Cryptocurrencies

Finage offers you more than 1700+ cryptocurrency data in real time.

With Finage, you can react to the cryptocurrency data in Real-Time via WebSocket or unlimited API calls. Also, we offer you a 7-year historical data API.

You can view the full Cryptocurrency market coverage with the link given below. https://finage.s3.eu-west-2.amazonaws.com/Finage_Crypto_Coverage.pdf

We monitor a number of mentions and their sentiment on Reddit, Twitter, and Telegram for the top 100 major crypto coins by liquidity.

Designed for quants and algorithmic traders, our real-time data stream provides you with an in-depth look at the social movements around cryptocurrencies and tokens.

Stay informed on the quantity and content of discussions, social buzz, and sentiment around any crypto/web3 project with our razor-sharp data. Social Pulse won't let you miss a beat in the fast-paced world of crypto trading.

This dataset provides comprehensive access to financial market data from Google Finance in real-time. Get detailed information on stocks, market quotes, trends, ETFs, international exchanges, forex, crypto, and related news. Perfect for financial applications, trading platforms, and market analysis tools. The dataset is delivered in a JSON format via REST API.

Crypto Data Platform Market Outlook

According to our latest research, the global Crypto Data Platform market size reached USD 1.85 billion in 2024, reflecting robust adoption across institutional and retail segments. The market is expected to expand at a CAGR of 18.2% during the forecast period, with revenues projected to reach USD 9.25 billion by 2033. This growth is primarily fueled by the increasing demand for real-time data analytics, advanced trading solutions, and regulatory compliance tools in the rapidly evolving cryptocurrency industry. The surge in digital asset adoption, coupled with heightened institutional participation and technological advancements, is driving the need for comprehensive, scalable, and secure crypto data platforms worldwide.

A significant growth factor for the Crypto Data Platform market is the exponential rise in crypto trading volumes and the proliferation of digital assets. As institutional investors, hedge funds, and family offices continue to increase their exposure to cryptocurrencies, the requirement for accurate, timely, and actionable data has become paramount. Crypto data platforms are now pivotal in providing market participants with historical and real-time price feeds, blockchain analytics, on-chain indicators, and sentiment analysis. These platforms also enable seamless integration with trading systems and portfolio management tools, empowering users to make informed investment decisions. The ongoing innovation in decentralized finance (DeFi) and the emergence of new digital asset classes further intensify the demand for robust data solutions, positioning crypto data platforms as a critical infrastructure layer in the digital economy.

Another key driver is the growing emphasis on regulatory compliance and risk management across the crypto ecosystem. As governments and regulatory bodies worldwide introduce stricter frameworks for anti-money laundering (AML), know-your-customer (KYC), and market surveillance, enterprises and exchanges are increasingly leveraging crypto data platforms to ensure adherence to these mandates. These platforms offer advanced compliance modules, transaction monitoring, and risk analytics, enabling stakeholders to mitigate operational and reputational risks. The integration of artificial intelligence (AI) and machine learning (ML) into these solutions further enhances their capability to detect anomalies, prevent fraud, and deliver predictive insights, thereby fostering trust and transparency in the market.

The rapid advancement in cloud computing, API-driven architectures, and interoperability standards is also propelling the Crypto Data Platform market forward. As digital asset markets operate around the clock and across geographies, there is a pressing need for scalable, resilient, and highly available data infrastructure. Cloud-based deployment models facilitate seamless access to vast datasets, while API integrations enable real-time connectivity with trading platforms, wallets, and external data sources. This technological evolution is enabling both established financial institutions and emerging fintech startups to harness the power of crypto data without significant upfront investments in hardware or IT resources. As a result, the market is witnessing accelerated product innovation, ecosystem collaboration, and the entry of new players offering specialized data services.

