Glassnode on Snowflake: Digital Asset Data Delivered Directly to your Warehouse

insights.glassnodePublished on 2026-06-04Last updated on 2026-06-04

Abstract

Glassnode has launched a data sharing environment on Snowflake, making comprehensive on-chain and market data directly accessible within institutional data warehouses. This eliminates the need for custom API pipelines and ETL management. The offering includes on-chain analytics, derivatives, spot & exchange data, ETF & corporate treasury metrics, with multiple time resolutions. A key feature is point-in-time (PiT) data to ensure historical accuracy and eliminate look-ahead bias in backtesting. Designed for quantitative trading, risk management, and macro research, the integration allows users to query data via SQL alongside other datasets. Access is provisioned through private Snowflake Marketplace listings.

The most sophisticated institutional teams don't just need better data. They need it inside the environments where their research and execution workflows already run.

We've launched Glassnode's Snowflake Data Sharing environment, becoming the first provider to bring comprehensive on-chain analytics into the Snowflake ecosystem.

To get started or learn more about Glassnode Data Shares, talk to our product experts.

Request access

Access Trusted Digital Asset Data Directly in Your Environment

Snowflake is the data warehouse of choice for institutional finance. But until now, integrating digital asset data into these workflows meant building custom API ingestion pipelines, managing ETL jobs, and reconciling data updates. That's engineering overhead that should be spent on alpha generation.

Our Snowflake integration eliminates all of it. Through Snowflake Marketplace private listings, our data shares deliver the full history of every trusted Glassnode metric directly into your environment.

You query it like any other table in your warehouse, because that's exactly what it is.

What's Included

This is the same data that powers the research workflows of the world's leading crypto-native institutions, now accessible without a single API call.

On-Chain Analytics | Address activity, entity behavior, supply dynamics, exchange flows, miner metrics, and advanced clustering-based insights across Bitcoin, Ethereum, and beyond.

Derivatives | Futures open interest, funding rates, liquidations, plus our recently expanded options suite: premiums, taker flows, combo strategies, implied volatility surfaces, and more.

Spot & Exchange Data | Exchange balances, inflow/outflow dynamics, and venue-level breakdowns showing capital rotation in real time.

ETFs & Corporate Treasuries | Bitcoin and Ethereum ETF flows, AUM dynamics, and corporate treasury holdings.

Multiple Resolutions | 10-minute, hourly, and daily granularity for everything from intraday signals to long-horizon macro research.

Built For The Workflows You Run

Quantitative & Systematic Trading | Query the full depth of on-chain, derivatives, and market data in SQL alongside your proprietary signals. PiT variants for backtesting fidelity. Sub-hourly resolution for intraday signals. No rate limits, no pagination.

Risk & Portfolio Construction | A unified view of exchange concentration, leverage dynamics, ETF flows, and supply overhangs. Native Snowflake delivery means direct integration with existing risk dashboards.

Multi-Strategy & Macro Research | Join Glassnode data with equity, fixed income, and macro datasets already in your warehouse. Same query layer, no middleware.

Fund Operations & Compliance | No bespoke pipelines means less operational risk. Snowflake's access controls and audit logging handle governance out of the box. Point-in-time timestamps provide data lineage for regulatory requirements and internal governance.

Eliminate Look-Ahead Bias from Backtests

For quantitative teams, historical data integrity is non-negotiable. Backtesting on retroactively revised data isn't backtesting. It's overfitting.

Glassnode is the first to offer point-in-time (PiT) blockchain data in Snowflake. PiT metrics are append-only and historically immutable. Each data point reflects exactly what was known when it was computed. No retroactive corrections, no look-ahead bias.

This matters because on-chain data is inherently mutable. Clustering improvements, late-reported exchange data, and refined labeling can all trigger revisions to standard metrics. PiT variants freeze the record, so your backtests reflect the information that was actually available to participants at each point in time. Every PiT data point includes a computed_at timestamp for full auditability.

Getting Started

Whether you're building backtested systematic models, integrating crypto into a cross-asset framework, or standing up institutional-grade risk infrastructure, Glassnode on Snowflake is the fastest path from blockchain data to production.

To request a trial or to learn more about Glassnode Data Shares, reach out to our institutional team at sales@glassnode.com.

The setup for Glassnode on Snowflake

  1. Retrieve your Snowflake account identifier via Snowflake's standard process or a simple SQL query.
  2. Share it with the Glassnode team. We provision access through Snowflake Marketplace private listings.
  3. Accept the listing. After initial replication, the data is live in your warehouse.

Data shares are organized by package (on-chain, market, signals, common, metadata), so you subscribe to what you need. Updates flow automatically.

Alternatively, you can initiate a trial via the Snowflake listing.

i️
Find the full setup documentation in Glassnode docs.

Glassnode Delivers the Analytical Depth That Drives Alpha

Coverage alone doesn't differentiate. What sets us apart is analytical depth built over nearly a decade of institutional-grade data engineering.

Proprietary entity adjustment | Our clustering technology identifies real-world entities (exchanges, institutions, long-term holders, miners) rather than raw addresses. Noisy blockchain data becomes actionable intelligence.

