Rockset now supports real-time data ingestion from Azure Blob Storage, Azure Event Hubs, and Azure Service Bus.
Customers can ingest, transform and analyze real-time data from their Azure data lake or event stream without the need for complex real-time data pipelines.
With an extensive library of built-in data connectors for schemaless ingestion from existing event streams, databases and lakes, Rockset stays in sync with the data source, enabling fast analytics within 1-2 seconds of new data being generated. As the data arrives, Rockset supports continuous SQL transformations and rollups without the need for batch ETL jobs.
Unlike traditional warehouses that use columnar stores, Rockset indexes every field for sub-second search, aggregations and joins. As a result, it frees developers from rigid data pipelines, schema definition, denormalization, deduplication, and query performance tuning, replacing it with real-time analytics at cloud scale, without the cost and complexity. With this new release, Azure customers can sync their data using fully managed connectors to popular Azure and Microsoft services, including Azure Blob Storage, Azure Event Hubs, and Azure Service Bus.
“Companies are recognizing that they cannot build a data-driven culture relying on batch-based analytics and BI alone. There is too much latency at every step — finding the data, ingesting it, querying it and representing it. In an age of lightning-fast consumer apps such as Instagram, users won’t tolerate excruciatingly slow analytics experiences. Not your customers, nor even your internal employees,” said Venkat Venkataramani, co-founder and CEO at Rockset. “Azure has a strong public cloud presence, and with this release we are making real-time analytics more accessible and affordable for all Azure customers.”
Rockset already integrates with multiple data lakes, event streams and transactional databases within Amazon Web Services (AWS) and Google Cloud Platform (GCP). With today’s launch, it’s now easier than ever for Azure customers to access real-time analytics. Rockset offers built-in connectors that are fully managed as part of its cloud platform, obviating the need for users to build and manage complicated data pipelines or use a separate ETL tool, and enabling real-time search, aggregations and joins across disparate data from multiple sources.
The Latest
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 ...
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 ...