Latest news with #Cohesity
Techday NZ
3 days ago
- Business
- Techday NZ
Cohesity & MongoDB expand integration for enterprise data resilience
Cohesity has announced an expanded integration with MongoDB to deliver enhanced data protection and recovery performance for large enterprise workloads. The deepened integration allows Cohesity customers to utilise the MongoDB Third Party Backup Ops Manager API, offering new controls and capabilities tailored to scenarios involving extensive datasets and stringent resilience requirements. This is particularly targeted at sectors such as financial services and the global banking industry, alongside Fortune 500 organisations, that demand minimal downtime and heightened security. According to Cohesity, this functionality is now available for users of its DataProtect solution. By leveraging the new API-based integration, the company states that enterprises can better address data recovery speed and backup process efficiencies. Cyber resilience focus Vasu Murthy, Senior Vice President and Chief Product Officer at Cohesity, commented on the growing challenges faced by large organisations due to the increasing threat of ransomware attacks and the related business risks. "With ransomware attacks now commonplace, cyber resilience is a strategic priority for all organisations. This is particularly true of large enterprises, which have a very low tolerance for risk," said Vasu Murthy. "Downtime for any reason can mean millions of dollars and massive reputational damage. As trusted providers for many of the world's largest companies, Cohesity and MongoDB are working together to strengthen our customers' ability to bounce back fast." This expanded partnership between Cohesity and MongoDB aims to address the growing need for robust, enterprise-grade data protection. Paul Henaghan, Managing Director, Cohesity, Australia and New Zealand, highlighted the benefits for institutions across the region in the context of the evolving cyber threat environment. "In today's cyber threat landscape, organisations across Australia and New Zealand require data resilience that goes beyond basic backup, especially among large enterprises with complex technology environments and systems. Our expanded integration with MongoDB gives customers the enterprise-grade speed, scale and security needed to confidently protect and recover their most vital workloads. This is a powerful step forward in helping ANZ businesses reduce downtime risk and maintain trust in the face of escalating cyber threats," said Paul Henaghan. Industry research and capabilities Cohesity's own research indicates a gap between organisations' perceived and actual cyber resilience. Only a small fraction—2%—of participants in a global survey reported being able to recover data and restore business processes within 24 hours following a cyberattack. The company suggests that the new MongoDB integration helps close this gap for customers seeking to meet rigorous recovery point and time objectives. Some of the advanced features introduced through this integration include improved speed, as parallel data streams allow for the simultaneous processing of billions of objects. Cohesity states that intelligent backups can bring MongoDB databases online four times faster than traditional approaches. The system also boasts a scale-out architecture capable of accommodating petabyte-sized workloads on a single platform, introducing efficiencies such as global, variable-length deduplication and compression to reduce data footprint. Additional security measures include support for immutable write once, read many (WORM) storage, end-to-end data encryption, continuous data protection, and secure SSL authentication—all underpinned by Zero Trust principles and a multi-layered defence model. Collaborative efforts Representatives from MongoDB and its partners echoed the significance of the collaboration. Benjamin Cefalo, Senior Vice President of Product Management at MongoDB, highlighted the broader mission of supporting performance and agility without sacrificing enterprise resilience. "As the leading document database for modern applications, MongoDB empowers organisations to build, scale, and innovate faster," said Benjamin Cefalo. "Our collaboration with Cohesity reinforces that mission by helping customers protect their data with robust, enterprise-grade resilience—without compromising the agility and performance developers expect from our platform." Juan Orlandini, Chief Technology Officer for North America at Insight, commented on the immediate applicability of these improvements to large organisations operating with critical datasets. "Cohesity continues to broaden and enhance its support for enterprise workloads, including those with critical customer datasets at petabyte scale. The deepened integration with MongoDB addresses the most pressing data security concerns for large enterprises and financial organisations by delivering the speed, scalability, and security they require." Market demand The integration between Cohesity and MongoDB is geared towards organisations with large-scale, mission-critical database workloads that cannot afford significant interruptions. Cohesity cites ongoing demand among its global enterprise customer base, which includes over 85 of the Fortune 100 and nearly 70% of the Global 500, for enhanced backup and recovery processes.