Regionally, North America continues to dominate the Crypto Data Platform market, accounting for the largest revenue share in 2024. The region’s leadership is underpinned by the presence of major crypto exchanges, institutional investors, and a mature regulatory landscape. Europe and Asia Pacific are also witnessing rapid adoption, driven by progressive regulatory initiatives, growing fintech ecosystems, and increasing retail investor participation. Latin America and the Middle East & Africa are emerging as promising markets, supported by rising digital asset adoption and government-led blockchain initiatives. However, regional disparities in regulatory clarity, technological infrastructure, and capital market maturity present both opportunities and challenges for market participants.

Component Analysis

The Crypto Data Platform market by component is segmented into Solutions and Services, each playing a vital role in the industry’s value chain. Solutions encompass the core software platforms that aggregate, normali

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This dataset contains real-time prices of various cryptocurrencies that are listed on Yahoo Finance. The data has been collected from Yahoo Finance API and consists of 9,600 rows of data.

🚀 Title: One-Hour High-Frequency Crypto Snapshot – Top 250 Coins, ~75K Ticks

📝 Overview

This dataset captures live market snapshots every 12 seconds for the top 250 cryptocurrencies, all fetched over a one-hour period using the CoinGecko Demo API. Perfect for real-time trend tracking, volatility analysis, and comparison across major coins.

📊 Schema Summary

🎯 Use Cases

• Visualize real-time price evolution for Bitcoin, Ethereum, and other major coins
• Compute rolling averages and short-term volatility
• Perform coin-to-coin comparisons (price dynamics, volume trends)
• Explore volume-price correlations, and flag anomaly detection
• Build heatmaps, live dashboards, or time-series models

📌 Attribution & Licensing

Data collected via CoinGecko API—**Data powered by CoinGecko**

🟦 What this is Canonical ERC-20 token reference with deterministic tracing at the row level. One row per token contract, with audit-grade lineage to the first recognition event and to parent/genesis derivations. • Schema-stable, versioned, audit-ready • Historical + real-time options

🌐 Chains / Coverage ETH, BSC, Base, Arbitrum, Unichain, Avalanche, Polygon, Celo, Linea, Optimism (others on request). Full history from chain genesis; reorg-aware real-time ingestion and updates.

📑 Schema List the columns exactly as delivered. Keep names/types consistent with files. • contract_address BYTEA - PK; 20-byte ERC-20 contract address • tracing_id BYTEA - deterministic row-level hash (proof-of-derivation) • parent_tracing_ids BYTEA - salted hash(es) of immediate parent rows in the derivation graph • genesis_tracing_ids BYTEA - salted hash(es) of original sources (genesis of the derivation path) • genesis_block_number BIGINT - first block where the token was recognized • genesis_tx_index INTEGER - tx index for that event • genesis_log_index INTEGER - log index for that event • name TEXT - ERC-20 name() • symbol TEXT - ERC-20 symbol() • decimals SMALLINT - ERC-20 decimals()

Notes • Use encode(contract_address,'hex') for hex presentation. • Metadata (name, symbol, decimals) is populated from ABI reads. • If the ABI read was unsuccessful, the token is not present in this table (columns are NOT NULL by design).

🔑 Keys & Joins • Primary key: contract_address • Lineage triple for joins to raw events: (genesis_block_number, genesis_tx_index, genesis_log_index)

🧬 Lineage & Reproducibility Every row has a verifiable path back to the originating raw events via the lineage triple and tracing graph: • tracing_id - this row’s identity • parent_tracing_ids - immediate sources • genesis_tracing_ids - original on-chain sources This supports audits and exact reprocessing to source transactions/logs/function calls.

📈 Common uses • Token registry to normalize joins for swaps, transfers, pools, and prices • Amount scaling via decimals for analytics, PnL, and model features • App backends: display names/symbols and validate token addresses

🚚 Delivery By default • WebSocket (API/WSS) reorg-aware live emissions when a new update is available; <140 ms median latency on ETH streams (7-day). • SFTP server for archives and daily End-of-Day (EOD) snapshots. • Model Context Protocol (MCP) for AI workflows (pull slices, schemas, lineage). Optional • Integrations to Amazon S3, Azure Blob Storage, Snowflake, and other enterprise platforms on request.

