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Nobl9 Introduces Replay

Nobl9 announced Replay, providing new capabilities to its Service Level Objective (SLO) platform that significantly increase and optimize how enterprises can deploy, use and benefit from SLOs.

The new features allow customers to create SLOs with historical data from the last 30 days and pre-calculates error budgets.

“Our customers run mission-critical platforms that let enterprise developers quickly deliver software applications. Nobl9 makes the handshake-agreement between platform teams and application teams explicit in code,” said Brian Singer, co-founder and CPO at Nobl9. “Now with Replay, our customers can ask ‘what if we had set different goals for our service?’ and see the results based on accurate past data so they don’t have to guess or wait for new data to accumulate.”

With Replay, customers can instantly view their last 30 days of reliability data in context when they create their SLOs. Their historical Service Level Indicator (SLI) data will be retrieved alongside real time observability metrics and merged together to see an error budget within the time window they select.

Some roadblocks for customers starting with SLOs are knowing how they did previously and what their realistic expectations should be for reliability. Replay provides a window into that by retrieving and graphing their reliability up to 30 days before the creation of the SLO. Customers can then immediately see if their historical data met the reliability criteria they set and see what error budget is remaining once their SLO is created.

Instead of exporting data and reviewing the last 30 days of metrics in a spreadsheet, with Replay, Nobl9 enables automation to capture historical data, merge them with real time SLI data, and allows customers to view SLO charts and generate reports shortly after creating an SLO.

Replay supports Datadog, Prometheus, Amazon Managed Prometheus, Splunk, and Graphite metrics. Additional data sources will be added soon.

Replay is now available in beta to all existing Nobl9 customers.

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Nobl9 Introduces Replay

Nobl9 announced Replay, providing new capabilities to its Service Level Objective (SLO) platform that significantly increase and optimize how enterprises can deploy, use and benefit from SLOs.

The new features allow customers to create SLOs with historical data from the last 30 days and pre-calculates error budgets.

“Our customers run mission-critical platforms that let enterprise developers quickly deliver software applications. Nobl9 makes the handshake-agreement between platform teams and application teams explicit in code,” said Brian Singer, co-founder and CPO at Nobl9. “Now with Replay, our customers can ask ‘what if we had set different goals for our service?’ and see the results based on accurate past data so they don’t have to guess or wait for new data to accumulate.”

With Replay, customers can instantly view their last 30 days of reliability data in context when they create their SLOs. Their historical Service Level Indicator (SLI) data will be retrieved alongside real time observability metrics and merged together to see an error budget within the time window they select.

Some roadblocks for customers starting with SLOs are knowing how they did previously and what their realistic expectations should be for reliability. Replay provides a window into that by retrieving and graphing their reliability up to 30 days before the creation of the SLO. Customers can then immediately see if their historical data met the reliability criteria they set and see what error budget is remaining once their SLO is created.

Instead of exporting data and reviewing the last 30 days of metrics in a spreadsheet, with Replay, Nobl9 enables automation to capture historical data, merge them with real time SLI data, and allows customers to view SLO charts and generate reports shortly after creating an SLO.

Replay supports Datadog, Prometheus, Amazon Managed Prometheus, Splunk, and Graphite metrics. Additional data sources will be added soon.

Replay is now available in beta to all existing Nobl9 customers.

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

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