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Era Software Releases EraStreams

Era Software announced the private beta version of EraStreams, a no-code data pipeline that lets users integrate, transform, and route observability data to EraSearch, the company's petabyte-scale log management platform, and third-party monitoring tools.

Era Software is broadening the scope of its log management solution and fitting into more of the observability stack. With a time series database and object storage under the hood, the company's approach to observability data management resolves scale, performance, and cost issues associated with running applications on modern architectures, including cloud, containers, and microservices.

EraStreams complements EraSearch to optimize cost and performance and integrates into existing DevOps workflows and tools to help teams manage observability costs and improve troubleshooting effectiveness. As a result, IT and security teams can continue to use monitoring tools they rely on while controlling the volume of data that gets routed to these tools to optimize data usage and cost efficiency. Teams also have the option to route any logs to EraSearch for low-cost storage and fast, petabyte-scale query.

"Our vision is to be the observability data management choice for organizations dealing with massive volumes of observability data," said Todd Persen, Co-founder and CEO, Era Software. "The unveiling of EraStreams today advances this vision with a data pipeline to help you manage observability costs and improve troubleshooting effectiveness. In addition, it gives you real-time insights into application and system performance and adds another component to our scalable, cost-effective observability data management."

For security teams with high log management costs and performance challenges using expensive SIEM solutions, EraStreams transforms and routes optimized datasets to a SIEM for security analytics and offers an option to send raw data to EraSearch for cost-efficient log management. If personally identifiable information (PII) poses a security risk, teams can protect sensitive data by masking PII before writing it to data storage. Data managed in EraSearch can be efficiently retrieved through its observability data rehydration capability when needed for investigations or threat hunting.

EraStreams was designed with ease of use and reliability to help teams see how observability data flows through pipelines and manage data at scale. Challenges with data flow stem from system failures and data intake variation. EraStreams better handles failure modes and pipeline changes to minimize data loss with dynamic backpressure management and reconfigurations. In addition, EraStreams provides a powerful set of features that offer multiple ways to reduce observability costs. When used with EraSearch, EraStreams reduces the total cost of ownership for existing log management solutions while preserving historical information in EraSearch for low-cost object storage and fast search and query.

"Today, some companies may generate over 100 terabytes of log data per day, and scale and pricing prevents many organizations from ingesting more data," added Persen. "With EraSearch and EraStreams, we can help you manage high volumes of data at a lower cost per GB ingested – we give you the ability to ingest and make the data queryable in real time. You should be able to find a needle in the haystack. With EraSearch and EraStreams, you can economically ingest a petabyte of log data daily with an average response time of less than 500 milliseconds."

Some of the common use cases EraStreams supports include:

- Log management cost reduction

- Troubleshooting

- Compliance and risk management

- Data integration

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Era Software Releases EraStreams

Era Software announced the private beta version of EraStreams, a no-code data pipeline that lets users integrate, transform, and route observability data to EraSearch, the company's petabyte-scale log management platform, and third-party monitoring tools.

Era Software is broadening the scope of its log management solution and fitting into more of the observability stack. With a time series database and object storage under the hood, the company's approach to observability data management resolves scale, performance, and cost issues associated with running applications on modern architectures, including cloud, containers, and microservices.

EraStreams complements EraSearch to optimize cost and performance and integrates into existing DevOps workflows and tools to help teams manage observability costs and improve troubleshooting effectiveness. As a result, IT and security teams can continue to use monitoring tools they rely on while controlling the volume of data that gets routed to these tools to optimize data usage and cost efficiency. Teams also have the option to route any logs to EraSearch for low-cost storage and fast, petabyte-scale query.

"Our vision is to be the observability data management choice for organizations dealing with massive volumes of observability data," said Todd Persen, Co-founder and CEO, Era Software. "The unveiling of EraStreams today advances this vision with a data pipeline to help you manage observability costs and improve troubleshooting effectiveness. In addition, it gives you real-time insights into application and system performance and adds another component to our scalable, cost-effective observability data management."

For security teams with high log management costs and performance challenges using expensive SIEM solutions, EraStreams transforms and routes optimized datasets to a SIEM for security analytics and offers an option to send raw data to EraSearch for cost-efficient log management. If personally identifiable information (PII) poses a security risk, teams can protect sensitive data by masking PII before writing it to data storage. Data managed in EraSearch can be efficiently retrieved through its observability data rehydration capability when needed for investigations or threat hunting.

EraStreams was designed with ease of use and reliability to help teams see how observability data flows through pipelines and manage data at scale. Challenges with data flow stem from system failures and data intake variation. EraStreams better handles failure modes and pipeline changes to minimize data loss with dynamic backpressure management and reconfigurations. In addition, EraStreams provides a powerful set of features that offer multiple ways to reduce observability costs. When used with EraSearch, EraStreams reduces the total cost of ownership for existing log management solutions while preserving historical information in EraSearch for low-cost object storage and fast search and query.

"Today, some companies may generate over 100 terabytes of log data per day, and scale and pricing prevents many organizations from ingesting more data," added Persen. "With EraSearch and EraStreams, we can help you manage high volumes of data at a lower cost per GB ingested – we give you the ability to ingest and make the data queryable in real time. You should be able to find a needle in the haystack. With EraSearch and EraStreams, you can economically ingest a petabyte of log data daily with an average response time of less than 500 milliseconds."

Some of the common use cases EraStreams supports include:

- Log management cost reduction

- Troubleshooting

- Compliance and risk management

- Data integration

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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...

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