🗂️ Files (time-partitioned in UTC, compressed) • Parquet • CSV • XLS • JSON

💡 Quality and operations • Reorg-aware ingestion. • 99.95% uptime SLA. • Backfills to chain genesis. • Versioned, schema-stable datasets; changes are additive and announced.

🔄 Change policy Schema is stable. Any breaking change ships as a new version (e.g., erc20_tokens_v2) with migration notes. Content updates are additive (new rows/fields filled); types aren’t changed in place.

Financial Data Exchange API Integration Market Outlook

According to our latest research, the Financial Data Exchange API Integration market size reached USD 3.42 billion globally in 2024. The market is experiencing a robust expansion, registering a CAGR of 23.1% from 2025 to 2033. By the end of 2033, the market is forecasted to attain a value of USD 25.09 billion. This remarkable growth trajectory is propelled by the increasing adoption of open banking, regulatory mandates for data transparency, and the growing demand for seamless connectivity between financial institutions, fintech firms, and third-party service providers.

One of the most significant growth factors driving the Financial Data Exchange API Integration market is the widespread adoption of open banking initiatives across the globe. Regulatory frameworks such as PSD2 in Europe, the Consumer Data Right in Australia, and similar policies in North America are compelling banks and financial institutions to provide secure, standardized API access to customer data. This not only enhances customer experience by enabling personalized financial services but also fosters innovation by allowing third-party developers to build novel financial products. As a result, the market is witnessing a surge in demand for robust, scalable, and secure API integration solutions that can handle complex data exchange requirements while ensuring compliance with evolving regulatory standards.

Another pivotal driver fueling the market’s expansion is the rapid digital transformation within the financial services sector. Financial institutions are increasingly leveraging APIs to enhance operational efficiency, streamline workflows, and deliver real-time services such as instant payments, automated wealth management, and digital lending. The proliferation of fintech startups and the entry of technology giants into the financial domain have further intensified the need for seamless data connectivity and interoperability. This has led to a significant uptick in investments in API integration platforms and services, as organizations seek to modernize legacy systems, reduce integration complexities, and accelerate time-to-market for new digital offerings.

The growing emphasis on customer-centricity and data-driven decision-making is also contributing to the robust growth of the Financial Data Exchange API Integration market. Financial institutions are increasingly harnessing APIs to aggregate and analyze vast volumes of customer data from multiple sources, enabling them to deliver hyper-personalized products, improve risk assessment, and enhance fraud detection capabilities. The integration of advanced technologies such as artificial intelligence, machine learning, and blockchain with financial data exchange APIs is opening up new avenues for innovation, further amplifying the market’s growth potential. Moreover, the shift towards cloud-based API integration solutions is enabling organizations to achieve greater scalability, flexibility, and cost-efficiency, thereby accelerating the adoption of API-driven architectures across the financial ecosystem.

From a regional perspective, North America currently dominates the Financial Data Exchange API Integration market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The presence of a highly developed financial services infrastructure, early adoption of open banking regulations, and a vibrant fintech ecosystem are key factors contributing to North America’s leadership. However, the Asia Pacific region is expected to exhibit the fastest growth during the forecast period, driven by rapid digitalization, increasing smartphone penetration, and supportive government policies promoting financial inclusion. Europe remains a significant market due to its stringent regulatory environment and proactive stance on data privacy and security. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as promising markets, fueled by rising investments in fintech and digital banking initiatives.

