
Redis
Founded Year
2011Stage
Series G | AliveTotal Raised
$355MValuation
$0000Last Raised
$110M | 4 yrs agoMosaic Score The Mosaic Score is an algorithm that measures the overall financial health and market potential of private companies.
-41 points in the past 30 days
About Redis
Redis is a real-time data platform focused on providing in-memory data structure storage and enterprise scaling solutions. The company offers products such as an in-memory database for caching and streaming, a fully managed cloud service integrated with major cloud providers, and self-managed software designed for enterprise needs. Redis primarily serves sectors that require high-performance data operations, such as financial services, gaming, healthcare, and retail. It was founded in 2011 and is based in Mountain View, California.
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ESPs containing Redis
The ESP matrix leverages data and analyst insight to identify and rank leading companies in a given technology landscape.
The NoSQL database market revolves around the development, provision, and adoption of non-relational database management systems. NoSQL databases are designed to handle large volumes of unstructured or semi-structured data, offering scalability, high performance, and flexibility compared to traditional relational databases. The market encompasses a variety of NoSQL database technologies, including…
Redis named as Outperformer among 15 other companies, including Microsoft Azure, Oracle, and Cloudera.
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Expert Collections containing Redis
Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.
Redis is included in 3 Expert Collections, including Unicorns- Billion Dollar Startups.
Unicorns- Billion Dollar Startups
1,249 items
Conference Exhibitors
5,302 items
Tech IPO Pipeline
257 items
The tech companies we think could hit the public markets next, according to CB Insights data.
Redis Patents
Redis has filed 15 patents.
The 3 most popular patent topics include:
- database management systems
- data management
- databases

Application Date | Grant Date | Title | Related Topics | Status |
---|---|---|---|---|
5/9/2023 | 7/23/2024 | Computer memory, Database management systems, Data management, SDRAM, Databases | Grant |
Application Date | 5/9/2023 |
---|---|
Grant Date | 7/23/2024 |
Title | |
Related Topics | Computer memory, Database management systems, Data management, SDRAM, Databases |
Status | Grant |
Latest Redis News
Oct 24, 2024
FinTech systems are at the forefront of transforming the way payments are processed. With the increasing demand for instant and seamless financial transactions, building resilient FinTech systems capable of handling high-volume, low-latency payment processing has become a critical requirement. To achieve this, several key factors must be considered, including system architecture, data management, and fault tolerance mechanisms. An example, XTM recently launched its Paynuity Paycard program, PayNow, designed to facilitate earnings payouts, remittances, and employee benefits for temporary and migrant workers in the U.S. This solution aims to streamline financial access for workers who often face barriers with traditional banking systems. Scalable System Architecture A scalable architecture is essential for FinTech systems to accommodate fluctuating transaction volumes without compromising performance. Microservices architecture is often preferred because it allows different components to be developed, deployed, and scaled independently. This modularity ensures that the payment processing system can handle spikes in transaction volume by automatically scaling the required services. Additionally, utilizing load balancing techniques ensures that traffic is distributed evenly across servers, minimizing latency and preventing system bottlenecks. Cloud-native solutions, such as container orchestration with Kubernetes, provide further scalability and resilience. By using cloud-based infrastructure, FinTech companies can dynamically allocate resources based on demand, ensuring that high-volume transactions are processed with minimal delays. Optimizing Data Management and Storage Efficient data management is another key factor in building resilient FinTech systems. Payment processing requires real-time data analysis to detect fraud, authorize transactions, and update account balances. Utilizing in-memory databases, such as Redis or Memcached, can significantly reduce latency by storing frequently accessed data in memory rather than on disk. This approach allows for faster data retrieval and supports the real-time processing demands of high-volume payment systems. Moreover, leveraging distributed database systems, like Apache Cassandra or Amazon DynamoDB, ensures that data is available even in the event of hardware failures. These databases replicate data across multiple nodes, providing fault tolerance and improving system reliability. Implementing a data partitioning strategy can also help manage large datasets efficiently, reducing query times and ensuring consistent performance under heavy load. Ensuring Fault Tolerance and Redundancy Resilient FinTech systems must incorporate fault tolerance to ensure continuous operation, even in the face of component failures. One approach to achieving this is through the use of redundant systems and failover mechanisms. For instance, active-active clustering allows multiple servers to handle transactions simultaneously, with traffic rerouted to a backup server if the primary server fails. This setup ensures uninterrupted payment processing and minimizes downtime. Additionally, implementing circuit breaker patterns can prevent a failure in one part of the system from cascading across the entire architecture. This pattern temporarily halts requests to a failing service, allowing it to recover without overwhelming other system components. Leveraging Advanced Security Measures Given the sensitivity of financial data, building secure FinTech systems is paramount. Integrating advanced encryption techniques and tokenization ensures that transaction data is protected from unauthorized access. Furthermore, real-time monitoring using AI-driven analytics can detect potential security threats or unusual transaction patterns, allowing for immediate mitigation. By adopting a comprehensive approach that addresses scalability, data management, fault tolerance, and security, FinTech companies can build resilient systems capable of delivering low-latency payment processing, even under high transaction volumes. This resilience is crucial for meeting the growing expectations of consumers and businesses in the digital age. [To share your insights with us, please write to psen@itechseries.com ]
Redis Frequently Asked Questions (FAQ)
When was Redis founded?
Redis was founded in 2011.
Where is Redis's headquarters?
Redis's headquarters is located at 700 East El Camino Real, Mountain View.
What is Redis's latest funding round?
Redis's latest funding round is Series G.
How much did Redis raise?
Redis raised a total of $355M.
Who are the investors of Redis?
Investors of Redis include Technology Crossover Ventures, Tiger Global Management, SoftBank, Softbank Capital, Viola Ventures and 10 more.
Who are Redis's competitors?
Competitors of Redis include Aerospike, MarkLogic, SingleStore, DataStax, Imply and 7 more.
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Compare Redis to Competitors

