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Corvil Integrates with Cloudera

Corvil announced a data integration with Cloudera, a provider of enterprise big data management, powered by Apache Hadoop.

Joint customers may now stream Corvil data continuously and at scale into Cloudera Enterprise, where it can be processed, analyzed, modeled and served using a variety of engines that are part of Cloudera data management platform.

Today’s collaboration was motivated by requests from companies in the financial services sector that are seeking to address the escalating burden of governance, risk, and compliance in an increasingly regulated and scrutinised financial industry.

"Understanding what’s happening with your applications is critical, and ESG research shows that IT operations analytics is a top use case for real-time big data," said Nik Rouda, Senior Analyst at Enterprise Strategy Group. "Corvil teaming up with Cloudera brings this power to the leading Hadoop-based enterprise big data management platform."

"Legacy risk, compliance, and governance systems are typically ‘T+1’, meaning they only provide reports and findings at the end of the trading day or the next day," said Donal O’Sullivan, VP Product Management at Corvil. "In today’s hyper-speed markets, a business can build up an unsustainable position, find itself unable to reconcile trades carried out on behalf of a client or fail to recognize that a software process has inadvertently activated and is trading on the open market without supervision, all in a matter of a second. A new era of real-time systems is needed to address this new reality."

Due to broad industry regulations, modern financial institutions must be able to seamlessly capture, extract, transform, load (ETL), and stream high-fidelity business data in real-time to enable business analytics and data inquiry to be done in a timely, trusted and forensically verifiable manner. By tapping into data directly from the network, Corvil can extract raw packet data and transform it into enriched and normalized business meta data in real-time. Corvil data contains a forensically verifiable record of all machine to machine interactions translated into business context with nanosecond precision timestamps for all event records, making the company the analytics solution of choice within Financial Markets.

"Addressing customer demands for real-time analytics and ingesting high value data sources helps our joint customers maximize the value of the Cloudera platform and deliver on the governance, compliance and risk requirements of the financial industry," said Tim Stevens, VP of Business and Corporate Development at Cloudera.

Corvil streams data from distributed ETL nodes at rates up to millions of events per second into Cloudera Enterprise using newly developed Kafka and Flume connectors. With this data in Cloudera, analysts can easily construct real-time, interactive and/or batch jobs against the Corvil data on-demand using the latest Apache tools such as Hadoop, HBase, and Spark. The new Corvil connectors for Kafka and Flume have been tested and certified by Cloudera. These connectors are now generally available from Corvil at no additional charge. Customers can also easily visualize the Corvil data and analytical insights using tools such as Tableau and Qlik.

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

Corvil Integrates with Cloudera

Corvil announced a data integration with Cloudera, a provider of enterprise big data management, powered by Apache Hadoop.

Joint customers may now stream Corvil data continuously and at scale into Cloudera Enterprise, where it can be processed, analyzed, modeled and served using a variety of engines that are part of Cloudera data management platform.

Today’s collaboration was motivated by requests from companies in the financial services sector that are seeking to address the escalating burden of governance, risk, and compliance in an increasingly regulated and scrutinised financial industry.

"Understanding what’s happening with your applications is critical, and ESG research shows that IT operations analytics is a top use case for real-time big data," said Nik Rouda, Senior Analyst at Enterprise Strategy Group. "Corvil teaming up with Cloudera brings this power to the leading Hadoop-based enterprise big data management platform."

"Legacy risk, compliance, and governance systems are typically ‘T+1’, meaning they only provide reports and findings at the end of the trading day or the next day," said Donal O’Sullivan, VP Product Management at Corvil. "In today’s hyper-speed markets, a business can build up an unsustainable position, find itself unable to reconcile trades carried out on behalf of a client or fail to recognize that a software process has inadvertently activated and is trading on the open market without supervision, all in a matter of a second. A new era of real-time systems is needed to address this new reality."

Due to broad industry regulations, modern financial institutions must be able to seamlessly capture, extract, transform, load (ETL), and stream high-fidelity business data in real-time to enable business analytics and data inquiry to be done in a timely, trusted and forensically verifiable manner. By tapping into data directly from the network, Corvil can extract raw packet data and transform it into enriched and normalized business meta data in real-time. Corvil data contains a forensically verifiable record of all machine to machine interactions translated into business context with nanosecond precision timestamps for all event records, making the company the analytics solution of choice within Financial Markets.

"Addressing customer demands for real-time analytics and ingesting high value data sources helps our joint customers maximize the value of the Cloudera platform and deliver on the governance, compliance and risk requirements of the financial industry," said Tim Stevens, VP of Business and Corporate Development at Cloudera.

Corvil streams data from distributed ETL nodes at rates up to millions of events per second into Cloudera Enterprise using newly developed Kafka and Flume connectors. With this data in Cloudera, analysts can easily construct real-time, interactive and/or batch jobs against the Corvil data on-demand using the latest Apache tools such as Hadoop, HBase, and Spark. The new Corvil connectors for Kafka and Flume have been tested and certified by Cloudera. These connectors are now generally available from Corvil at no additional charge. Customers can also easily visualize the Corvil data and analytical insights using tools such as Tableau and Qlik.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.