Data Analytics News: Nscale Snaps Up Anyscale for $1.65B

The data analytics news cycle closes out July with a flurry of significant moves: a nine-figure acquisition reshaping the distributed AI compute landscape, a general availability milestone from Snowflake, and a string of open-source and governance releases that will keep data teams busy heading into August. Here is everything data analysts, data engineers, and analytics leaders need to know from today’s data analytics news.

Nscale Acquires Anyscale in $1.65 Billion Deal

In the biggest M&A story of the day, AI infrastructure company Nscale agreed to acquire Anyscale for approximately $1.65 billion, as reported by TechCrunch on July 30. Anyscale built and commercialised Ray, the open-source framework that underpins distributed data processing, model training, and AI inference workloads across thousands of GPUs — making it a foundational piece of the modern data engineering stack. The deal brings Nscale’s GPU infrastructure together with Anyscale’s software layer, with Anyscale’s roughly 200-person team joining the combined company. Ray itself remains open source under the PyTorch Foundation, so teams running distributed data pipelines on Ray should not expect any licensing disruptions.

Snowflake Cortex Code AI Suggestions Reach General Availability

Snowflake moved its Cortex Code AI suggestions in Workspaces from preview to general availability on July 28, giving data engineers and analysts inline AI-powered code completions directly within Snowsight notebooks and SQL scripts. The release also standardised the keybinding experience: pressing Tab now accepts an AI suggestion, while the Enter key handles deterministic catalog completions, resolving a friction point that had frustrated users during the preview period. For teams that write their transformations inside Snowflake, this removes the need to jump to an external IDE for AI-assisted coding. If you are building those transformation layers from scratch, our lesson on sorting and deduplicating results in SQL covers the query patterns you will be asking Cortex Code to help you write.

dbt Core v2.0 Advances Toward Stable Release on Shared Rust Runtime

dbt Labs shipped dbt Core v2.0.0-alpha.5 in mid-July, continuing a steady cadence of pre-release milestones since the first alpha launched on June 1. The headline architectural change is that a large portion of the Rust code from the proprietary dbt Fusion engine has been open-sourced and now serves as the shared runtime for both Core and Fusion — meaning open-source users gain direct access to the same high-performance foundation rather than waiting for Python ports. Version 2.0 also introduces strict configuration validation that catches typos at parse time instead of silently ignoring them, and it can emit compact Parquet artifacts as a lightweight alternative to the large manifest.json files that slow down CI pipelines. Teams managing complex data analytics pipelines built on dbt should track the alpha releases closely. For a refresher on the filtering logic that dbt models express in SQL, see our guide to advanced filtering with IN, BETWEEN, and LIKE.

lakeFS Summer 2026 Release Delivers AI Governance by Design

Open-source data versioning platform lakeFS shipped its Summer 2026 release with a focus on enterprise AI governance, landing just days before the EU AI Act’s first enforcement deadline on August 2. The headline feature is Datasets as a semantic layer — immutable, versioned, and shareable logical sets of data that give AI agents and analytics teams a stable, governed interface without exposing the underlying physical storage location. Other highlights include multi-tenancy support for separating business units or regulatory domains within a single lakeFS installation, and automated branch and data retention management to streamline compliance audit workflows. For analytics teams operating under GDPR or preparing for EU AI Act obligations, this is a meaningful step toward audit-ready data infrastructure.

Anaconda Acquires Kilo Code to Close the AI Production Gap

Earlier this month, Anaconda acquired Kilo Code on July 15, an open-source, model-agnostic agentic engineering platform used by more than 3 million developers across VS Code, JetBrains, the web, and the CLI. Kilo routes nearly ten trillion tokens per month across more than 500 AI models, acting as a model-agnostic gateway that prevents lock-in to any single AI provider. The strategic intent is to close the loop between data science experimentation — Anaconda’s home territory — and production deployment, addressing the widely cited finding that roughly 80% of enterprise AI projects never reach production. The acquisition builds on Anaconda’s earlier purchase of Outerbounds, which brought production-grade AI orchestration to the Anaconda Platform.

Supermetrics Studio Turns AI-Built Dashboards Into Live Governed Reports

Marketing analytics platform Supermetrics launched Supermetrics Studio in July, a new workspace that ingests AI-generated dashboards from tools like Claude and ChatGPT and powers them with live data from existing Supermetrics connectors. Users describe a dashboard in plain text or upload a screenshot of a report they already have, and Studio builds a governed, versioned version connected to live marketing data. The July release also added write-back campaign management across Meta, Google, Microsoft, OpenAI, and TikTok, and brought Adobe Analytics into general availability as a premium connector. For marketing and product analytics teams, it is one of the more practical integrations between conversational AI and production reporting to arrive in the platform this year.

That rounds out today’s data analytics news. The Nscale-Anyscale deal is the clear headline, but the quieter developments — Snowflake’s Cortex Code GA, dbt v2.0’s architectural shift to a shared Rust runtime, and lakeFS’s governance release ahead of the EU AI Act deadline — point to a broader maturation of the tools data teams rely on daily. We will be back tomorrow with the next edition. In the meantime, explore the full SQL for Data Analysis series for the foundational skills that underpin all of this tooling.