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Pepperdata Introduces Query Spotlight

Pepperdata announced the availability of Query Spotlight, a new product in their big data analytics performance suite.

Query Spotlight makes it easy for operators and developers to understand the detailed performance characteristics of their query workloads together with infrastructure-wide issues that impact these workloads. With this new functionality, query workloads can be tuned, debugged and optimized for better performance and reduced costs, both in the cloud and on premises.

Query Spotlight simultaneously provides detailed information on query resource utilization, along with detailed database views. Query Spotlight details execution plan skew, poorly optimized queries and historical runtime variance so operations teams can remediate issues as they arise. Query Spotlight also highlights hot partitions, outdated table statistics, and other system and storage issues.

Enterprise customers report that queries are a significant portion of their analytics workloads, and the performance of these workloads is critical. Query Spotlight lets IT and applications teams easily identify the most expensive queries and quickly optimize their performance.

Query Spotlight provides a 360-degree view, enabling visibility between executing queries and the database tables they access.

Benefits include:

- Detailed visibility into query workloads, including delayed and most expensive queries as well as wasted CPU and memory queries, enabling root cause analysis

- A single dashboard for a time-correlated view of your query, database and infrastructure metrics to better understand and manage utilization and performance, including query run summaries, tables accessed and query plans mapped to execution stages

- Faster problem resolution through improved visibility and immediate feedback through real-time metrics

- On premises and cloud resource monitoring, with detailed query cost information

- Support for Hive, IBM BigSQL, Redshift and Snowflake

"Queries represent more than 50% of the analytics workloads in today's big data environments. Working closely with our customers we have identified key issues they face with queries in their deployments. We are confident that Query Spotlight solves these pain points. Query Spotlight provides targeted insight into query execution, giving customers the answers they need to dramatically increase performance and rapidly decrease costs," said Ash Munshi, CEO, Pepperdata.

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Pepperdata Introduces Query Spotlight

Pepperdata announced the availability of Query Spotlight, a new product in their big data analytics performance suite.

Query Spotlight makes it easy for operators and developers to understand the detailed performance characteristics of their query workloads together with infrastructure-wide issues that impact these workloads. With this new functionality, query workloads can be tuned, debugged and optimized for better performance and reduced costs, both in the cloud and on premises.

Query Spotlight simultaneously provides detailed information on query resource utilization, along with detailed database views. Query Spotlight details execution plan skew, poorly optimized queries and historical runtime variance so operations teams can remediate issues as they arise. Query Spotlight also highlights hot partitions, outdated table statistics, and other system and storage issues.

Enterprise customers report that queries are a significant portion of their analytics workloads, and the performance of these workloads is critical. Query Spotlight lets IT and applications teams easily identify the most expensive queries and quickly optimize their performance.

Query Spotlight provides a 360-degree view, enabling visibility between executing queries and the database tables they access.

Benefits include:

- Detailed visibility into query workloads, including delayed and most expensive queries as well as wasted CPU and memory queries, enabling root cause analysis

- A single dashboard for a time-correlated view of your query, database and infrastructure metrics to better understand and manage utilization and performance, including query run summaries, tables accessed and query plans mapped to execution stages

- Faster problem resolution through improved visibility and immediate feedback through real-time metrics

- On premises and cloud resource monitoring, with detailed query cost information

- Support for Hive, IBM BigSQL, Redshift and Snowflake

"Queries represent more than 50% of the analytics workloads in today's big data environments. Working closely with our customers we have identified key issues they face with queries in their deployments. We are confident that Query Spotlight solves these pain points. Query Spotlight provides targeted insight into query execution, giving customers the answers they need to dramatically increase performance and rapidly decrease costs," said Ash Munshi, CEO, Pepperdata.

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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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...