DataStrike Expands Snowflake Managed Services as Cloud Data Platform Adoption Grows

DataStrike, a leader in data infrastructure managed services, today announced the expansion of its Snowflake managed services practice to meet growing demand from mid-market organizations running production workloads on the platform. As companies continue moving critical data infrastructure to Snowflake and prepare their environments for AI-driven use cases, DataStrike is seeing increased demand for hands-on expertise around cost governance, pipeline reliability, performance management and ongoing platform operations.

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Snowflake has become a foundational platform for organizations consolidating their data estates, and for most mid-market IT teams, the real complexity shows up after go-live, when warehouse sprawl, expensive queries, unmonitored pipelines and runaway storage costs start affecting the business. Many organizations do not have dedicated Snowflake admin on staff, and generic monitoring tools are not built to fill that role.

“Snowflake is a powerful platform, but it takes real attention to get the most out of it. Credit consumption can escalate quickly, pipelines fail in ways that aren’t always visible, and without someone watching it around the clock, those problems tend to compound,” said Rob Brown, President and COO of DataStrike. “We’ve built our practice around owning that responsibility for our clients, whether they are stabilizing a production environment, recovering from credit drift, or building out the data foundation they need for AI.”

DataStrike’s Snowflake managed services cover the full operational picture, from cost governance and pipeline reliability to performance management and architectural readiness, with senior U.S.-based engineers available around the clock to handle both the alerting and the resolution. Core services include:

Cost and Consumption Management: Monitors credit utilization and warehouse activity, identifies idle or oversized resources, and catches cost issues before they become budget problems.

Pipeline Monitoring: Covers Snowpipe jobs, task scheduling, data flow latency and failure detection across ingestion and transformation processes.

Performance Management: Watches for slow and failed queries, long queue times and large table scans, with senior-level expertise to act on what the monitoring surfaces.

Architectural Assessments: Evaluates new and existing Snowflake environments against best practices, surfacing opportunities to reduce cost, improve reliability and prepare the data layer for what comes next.

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The expansion reflects DataStrike’s broader growth across specialized data platforms. Earlier this year, the company announced a 600 percent increase in PostgreSQL managed services engagements as enterprise demand for open-source database expertise accelerated, and that Postgres momentum is now directly feeding Snowflake adoption among its client bases.

Many of those same clients are moving into Snowflake environments, and the demand pattern tracks with what analysts are seeing across the industry. In a recent CIO.com article, IDC AI and automation analyst Devin Pratt describes Snowflake as part of a broader industry push to offer “that unified data platform for their end user to make it as easy as possible to use for complex tasks, like AI.” As more organizations build out AI infrastructure on top of their Snowflake environments, the data layer underneath it is getting more scrutiny than it used to, and the managed services market around Snowflake is growing alongside that demand.

To support clients across that full lifecycle, DataStrike recently announced a strategic partnership with Brainforge, a premier Snowflake partner, extending its ability to cover everything from initial implementation and AI readiness through to ongoing managed services.

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