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Pepperdata Query Spotlight Supports Apache Impala

Pepperdata expanded the capabilities of Query Spotlight, a recent product in its big data performance suite, to support Apache Impala—one of the leading players in interactive SQL query engines.

Query Spotlight makes it easier for operators and developers to understand the detailed performance characteristics of their query workloads together with related infrastructure-wide issues. The product allows query workloads to be tuned, debugged and optimized for better performance and reduced costs in the cloud and on premises. Query Spotlight for Impala displays detailed stats, query plan, breakup of the query duration by every step and also provides visibility into databases and tables. The recommendation engine includes system level recommendations as well as query level recommendations—joins included.

Query Spotlight is deployed and trusted by Fortune 500 companies. 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 holistic view, enabling visibility between executing queries and the database tables they access. With added support for Impala queries, benefits include:

- Visibility into all your Hive and Impala queries in one place in a similar format

- Recommendations to improve the performance of your queries

- Comparison of query runs with chargeback reports

“Queries are a significant portion of our customer’s big data workloads, so we know the performance of these workloads is critical. IT and applications teams can now get visibility into their Hive and Impala queries in one place, compare the runs of their queries and take advantage of the recommendations Query Spotlight provides,” says Ash Munshi, CEO, Pepperdata. “We’re confident Query Spotlight can increase the performance of their Impala queries while helping them decrease overall costs.”

The product is available now. New query types are forthcoming.

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Pepperdata Query Spotlight Supports Apache Impala

Pepperdata expanded the capabilities of Query Spotlight, a recent product in its big data performance suite, to support Apache Impala—one of the leading players in interactive SQL query engines.

Query Spotlight makes it easier for operators and developers to understand the detailed performance characteristics of their query workloads together with related infrastructure-wide issues. The product allows query workloads to be tuned, debugged and optimized for better performance and reduced costs in the cloud and on premises. Query Spotlight for Impala displays detailed stats, query plan, breakup of the query duration by every step and also provides visibility into databases and tables. The recommendation engine includes system level recommendations as well as query level recommendations—joins included.

Query Spotlight is deployed and trusted by Fortune 500 companies. 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 holistic view, enabling visibility between executing queries and the database tables they access. With added support for Impala queries, benefits include:

- Visibility into all your Hive and Impala queries in one place in a similar format

- Recommendations to improve the performance of your queries

- Comparison of query runs with chargeback reports

“Queries are a significant portion of our customer’s big data workloads, so we know the performance of these workloads is critical. IT and applications teams can now get visibility into their Hive and Impala queries in one place, compare the runs of their queries and take advantage of the recommendations Query Spotlight provides,” says Ash Munshi, CEO, Pepperdata. “We’re confident Query Spotlight can increase the performance of their Impala queries while helping them decrease overall costs.”

The product is available now. New query types are forthcoming.

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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 ...