powered services. The discussions also highlighted trends in financial services, such as digital capabilities, customer experience, new business growth, financial crime, and cybersecurity. Additionally, the event covered topics on operationalizing AI/ML in the enterprise, best practices design patterns for building and scaling microservice apps, and more.

Satyan Parameswaran, President of UPS Information Technology, shares insights on how his company uses data to drive customer satisfaction, address new market opportunities, and improve its reputation. He discusses the development of a digital twin called HEAT, which operates in real-time and predicts resourcing needs at UPS facilities. Satyan emphasizes the importance of capturing and interpreting high-quality data quickly, as well as having strong focus on data quality. The company's technology and logistics capabilities were crucial in delivering vaccines to over 100 countries during the COVID-19 pandemic.

A blood donation application called Zindagi has been developed using Redis as its main database, allowing for efficient data transmission and real-time updates, which speeds up the entire process of matching donors with patients who have the same blood type. The application uses various tools such as RedisJSON, RediSearch, Blazor, and .NET Core Runtime to provide advanced querying capabilities and a seamless user experience. By leveraging Redis' ability to send data at lightning-fast speed, Zindagi accelerates the blood donation process, enabling donors and patients to interact with each other in real-time and arrange possible donations, ultimately saving lives by providing more efficient matching of donors with patients.

Redis Enterprise offers a managed Active-Active database that allows for geo-distribution and conflict resolution. Dynomite is an open-source NoSQL database that can be migrated to Redis Enterprise for improved scalability, performance, and reliability. The migration process involves exporting data from the existing Dynomite cluster using Redis Database Backup Files (RDB) and importing them into the new Redis Enterprise Active-Active database. This process can be done in two ways: point-in-time snapshot export and continuous replication through the "ReplicaOf" feature, which replicates deltas after initial synchronization. The migration process requires configuration updates to allow for secure communication between Dynomite and Redis Enterprise, including inbound rules, peer VPCs, and TLS encryption. Once completed, the data can be accessed from multiple clusters or local endpoints, enabling geo-distribution and improved conflict resolution.

Running a Redis SQL query doesn't have to be difficult, especially when introducing it into an existing architecture without disrupting the current SQL-based solution. One approach is to map table data to Redis data structures, such as Hashes with primary keys stored in Sorted Sets or Sets. This allows for queries like inserting data, querying by product ID, and querying by price using Redis equivalent commands. Another approach uses Apache Spark and the Spark-Redis library to store and access Redis data while maintaining a SQL interface, enabling users to insert, update, and query data using SQL commands with the underlying data mapped to Redis data structures.

The RedisDays San Francisco event showcased Redis' real-time data innovations, highlighting how they are helping developers build apps faster and simplify their experience. Yiftach Shoolman, Redis Co-Founder and CTO, unveiled the latest product, Redis Stack, which consolidates key capabilities into a single product, making it easier for developers to build more real-time applications with the speed and stability of Redis. Redis Stack offers a new developer experience, including all key data models and support for various languages. Later sessions explored Redis Stack further, discussing its server-side capabilities, data models, and use cases. The event also highlighted Redis 7.0, which reemphasizes the project's commitment to open source, introducing features like Redis Functions, Sharded PubSub, ACL V2, and Command Introspection. Additionally, sessions discussed the impact of the tech industry's evolution on the developer experience, with experts sharing their insights on how developers are demanding a fully integrated experience.

Redis announced a sweepstakes as part of its RedisDays London virtual event, which resulted in the company's Director of Digital Strategy & Enterprise Architecture winning a Tesla with Autopilot features. The winner, Rameshwar Balanagu, plans to use the vehicle to help his wife overcome her fear of freeways. As an enterprise architect, Balanagu believes that Redis has the potential to be a real-time data layer for companies looking to modernize their database systems, but acknowledges that not all companies are yet ready for this level of acceleration.