Component Analysis

The Component segment of the Financial Data Exchange API Integration market is categorized into Software, Services, and Platforms. Software solutions form the backbone of API integration, providing the essential tools and frameworks required to establish secure, scalable, and co

Crypto-Native Payroll Market Outlook

According to our latest research, the global crypto-native payroll market size in 2024 reached USD 1.14 billion, reflecting a robust adoption curve across diverse industries. The market is expected to grow at a CAGR of 22.7% from 2025 to 2033, reaching an anticipated value of USD 8.41 billion by 2033. This impressive growth trajectory is primarily driven by the increasing acceptance of cryptocurrencies as legitimate payment instruments, the demand for borderless payroll solutions, and the proliferation of blockchain-enabled financial services. As per our latest research, the market is witnessing a surge in both enterprise and SME adoption, underpinned by the promise of faster settlements, reduced transaction costs, and enhanced transparency.

One of the primary growth factors for the crypto-native payroll market is the growing globalization of the workforce. With remote and distributed teams becoming the norm, especially in the IT and digital services sectors, traditional payroll systems often struggle with cross-border payments, compliance, and currency conversion complexities. Crypto-native payroll solutions offer seamless, real-time, and cost-effective remuneration options that bypass conventional banking channels, making them particularly attractive to companies with multinational operations. This shift is further supported by the increasing regulatory clarity in major economies, which is encouraging organizations to explore cryptocurrency-based payroll as a viable and compliant alternative.

Another significant driver is the rise of stablecoins and programmable payments, which have addressed many of the volatility and operational risks previously associated with cryptocurrency compensation. Stablecoins, pegged to fiat currencies, offer the stability required for payroll operations, while smart contract-based solutions enable automated, auditable, and conditional payments. This technological evolution is empowering both employers and employees to customize payment schedules, split payments across multiple currencies, and integrate payroll with decentralized finance (DeFi) platforms for additional financial services. The integration of such features is accelerating the adoption of crypto-native payroll, especially among tech-savvy and innovation-driven enterprises.

Additionally, the competitive landscape and the need for talent acquisition in high-growth sectors are pushing organizations to offer crypto-based payroll as a differentiator. Employees, particularly in the technology, blockchain, and creative industries, are increasingly seeking compensation in cryptocurrencies for reasons such as investment potential, financial autonomy, and ease of cross-border transactions. This trend is compelling companies to adopt crypto-native payroll solutions to attract and retain top talent, thereby fueling market expansion. Furthermore, the enhanced security, transparency, and traceability provided by blockchain technology are addressing concerns around fraud, compliance, and auditability, making crypto-native payroll solutions more appealing to enterprises of all sizes.

The integration of Payroll API solutions is becoming increasingly significant in the crypto-native payroll market. These APIs facilitate seamless communication between payroll systems and other financial platforms, enhancing the efficiency and accuracy of payroll processing. By leveraging Payroll API, organizations can automate data exchange, reduce manual errors, and ensure real-time updates across their financial systems. This capability is particularly beneficial for companies operating in multiple jurisdictions, as it simplifies compliance with diverse regulatory requirements and streamlines cross-border transactions. As the demand for integrated financial solutions grows, Payroll API is poised to play a crucial role in the evolution of crypto-native payroll systems, offering organizations a competitive edge in managing their payroll operations.

From a regional perspective, North America currently leads the crypto-native payroll market, accounting for the largest share due to early regulatory advancements, high digital literacy, and the presence of major blockchain innovators. Europe follows closely, driven by progressive regulatory frameworks and the rapid digitization of finan

Crypto Exchange Platform Market Outlook

According to our latest research, the global Crypto Exchange Platform market size reached USD 45.3 billion in 2024, reflecting robust momentum driven by increasing digital asset adoption and institutional participation. The market is projected to grow at a CAGR of 15.2% from 2025 to 2033, reaching a forecasted value of USD 155.1 billion by 2033. This remarkable growth trajectory is primarily fueled by expanding cryptocurrency adoption, technological advancements in blockchain infrastructure, and an evolving regulatory landscape that is increasingly supportive of digital asset trading platforms.