Full-stack derivatives | On-chain, futures, options, and spot from a single provider with unified methodology and consistent quality.

Expanding coverage | New chains, instruments, and products get added continuously. Your Snowflake environment reflects every update automatically.

* Default historical data during the trial is limited to 14 days and 1h/24h resolutions - contact sales@glassnode.com to request a trial with higher resolution and extended history.


  • Follow us on X for timely market updates and analysis
  • Join our Telegram channel for regular market insights
  • For on-chain metrics, dashboards, and alerts, visit Glassnode Studio

Related Questions

QWhat problem does the Glassnode Snowflake Data Sharing environment aim to solve for institutional teams?

AIt aims to eliminate the engineering overhead of building custom API ingestion pipelines, managing ETL jobs, and reconciling data updates, allowing institutional teams to access comprehensive on-chain and market data directly within their Snowflake data warehouse for seamless integration into their existing research and execution workflows.

QAccording to the article, what is a key feature of Glassnode's data offering on Snowflake that is specifically important for quantitative backtesting?

AA key feature is the provision of point-in-time (PiT) blockchain data, which is append-only and historically immutable. This eliminates look-ahead bias by ensuring each data point reflects exactly what was known at the time it was computed, with no retroactive corrections.

QWhat types of data categories are included in the Glassnode Data Shares accessible via Snowflake?

AThe data categories include: On-Chain Analytics (address activity, entity behavior, supply dynamics, etc.), Derivatives (futures, options metrics), Spot & Exchange Data (exchange balances, flows), ETFs & Corporate Treasuries data, and data at Multiple Resolutions (10-minute, hourly, daily).

QWhat are the primary institutional use cases or workflows mentioned as being supported by Glassnode on Snowflake?

AThe primary use cases are: Quantitative & Systematic Trading, Risk & Portfolio Construction, Multi-Strategy & Macro Research, and Fund Operations & Compliance. It supports these by allowing direct SQL querying alongside other datasets, providing unified views, and integrating with existing dashboards and governance controls.

QWhat are the initial steps required to set up and access Glassnode Data Shares through Snowflake?

AThe steps are: 1. Retrieve your Snowflake account identifier. 2. Share it with the Glassnode team, who will provision access via Snowflake Marketplace private listings. 3. Accept the listing, after which the data will be live in your warehouse after initial replication. Alternatively, a trial can be initiated directly via the Snowflake listing.

Related Reads

STAR 50 Soars 10.73%, Why Did A-Shares Stage a "V-Shaped Reversal"?

After a prolonged decline, the Chinese A-share market staged a strong rally on July 21. The STAR 50 index surged 10.73%, its largest single-day gain in nearly a year, leading a broad-based "V-shaped" reversal. The Shanghai Composite Index rose 1.79%, the Shenzhen Component Index gained 4.81%, and the ChiNext Index jumped 7.05%. Total market turnover reached 2.97 trillion yuan, an increase of 256.1 billion yuan from the previous session, with over 3,100 stocks advancing. The semiconductor sector spearheaded the rebound, with related ETFs posting significant gains. Analysts attribute the surge to three converging factors. First, coordinated capital inflows from "national team" institutions, insurance funds, listed company buybacks, and fund house self-purchases have bolstered market liquidity and confidence. Second, supportive policy signals, including commitments from regulators to ensure stable market operations, provided a favorable backdrop. Third, a stabilization and recovery in overseas markets, notably South Korea, created a positive external environment. Institutions suggest the most severe panic selling phase for the tech sector has likely passed, following a significant digestion of crowded positions and leveraged funds. While short-term volatility may persist, the medium to long-term outlook remains underpinned by enduring trends like AI computing demand expansion and semiconductor localization. The market's focus now shifts to the sustainability of supportive fund flows, earnings reports, and upcoming catalysts from the global AI industry chain.

marsbit15m ago

STAR 50 Soars 10.73%, Why Did A-Shares Stage a "V-Shaped Reversal"?

marsbit15m ago

U.S. Tech Momentum Stocks Post Largest Single-Day Gain Ever, But Is the Plunge Over?

US tech momentum stocks staged a sharp rebound on Tuesday (July 21st). Morgan Stanley's TMT Momentum Factor surged over 12%, marking its largest single-day gain on record, exceeding even peaks from the 2000 dot-com bubble. Key momentum indices from Goldman Sachs also posted their strongest daily performances in years. The rally was led by semiconductors, with the Philadelphia Semiconductor Index jumping 4.6%. This rebound followed three consecutive down days and a cumulative 33% plunge in momentum stocks, one of the steepest drawdowns since the dot-com era. Analysts attribute the surge largely to a short squeeze. Heavy selling had pushed high-beta momentum stocks into deeply oversold territory, forcing many short sellers, particularly in Asia, to cover their positions, creating a self-reinforcing buying spiral. However, the rebound's internals appear weak. Trading volume was notably low, and advancing stocks still lagged decliners on the S&P 500, indicating a narrow, concentrated rally rather than broad market participation. Diverging views emerge on the outlook. BTIG warns the bounce has hit key resistance and recommends selling into strength, citing extreme volatility and historical parallels to past market tops. Conversely, Goldman Sachs and UBS believe the momentum unwind is nearing its end, suggesting it may be time to gradually add exposure, as positioning has been significantly reduced. They caution, however, that high volatility warrants a measured approach, potentially using defined-risk strategies. The upcoming earnings season, particularly reports from major tech firms like Alphabet, is seen as a critical test for the rally's sustainability. Simultaneously, bond markets flashed a warning, with yields rising partly due to spiking oil prices. Analysts note that if long-term Treasury yields break decisively higher, it could pose a significant headwind for equities, especially growth stocks.