Siemens, a large enterprise in the United States with over 50 thousand employees and $25 billion in annual revenue, faces significant security challenges due to its complex systems and large network of suppliers. Kurt John, Chief Cybersecurity Officer at Siemens USA, shares insights on how the company manages security risks and leads its transformation into a data-first, cloud-first company. The key to their success lies in collecting real-time data and analytics, which enables them to detect anomalies and flag potential security threats quickly. This approach also has operational benefits, such as improving efficiency and reducing costs. Additionally, Siemens is leveraging digital twins technology to transform its business model, with applications including internal efficiency, production operations, training for technicians, and customer-facing solutions. By scaling a real-time data layer, modernizing applications, and optimizing hybrid clouds, IT leaders can find short-term benefits through cost, efficiency, and other operational improvements, while the longer-term benefits come from learning customer needs and deploying real-time experiences.

Real-time artificial intelligence (AI) and machine learning (ML) use cases are becoming increasingly popular, with feature stores playing a crucial role in their successful deployment to production. The most important characteristic of a feature store for real-time AI/ML is the feature serving speed from the online store to the ML model for online predictions or scoring. Companies often perform thorough benchmarking to determine which choice of architecture or online feature store is the most performant and cost-effective. Feature stores such as Feast, Wix, Tecton, and Qwak have different architectures and support various types of features sources. The choice of an online feature store and its architecture can significantly impact performance and cost-effectiveness. Companies should carefully consider these factors when choosing a feature store for their real-time AI/ML use cases.

Redis Enterprise is now available in the Delhi region (Asia-South2) of the Google Cloud Platform, in addition to the Mumbai (Asia-South1) region. This expansion brings the total number of regions where customers can deploy Redis Enterprise to over 20 and enables real-time data use cases such as enterprise cache, session management, gaming leaderboard, fraud detection, high-speed transactions, asynchronous communication, and more. The Active-Active Geo-Distributed topology provides benefits like local latency for globally distributed applications, unification of the data layer across regions and clouds, and business continuity even when disaster strikes. With this expansion, customers can execute their data governance strategy to comply with Indian data privacy laws and align with regional requirements. The partnership between Google Cloud and Redis enables a ubiquitous platform to run businesses in an always-on fashion, accelerates time-to-market delivery of new products and services, and provides a consumption-based OpEx model for startups or small businesses to scale up or down based on their business cycles.

Redis has expanded its Developer Hub to cater to the needs of DevOps professionals, recognizing the specific challenges they face in deploying and maintaining applications at scale. The new section offers technical content to help DevOps teams operate Redis efficiently, including tutorials on integrating Redis into CI/CD pipelines, observability, provisioning, and orchestration. With features like high availability, low latency, and automated scalability, Redis Enterprise is well-suited for DevOps teams, allowing them to accelerate application deployment and reduce management overhead. The addition of tools like Argo CD, Datadog, and Azure Cache enables DevOps teams to easily embed Redis into their workflows, improving overall efficiency and productivity.

In this podcast episode, Dorothy Li, CTO of Convoy, discusses how the company uses real-time data, machine learning, mobile applications, IoT, and smart trailers to drive supply chain efficiency, sustainability, and trucker happiness. She highlights the challenges faced by the trucking industry in modernizing its operations and emphasizes the importance of addressing pain points, improving experiences, defining broader sustainability goals, and seeking to address inefficiencies. Convoy's mission is to provide meaningful services to shippers, carriers, brokers, truckers, and the entire supply chain that depends on delivering goods efficiently, sustainably, and safely. The company focuses on creating a digital freight network that helps solve the toughest problems in the massive $800 billion industry.

Redis will be attending the AWS Summits 2022 in various locations including Paris, San Francisco, India, and New York City, offering one-on-one interactions with developers and technical questions. The company aims to showcase its Redis Enterprise Cloud on AWS, highlighting its performance, scalability, high availability, and support. Key experts from Redis will be present to share demos, answer questions, and provide insights into the latest innovations planned for 2022. Attendees can also book personalized meetings, join technical sessions, and participate in on-demand demos to learn about the benefits of using Redis Enterprise Cloud with AWS.