Business Wire
3 days ago
- Business
- Business Wire
Cohesity Strengthens Resilience of Large, Mission-Critical MongoDB Workloads
SANTA CLARA, Calif.--(BUSINESS WIRE)-- Cohesity, the leader in AI-powered data security, today announced a deeper integration with MongoDB, the leading database for modern applications. The new integration provides advanced performance and control capabilities for backup and recovery of MongoDB databases. Cohesity is among the first data protection software providers to deliver MongoDB workload protection through the MongoDB Third Party Backup Ops Manager API. Designed with large, critical datasets in mind, the integration supports the strict resilience requirements of global banking, financial services, and Fortune 500 enterprises. The integration is currently available for Cohesity DataProtect. .@Cohesity, the leader in AI-powered data security, today announced a deeper integration with MongoDB, the leading database for modern applications, to provide advanced performance and control capabilities for backup and recovery of MongoDB databases. Share "With ransomware attacks now commonplace, cyber resilience is a strategic priority for all organizations. This is particularly true of large enterprises, which have a very low tolerance for risk,' said Vasu Murthy, senior vice president and chief product officer, Cohesity. 'Downtime for any reason can mean millions of dollars and massive reputational damage. As trusted providers for many of the world's largest companies, Cohesity and MongoDB are working together to strengthen our customers' ability to bounce back fast.' Cohesity research finds organizations overestimate their cyber resilience capabilities and maturity—often overlooking deficiencies in data recovery speed and capabilities. In fact, only 2% of respondents in a global survey said they could recover data and restore business processes within 24 hours if a cyberattack occurred. The performance and control capabilities enabled through Cohesity's deepened integration with MongoDB supports customers in meeting stringent recovery point and recovery time objectives. Advanced capabilities include: Greater speed – parallel data streams enable billions of objects to be processed instantaneously. At the same time, Cohesity's intelligent backups can get customers' MongoDB databases back online 4X faster than traditional methods. Robust scalability – a scale-out architecture provides petabyte-sized support on a single platform. Customers can reduce their data footprint with global, variable-length deduplication and compression. Enhanced security – immutable write once, read many (WORM) storage, data encryption in flight and at rest, continuous data protection, secure SSL authentication, and a multi-layer defense posture based on Zero Trust security principles offer additional security enhancements. 'As the leading document database for modern applications, MongoDB empowers organizations to build, scale, and innovate faster,' said Benjamin Cefalo, senior vice president of product management, MongoDB. 'Our collaboration with Cohesity reinforces that mission by helping customers protect their data with robust, enterprise-grade resilience—without compromising the agility and performance developers expect from our platform.' 'Cohesity continues to broaden and enhance its support for enterprise workloads, including those with critical customer datasets at petabyte scale,' said Juan Orlandini, chief technology officer for North America, Insight. 'The deepened integration with MongoDB addresses the most pressing data security concerns for large enterprises and financial organizations by delivering the speed, scalability, and security they require.' To learn more about the DataProtect and MongoDB integration, read the Cohesity blog. Cohesity is the leader in AI-powered data security. Over 13,600 enterprise customers, including over 85 of the Fortune 100 and nearly 70% of the Global 500, rely on Cohesity to strengthen their resilience while providing Gen AI insights into their vast amounts of data. Formed from the combination of Cohesity with Veritas' enterprise data protection business, the company's solutions secure and protect data on-premises, in the cloud, and at the edge. Backed by NVIDIA, IBM, HPE, Cisco, AWS, Google Cloud, and others, Cohesity is headquartered in Santa Clara, CA, with offices around the globe. To learn more, follow Cohesity on LinkedIn, X, and Facebook.