One of the most significant growth factors for the Crypto Exchange Platform market is the surge in global cryptocurrency adoption among both retail and institutional investors. As digital currencies become more mainstream, both individuals and organizations are seeking secure, reliable, and user-friendly platforms to facilitate trading, storage, and management of crypto assets. The growing popularity of decentralized finance (DeFi) and the increasing integration of cryptocurrencies into traditional financial systems have further accelerated the demand for robust exchange platforms. Additionally, the introduction of new digital assets and tokens, including stablecoins and non-fungible tokens (NFTs), has diversified trading opportunities, attracting a broader user base to these platforms.

Technological innovation continues to play a pivotal role in shaping the Crypto Exchange Platform market. Advances in blockchain technology, enhanced security protocols, and the integration of artificial intelligence and machine learning for fraud detection and risk management have significantly improved platform reliability and user trust. The emergence of hybrid exchange models, combining the best features of centralized and decentralized exchanges, is also gaining traction, offering users increased liquidity, security, and transparency. Furthermore, the proliferation of mobile trading applications and API-driven trading solutions is making crypto trading more accessible, further driving market growth.

The regulatory environment is another critical growth factor influencing the Crypto Exchange Platform market. Governments and regulatory bodies worldwide are increasingly recognizing the importance of establishing clear frameworks for digital asset trading. Regulatory clarity not only boosts investor confidence but also encourages institutional participation, which is essential for market maturity. Recent moves by countries to implement licensing regimes, enforce anti-money laundering (AML) and know-your-customer (KYC) requirements, and provide legal recognition to cryptocurrencies have created a more stable and predictable operating environment for crypto exchanges, thereby supporting sustained market expansion.

Regionally, the Asia Pacific region continues to dominate the Crypto Exchange Platform market, driven by high levels of crypto adoption in countries like Japan, South Korea, and Singapore. North America remains a significant market due to its advanced financial infrastructure and active regulatory engagement, while Europe is witnessing rapid growth owing to progressive regulatory initiatives and increasing institutional interest. Emerging markets in Latin America and the Middle East & Africa are also showing strong potential, fueled by rising demand for alternative financial solutions and increasing mobile internet penetration. As the market continues to globalize, regional trends and regulatory developments will play a crucial role in shaping the future landscape of crypto exchange platforms.

Component Analysis

The Component segment in the Crypto Exchange Platform market is divided into Software and Services, both of which are indispensable for the optimal functioning of crypto trading platforms. Software forms the backbone of exchange operations, encompassing trading engines, user interfaces, wallet integration, and security protocols. The increasing demand for high-performance and secure trading environments has led to continuous innovation in exchange software, including the integration of advanced analytics, real-time data processing, and automated trading features. As the market matures, software solutions are evolving to address scalability challenges, support a broader range of digital assets, and p

🟦 What this is Canonical ERC-721 NFT collection reference with deterministic tracing at the row level. One row per deployed NFT contract, providing audit-grade lineage to the first recognition event and parent/genesis derivations.

Key traits • Schema-stable, versioned, and audit-ready • Historical + real-time ingestion from chain genesis • Verified on-chain metadata (name, symbol) from contract ABI reads

🌐 Chains / Coverage ETH, BSC, Base, Arbitrum, Unichain, Avalanche, Polygon, Celo, Linea, Optimism (others on request). Full history from chain genesis; reorg-aware real-time ingestion and updates.