marsbit22m ago

U.S. Tech Momentum Stocks Post Largest Single-Day Gain Ever, But Is the Plunge Over?

marsbit22m ago

U.S. Tech Momentum Stocks Record Largest Single-Day Gain Ever, but Has the Rout Ended?

U.S. tech momentum stocks staged a dramatic rebound on Tuesday, July 21st. Key momentum indices like the Morgan Stanley TMT Momentum Factor and Goldman Sachs' High Beta Momentum Long Index posted historic or near-historic single-day gains, fueled largely by semiconductor stocks. This sharp rally followed a severe three-day sell-off that saw momentum stocks plunge 33%, marking one of the steepest pullbacks since the dot-com bubble. Analysts attribute the bounce primarily to a short squeeze, as forced covering from over-leveraged traders, particularly in Asia, created a buying spiral. However, the rally's health is questioned due to weak market breadth—overall trading volume was low, and decliners outnumbered advancers in the S&P 500 despite the index's gain—suggesting a narrow, concentrated surge rather than broad recovery. Opinions on the sustainability diverge. BTIG strategists warn the rebound has hit key resistance levels, citing extreme volatility and historic stock dispersion as signs of an ongoing broader correction, and recommend selling into strength. Conversely, Goldman Sachs and UBS view the aggressive momentum unwinding as nearing its end, noting reduced positioning and a lack of new fundamental catalysts. They suggest the sell-off presents a selective opportunity to add exposure, albeit cautiously and gradually using defined-risk strategies. The immediate trajectory hinges on the ongoing earnings season, with market focus on Alphabet's capital expenditure guidance for AI investment clarity. Meanwhile, bond markets present a risk, with rising Treasury yields—potentially heading toward 5.5%—and widening credit spreads for mega-cap tech companies posing a threat to equity valuations. The combination of technical factors, earnings results, and macro conditions leaves the durability of the rebound in doubt.

链捕手25m ago

U.S. Tech Momentum Stocks Record Largest Single-Day Gain Ever, but Has the Rout Ended?

链捕手25m ago

Long-Divided Must Unite, Long-United Must Divide: When L1 Becomes Its Own Rollup, What Is Ethereum's Endgame?

"The Inevitable Cycle: When L1 Becomes Its Own Rollup – What is Ethereum's Endgame?" For years, the Ethereum community grappled with concerns that L2s were fragmenting the ecosystem and eroding L1's value. While L2s provided cheaper execution, they also splintered liquidity and the unified user experience of a single chain. This has prompted a fundamental reassessment of the relationship between L1 and L2. Ethereum's roadmap is evolving. The "Scale" initiative merges L1 and L2 expansion into a holistic framework. L1 itself is advancing with higher gas limits, statelessness, and zkEVM verification, no longer content to be just a low-throughput settlement layer. Consequently, the primary value proposition of L2s is shifting from merely providing cheap blockspace to offering L1 cannot easily provide: application-specific optimizations, privacy features, and flexible governance models. L2s are becoming a spectrum of execution environments with varying degrees of security inheritance from Ethereum. A critical challenge in this multi-chain future is interoperability. The vision is to make Ethereum "feel like one chain again." This relies on advancements in native account abstraction (like EIP-7702) and intent-based architectures (Open Intents Framework), where users declare desired outcomes, and solvers handle the complex cross-chain execution. Furthermore, shortening Ethereum's finality time from minutes to seconds is crucial, as it underpins trust between chains for bridges, stablecoins, and cross-chain applications. Perhaps the most provocative idea is that Ethereum L1 itself could become a form of "its own Rollup." As zkEVM and proof systems mature, high-performance nodes could execute transactions and generate validity proofs. Regular validators would then verify these proofs instead of re-executing all transactions. This blurs the traditional L1/L2 hierarchy, making "Rollup" more of a general execution-verification architecture. Native Rollup aims to integrate L2 validation more directly into the Ethereum protocol, allowing L2s to inherit L1's security more fully and move away from reliance on security councils. In the end, L2s are not destined to replace L1 or be made obsolete by it. The likely future is a unified system where diverse execution environments—each optimized for specific use cases like DeFi, gaming, or privacy—coexist. They will share a common foundation of security, liquidity, and verifiable state, seamlessly connected to restore a cohesive user experience. The next phase for Ethereum is not just about scaling through separation, but about intelligently reintegrating what was separated back into a coherent whole.

链捕手41m ago

Long-Divided Must Unite, Long-United Must Divide: When L1 Becomes Its Own Rollup, What Is Ethereum's Endgame?

链捕手41m ago

Trading

Spot
活动图片