Forbes
5 days ago
- Business
- Forbes
Why Measuring Maturity Is Critical To Cyber Resiliency
James Blake is the Vice President of Cyber Resiliency at Cohesity and has over 30 years of experience as a CISO and in incident response. getty I often say that cyber resilience isn't something you can buy—it's an emergent property, the result of an organization taking the appropriate preparatory and operational steps to withstand a cyberattack. I once worked for a CEO whose boilerplate answer to any problem was to back a proverbial truck full of money into it—dumping dollar bills until the issue disappeared. He was used to traditional business continuity and disaster recovery scenarios, and he grew increasingly frustrated when "those cyber guys" couldn't give him a clear answer about how long systems would be down. This CEO was used to disruptions with obvious root causes: natural disaster, equipment failure, power loss or misconfiguration. Recovery in those cases was largely predictable—restoring operations en masse in the same or an alternate environment. You just needed to understand interdependencies and calculate speed—of network, storage and backup. Recovery time objectives (RTOs) and recovery point objectives (RPOs) could be measured and tested. But cyber incidents—especially large-scale destructive ones like ransomware or wiper attacks—are different. Attackers choose from hundreds of techniques across MITRE ATT&CK's 14 tactics. They disable end-point controls using vulnerable device drivers, hide in plain sight with legitimate IT tools, and rapidly weaponize and exploit vulnerabilities through Ransomware-as-a-Service platforms—faster than most organizations can patch. Did the attacker pivot through one machine or 50? If each machine could be a beachhead for reattack, investigation and remediation timelines vary wildly. Recovery could involve patching, configuration rollbacks, new controls, rotating credentials—all of which take time. How much time? Unfortunately, the adversary is often the one in control of that timeline. This lack of definitive timelines makes business leaders uneasy, but it's the reality we live in. Ironically, I've found that organizations with the most rigid RTOs are often the least prepared. They recover too quickly, skip remediation and are just as quickly reinfected or reattacked. Once we clarified the difference between business continuity, disaster recovery and secure cyber recovery, the CEO began to see that the headcount and spending were only part of the solution. What worked better? Planning. Cross-functional collaboration. A phased, pragmatic improvement plan. In short, maturity. Achieving cyber resilience isn't just about deploying the latest-and-greatest technology. It's about operationalizing that technology—building the appropriate workflows, processes and muscle memory so everyone knows their role when the inevitable attack happens. If we want to shorten those unpredictable recovery timelines, resiliency is our best tool. Think of cyber resilience as a chain—made up of technology, people and process. Each link matters. As the saying goes, a chain is only as strong as its weakest link. Any weak point—alert monitoring, threat hunting, vulnerability management, backup protection, digital forensics, incident response, logging, authentication, tabletop exercises, control tuning and threat intelligence—can degrade overall resilience. Yet organizations often launch massive projects to fix just one aspect, while ignoring another that is a dumpster fire. Modest improvements to the weakest link usually yield more value than myopic focus on perfecting a single, siloed initiative. Recent headlines show that organizations with massive cybersecurity budgets still suffer significant impacts from ransomware damage. That should be a wake-up call: It's not just about increasing spending and hiring more people. It's about applying those resources where they will measurably increase cyber resilience. The only way to do that? Step back, measure the relative maturity of each capability in your cyber resilience chain, and keep measuring as you evolve. That is how you avoid discovering—too late—that the chain was always going to break at the weakest link. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Cision Canada
12-06-2025
- Business
- Cision Canada
Milvus 2.6: Built for Scale, Designed to Reduce Costs