📑 Schema List the columns exactly as delivered. Keep names/types consistent with files. • contract_address BYTEA - PK; 20-byte ERC-20 contract address • tracing_id BYTEA - deterministic row-level hash (proof-of-derivation) • parent_tracing_ids BYTEA - salted hash(es) of immediate parent rows in the derivation graph • genesis_tracing_ids BYTEA - salted hash(es) of original sources (genesis of the derivation path) • genesis_block_number BIGINT - first block where the token was recognized • genesis_tx_index INTEGER - tx index for that event • genesis_log_index INTEGER - log index for that event • name TEXT - NFT collection name (from ABI call name()) • symbol TEXT - NFT symbol (from ABI call symbol()) • base_token_uri TEXT NULL - Base token URI (from ABI call baseTokenURI())

Notes • Use encode(contract_address,'hex') for hex presentation. • Metadata is obtained deterministically via ABI calls; records are included only when both name and symbol are successfully decoded. • Additional token-level data (e.g. token_id, token_uri) are stored in dependent tables such as erc721_items.

🔑 Keys & Joins • Primary key: contract_address • Lineage triple for joins to raw events: (genesis_block_number, genesis_tx_index, genesis_log_index)

🧬 Lineage & Reproducibility Every row has a verifiable path back to the originating raw events via the lineage triple and tracing graph: • tracing_id - this row’s identity • parent_tracing_ids - immediate sources • genesis_tracing_ids - original on-chain sources This supports audits and exact reprocessing to source transactions/logs/function calls.

📈 Common uses • Token registry to normalize joins for swaps, transfers, pools, and prices • Amount scaling via decimals for analytics, PnL, and model features • App backends: display names/symbols and validate token addresses

🚚 Delivery By default • WebSocket (API/WSS) reorg-aware live emissions when a new update is available; <140 ms median latency on ETH streams (7-day). • SFTP server for archives and daily End-of-Day (EOD) snapshots. • Model Context Protocol (MCP) for AI workflows (pull slices, schemas, lineage). Optional • Integrations to Amazon S3, Azure Blob Storage, Snowflake, and other enterprise platforms on request.

🗂️ Files (time-partitioned in UTC, compressed) • Parquet • CSV • XLS • JSON

💡 Quality and operations • Reorg-aware ingestion. • 99.95% uptime target SLA. • Backfills to chain genesis. • Versioned, schema-stable datasets; changes are additive and announced.

🔄 Change policy Schema is stable. Any breaking change ships as a new version (e.g., erc721_tokens_v2) with migration notes. Content updates are additive (new rows/fields filled); types aren’t changed in place.

Financial Data Exchange API Integration Market Outlook

According to our latest research, the global Financial Data Exchange API Integration market size reached USD 3.84 billion in 2024, underscoring the sector’s robust expansion. The market is projected to grow at a CAGR of 17.2% from 2025 to 2033, with the market size expected to reach USD 15.36 billion by 2033. This surge is attributed to the rapid digital transformation in the financial sector, increasing adoption of open banking, and the growing need for seamless, secure, and real-time data sharing among financial institutions and third-party providers.

One of the key drivers fueling the growth of the Financial Data Exchange API Integration market is the widespread adoption of open banking initiatives worldwide. Regulatory frameworks such as PSD2 in Europe and similar mandates in other regions are compelling banks and financial institutions to open their data via secure APIs, fostering innovation and competition. This regulatory push has accelerated the need for robust API integration platforms that can securely manage complex data exchanges between banks, fintechs, and other financial entities. Furthermore, consumer demand for personalized, real-time financial services is pushing organizations to integrate APIs that enable instant access to account information, payment initiation, and financial analytics, thus driving market expansion.

Another significant growth factor is the proliferation of fintech startups and digital-first financial service providers. These companies rely heavily on API integrations to connect with traditional financial institutions, aggregate customer data, and deliver innovative solutions such as mobile banking, robo-advisory, and payment gateways. The competitive landscape is encouraging established banks and insurance providers to modernize their IT infrastructure and adopt API-centric architectures. As a result, the demand for scalable, secure, and compliant API integration solutions is rising, further propelling the market forward. Additionally, the COVID-19 pandemic has accelerated digital adoption, making seamless data exchange a necessity for remote operations and digital customer engagement.