Cloudera is a company that operates in the hybrid data management and analytics sector. Its offerings include a hybrid data platform that is intended to manage data in various environments, featuring secure data management and cloud-native data services. Cloudera's tools are used in sectors such as financial services, healthcare, and manufacturing, focusing on areas like data engineering, stream processing, data warehousing, operational databases, machine learning, and data visualization. It was founded in 2008 and is based in Santa Clara, California.

DataStax specializes in providing a comprehensive generative AI stack for the development and deployment of production-ready applications within the technology sector. The company offers a RAG API that supports both vector and structured data, ensuring secure, compliant, and scalable solutions that are integrated with leading AI ecosystem partners. DataStax primarily caters to developers and enterprises looking to leverage generative AI technologies across various cloud platforms. It was founded in 2010 and is based in Santa Clara, California.

SingleStore provides real-time analytics and artificial intelligence (AI) solutions in the data management industry. The company offers a distributed SQL database that supports both transactional and analytical workloads, enabling data-intensive applications to operate with simplicity, speed, and scale. It primarily serves digital giants and leading SaaS providers. It was formerly known as MemSQL. It was founded in 2011 and is based in San Francisco, California.

CrateDB operates as an enterprise database optimized for time series, documents, and vector data workloads. The company offers a distributed structured query language (SQL) database that enables complex queries on various data types and integrates with artificial intelligence (AI) and machine learning (ML) frameworks. It primarily serves sectors that require real-time analytics and data management, such as AI/ML industries. The company was founded in 2013 and is based in Redwood City, California.

Cockroach Labs focuses on the development of distributed structured query language (SQL) databases. It provides CockroachDB, offering features such as elastic scaling, cloud-native compatibility, built-in survivability, and consistent SQL, primarily serving industries such as finance, gaming, manufacturing and logistics, media and streaming, retail and e-commerce. It was founded in 2015 and is based in New York, New York.

MariaDB is a cloud database company that specializes in open-source database products and services for various business sectors. The company offers enterprise and community versions of its database server, which support transactional, analytical, and mixed workloads for relational and JSON data models. Its services include technical support, database migration, remote database administration, consulting, and training. MariaDB was formerly known as SkySQI. It was founded in 2009 and is based in Milpitas, California.
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