REDWOOD SHORES, Calif., June 12, 2025 /CNW/ -- Zilliz, the company behind the open-source vector database Milvus, today announced the release of Milvus 2.6, furthering the company's long-standing mission to democratize AI through accessible and affordable vector data solutions. This latest release continues Zilliz's commitment to helping organizations scale their AI applications without incurring significant costs, delivering substantial cost reductions across storage, compute, operations, and developer effort. "I've long been passionate about democratizing AI by making the underlying technologies more accessible and affordable for everyone," said Charles Xie, CEO at Zilliz. "For over a year, I've emphasized that cost reduction is critical for widespread AI adoption. As data volumes continue to grow exponentially, organizations require vector database solutions that can scale efficiently while maintaining cost control. Milvus 2.6 furthers our commitment to this vision with innovations that streamline infrastructure requirements and optimize resource utilization—all while maintaining the high performance our users expect." Continuing Our Mission: Cost Reduction Across Multiple Dimensions Monetary Savings: Lower Infrastructure & Storage Bills Milvus 2.6 introduces several innovations that directly reduce infrastructure costs: Tiered Storage with Hot/Cold Data Separation: Automatically moves frequently accessed vectors to high-performance storage while relegating less-used data to more economical options—reducing storage costs without compromising retrieval performance. Works seamlessly with leading storage providers including Cohesity, Pure Storage, MinIO, and NetApp. Int8 Vector Compression for HNSW Indexes: Stores dense vectors using 8-bit integers, substantially reducing memory requirements while maintaining search accuracy. RabitQ 1-bit Quantization: Pushes quantization to an extreme to achieve comparable retrieval quality with only half memory cost. New Write-Ahead Log (WAL) with Woodpecker: Eliminates the need for external message queues like Kafka or Pulsar, featuring a diskless architecture that reduces infrastructure costs while improving write performance. Organizations migrating from OpenSearch to Milvus have reported up to 8x cost reductions while maintaining or improving performance for vector search workloads. Operational Efficiencies: Less Infrastructure to Manage Milvus 2.6 simplifies operations with its innovative diskless architecture: Streaming Node: A new dedicated component for real-time data ingestion built directly into the platform—eliminating the need for the costly external message queues on the write path. CDC + BulkInsert: Combines Change Data Capture (CDC) with bulk data ingestion feature, simplifying data replication across instances in different geolocation. Storage v2 Format: Optimized for performance and future compatibility with data processing frameworks like Apache Spark. APT/YUM Deployment: Native package support simplifies installation and upgrades, reducing operational overhead. Developer Time: More Built-In Tools, Less Plumbing Milvus 2.6 boosts developer productivity with: Data-In, Data-Out: Enables direct ingestion of raw content (text, images, audio) with built-in inference capabilities—eliminating separate pre-processing pipelines. Custom Reranker: Allows developers to apply custom scoring logic using scalar fields and user-defined functions. Text & JSON Search: Native support for advanced text processing capabilities including: Advanced tokenization for Asian languages (Japanese/Korean) JSON path/flat/key indexing Match & phrase queries Sampling + Aggregation Queries: Built-in analytics tools for fast iteration during development. Open Source & Trusted by Developers Milvus is fully open source with permissive licensing (Apache 2.0) and developer-friendly architecture. With no black boxes, organizations can contribute, audit, and customize as needed—ensuring transparency and flexibility. Already a Leader in Vector Search Milvus has established itself as one of the most widely adopted vector databases in the world, powering AI applications at scale for more than 10,000 organizations worldwide across a wide range of use cases. "The integration between Milvus 2.6 and MinIO creates a powerful solution for AI workloads requiring both performance and cost efficiency," said Anand Babu Periasamy, CEO and co-founder of MinIO. "By enabling data to be written directly to MinIO instead of using Kafka and supporting hot/cold tiered storage strategies, Milvus 2.6 users can achieve significant infrastructure simplification while maintaining performance for their vector search applications." For more information on Milvus 2.6 and its groundbreaking features, visit the Milvus website and read our blog post. About Zilliz Zilliz is an American SaaS company that builds next-generation vector database technologies, helping organizations unlock the value of unstructured data and rapidly develop AI and machine learning applications. By simplifying complex data infrastructure, Zilliz brings the power of AI within reach for enterprises, teams, and individual developers alike. Zilliz offers a fully managed, multi-cloud vector database service powered by open-source Milvus, supporting major cloud platforms such as AWS, GCP, and Azure, and is available across more than 20 countries and regions. Headquartered in Redwood Shores, California, Zilliz is backed by leading investors including Aramco's Prosperity7 Ventures, Temasek's Pavilion Capital, Hillhouse Capital, 5Y Capital, Yunqi Partners, Trustbridge Partners, and others.