The increasing focus on customer experience and operational efficiency is also acting as a catalyst for market growth. Financial institutions are leveraging API integrations to automate workflows, reduce manual processing, and provide customers with unified, omnichannel experiences. The integration of APIs with advanced technologies like artificial intelligence, blockchain, and machine learning is enabling real-time fraud detection, credit scoring, and personalized financial recommendations. These technological advancements are not only enhancing service delivery but also creating new revenue streams for market participants, thereby contributing to the overall growth of the Financial Data Exchange API Integration market.

From a regional perspective, North America continues to dominate the market, accounting for the largest share in 2024 due to the early adoption of digital banking, a mature fintech ecosystem, and favorable regulatory environments. Europe follows closely, driven by strong regulatory mandates and a collaborative approach between traditional banks and fintechs. The Asia Pacific region is witnessing the fastest growth, fueled by a rapidly expanding digital economy, increasing smartphone penetration, and supportive government policies promoting financial inclusion. Latin America and the Middle East & Africa are also experiencing steady growth, albeit at a slower pace, as financial institutions in these regions accelerate their digital transformation journeys.

Component Analysis

The Component segment of the Financial Data Exchange API Integration market is primarily divided into Software and Services. The Software component encompasses API management platforms, integration middleware, and security solutions that

Context

This data set is generated by the help of Binance Api.

What is Binance Api? The Binance API is a method that allows you to connect to the Binance servers via Python or several other programming languages. With it, you can automate your trading.

More specifically, Binance has a RESTful API that uses HTTP requests to send and receive data. Further, there is also a WebSocket available that enables the streaming of data such as price quotes and account updates.

Content

In this data set the data is generated on the interval of 1 minute by an API. It includes many columns showing the real change in price of Bitcoin also shows the Open, High, Low, Close price of Bitcoin on particular minutes. The Open Time and Close Time in the data set are in Unix Timestamp.

Acknowledgements

Special thanks to Binance Stream Data Api.

🟦 What this is Canonical registry of all verified Aerodrome Slipstream liquidity pools, normalized into the BlockDB schema for deterministic cross-chain analysis. Fees are modeled separately as versioned terms, so fee changes over time are tracked precisely and are joinable back to the pool.

This dataset provides the structural backbone for connecting Aerodrome Slipstream pool states with liquidity, price, and swap datasets.

Key traits • Schema-stable, versioned, and lineage-verifiable • Deterministic pool UIDs across chains and factory contracts • Real-time and historical coverage with factory-level lineage • Fully joinable with reserves, prices, swaps, and fee-term timelines

🌐 Chains / Coverage ETH, BSC, Base, Arbitrum, Unichain, Avalanche, Polygon, Celo, Linea, Optimism (others on request). Full backfill to chain genesis with reorg-aware real-time ingestion. Covers all verified Aerodrome Slipstream deployments across supported EVM networks.

📑 Schema List the columns exactly as delivered. Names/types are stable.

liquidity_pools (base registry) • uid BIGINT NOT NULL - stable pool identifier (derived from address or pool_id) • tracing_id BYTEA - deterministic row-level hash (proof-of-derivation) • parent_tracing_ids BYTEA - salted hash(es) of immediate parent rows in the derivation graph • genesis_tracing_ids BYTEA - salted hash(es) of original sources (genesis of the derivation path) • genesis_block_number BIGINT NOT NULL - factory create event block • genesis_tx_index INTEGER NOT NULL - create event tx index • genesis_log_index INTEGER NOT NULL - create event log index • contract_address BYTEA NULL - 20-byte pool address (v2/v3-style) • pool_id BYTEA NULL - 32-byte pool id (v4-style / manager-based) • factory BYTEA NOT NULL - DEX factory / pool manager address • type_id INTEGER NOT NULL - pool type FK (constant-product, concentrated, stable/weighted, etc.) • pairnum NUMERIC(6) NULL - optional pair ordinal/descriptor • tokens BYTEA[] NOT NULL - array of 20-byte token addresses (order matches protocol convention) • asset_managers BYTEA[] NULL - per-token managers (e.g., Balancer) • amp NUMERIC(6) NULL - amplification for stable/weighted math • pool_type TEXT NULL - optional human-readable type label • weights NUMERIC(6,5)[] NULL - per-token weights in 0..1 (5 dp) • tick_spacing SMALLINT NULL - grid size for concentrated liquidity