Yahoo
12-06-2025
- Business
- Yahoo
Milvus 2.6: Built for Scale, Designed to Reduce Costs
REDWOOD SHORES, Calif., June 12, 2025 /CNW/ -- Zilliz, the company behind the open-source vector database Milvus, today announced the release of Milvus 2.6, furthering the company's long-standing mission to democratize AI through accessible and affordable vector data solutions. This latest release continues Zilliz's commitment to helping organizations scale their AI applications without incurring significant costs, delivering substantial cost reductions across storage, compute, operations, and developer effort. "I've long been passionate about democratizing AI by making the underlying technologies more accessible and affordable for everyone," said Charles Xie, CEO at Zilliz. "For over a year, I've emphasized that cost reduction is critical for widespread AI adoption. As data volumes continue to grow exponentially, organizations require vector database solutions that can scale efficiently while maintaining cost control. Milvus 2.6 furthers our commitment to this vision with innovations that streamline infrastructure requirements and optimize resource utilization—all while maintaining the high performance our users expect." Continuing Our Mission: Cost Reduction Across Multiple Dimensions Monetary Savings: Lower Infrastructure & Storage Bills Milvus 2.6 introduces several innovations that directly reduce infrastructure costs: Tiered Storage with Hot/Cold Data Separation: Automatically moves frequently accessed vectors to high-performance storage while relegating less-used data to more economical options—reducing storage costs without compromising retrieval performance. Works seamlessly with leading storage providers including Cohesity, Pure Storage, MinIO, and NetApp. Int8 Vector Compression for HNSW Indexes: Stores dense vectors using 8-bit integers, substantially reducing memory requirements while maintaining search accuracy. RabitQ 1-bit Quantization: Pushes quantization to an extreme to achieve comparable retrieval quality with only half memory cost. New Write-Ahead Log (WAL) with Woodpecker: Eliminates the need for external message queues like Kafka or Pulsar, featuring a diskless architecture that reduces infrastructure costs while improving write performance. Organizations migrating from OpenSearch to Milvus have reported up to 8x cost reductions while maintaining or improving performance for vector search workloads. Operational Efficiencies: Less Infrastructure to Manage Milvus 2.6 simplifies operations with its innovative diskless architecture: Streaming Node: A new dedicated component for real-time data ingestion built directly into the platform—eliminating the need for the costly external message queues on the write path. CDC + BulkInsert: Combines Change Data Capture (CDC) with bulk data ingestion feature, simplifying data replication across instances in different geolocation. Storage v2 Format: Optimized for performance and future compatibility with data processing frameworks like Apache Spark. APT/YUM Deployment: Native package support simplifies installation and upgrades, reducing operational overhead. Developer Time: More Built-In Tools, Less Plumbing Milvus 2.6 boosts developer productivity with: Data-In, Data-Out: Enables direct ingestion of raw content (text, images, audio) with built-in inference capabilities—eliminating separate pre-processing pipelines. Custom Reranker: Allows developers to apply custom scoring logic using scalar fields and user-defined functions. Text & JSON Search: Native support for advanced text processing capabilities including: Advanced tokenization for Asian languages (Japanese/Korean) JSON path/flat/key indexing Match & phrase queries Sampling + Aggregation Queries: Built-in analytics tools for fast iteration during development. Open Source & Trusted by Developers Milvus is fully open source with permissive licensing (Apache 2.0) and developer-friendly architecture. With no black boxes, organizations can contribute, audit, and customize as needed—ensuring transparency and flexibility. Already a Leader in Vector Search Milvus has established itself as one of the most widely adopted vector databases in the world, powering AI applications at scale for more than 10,000 organizations worldwide across a wide range of use cases. "The integration between Milvus 2.6 and MinIO creates a powerful solution for AI workloads requiring both performance and cost efficiency," said Anand Babu Periasamy, CEO and co-founder of MinIO. "By enabling data to be written directly to MinIO instead of using Kafka and supporting hot/cold tiered storage strategies, Milvus 2.6 users can achieve significant infrastructure simplification while maintaining performance for their vector search applications." For more information on Milvus 2.6 and its groundbreaking features, visit the Milvus website and read our blog post. About Zilliz Zilliz is an American SaaS company that builds next-generation vector database technologies, helping organizations unlock the value of unstructured data and rapidly develop AI and machine learning applications. By simplifying complex data infrastructure, Zilliz brings the power of AI within reach for enterprises, teams, and individual developers alike. Zilliz offers a fully managed, multi-cloud vector database service powered by open-source Milvus, supporting major cloud platforms such as AWS, GCP, and Azure, and is available across more than 20 countries and regions. Headquartered in Redwood Shores, California, Zilliz is backed by leading investors including Aramco's Prosperity7 Ventures, Temasek's Pavilion Capital, Hillhouse Capital, 5Y Capital, Yunqi Partners, Trustbridge Partners, and others. 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