liquidity_pool_fee_terms (versioned, non-overlapping) • pool_uid BIGINT NOT NULL - FK → liquidity_pools(uid) • tracing_id BYTEA - Row identity hash (proof-of-derivation) • parent_tracing_ids BYTEA - Salted hashes of contributing parents • genesis_tracing_ids BYTEA - Salted hashes of ultimate sources • genesis_block_number BIGINT NOT NULL - Fee-update event block • genesis_block_time TIMESTAMPZ NOT NULL - Fee-update event timestamp • genesis_tx_index INTEGER NOT NULL - Transaction index • genesis_log_index INTEGER NOT NULL - Log index • pool_fee NUMERIC(18,18) NOT NULL - Total fee fraction (e.g. 0.003 = 0.30 %) • user_fee_bps SMALLINT NULL - Optional user-side fee share (0–10 000) • protocol_fee_bps SMALLINT NULL - Optional protocol-side fee share (0–10 000) • fee_source TEXT NOT NULL - Provenance of fee rate (e.g. onchain:event) • fee_share_source TEXT NOT NULL - Provenance of fee split (e.g. onchain:param, docs)

Checks • At least one identifier present (contract_address or pool_id) and lengths enforced (20B/32B).

Notes • Fee terms are non-overlapping; each record defines a valid block-range. • Use liquidity_pool_fee_terms for historical fee reconstruction or to obtain the active fee at a given block.

🔑 Keys & joins • Primary key: uid • Lineage triple for joins to raw events: (genesis_block_number, genesis_tx_index, genesis_log_index) • Foreign keys: • (genesis_block_number, genesis_tx_index, genesis_log_index) → logs(block_number, tx_index, log_index) • factory → decentralized_exchanges(contract_address) • type_id → liquidity_pool_types(id)

🧬 Lineage & Reproducibility Every row has a verifiable path back to the originating raw events via the lineage triple and tracing graph: • tracing_id - this row’s identity • parent_tracing_ids - immediate sources • genesis_tracing_ids - original on-chain sources This supports audits and exact reprocessing to source transactions/logs/function calls.

📈 Common uses • Building the complete pool registry for routing and analytics • Filtering pools by fee, type, or token pair • Integrating with reserves, price, and swap datasets for liquidity intelligence • MEV routing, arbitrage path optimization, and chain-wide pool analytics • Constructing pool-level AI or quantitative features

🚚 Delivery By default • WebSocket (API/WSS) reorg-aware live emissions when a new update is available; <140 ms median latency on ETH streams (7-day). • SFTP server for archives and daily End-of-Day (EOD) snapshots. • Model Context Protocol (MCP) for AI workflows (pull slices, schemas, line...

🟦 What this is Canonical registry of all DEX liquidity pools, normalized into the BlockDB schema for deterministic cross-chain analysis. Fees are modeled separately as versioned terms, so fee changes over time are tracked precisely and are joinable back to the pool.

Key traits • Schema-stable, versioned, and lineage-verifiable • Deterministic pool UIDs across chains and factory contracts • Real-time and historical coverage with factory-level lineage • Fully joinable with reserves, prices, swaps, and fee-term timelines

🌐 Chains / Coverage ETH, BSC, Base, Arbitrum, Unichain, Avalanche, Polygon, Celo, Linea, Optimism (others on request). Full history from chain genesis; reorg-aware real-time ingestion and updates. Coverage includes: • Uniswap V2, V3, V4 • Balancer V2, PancakeSwap, Solidly, Maverick, Aerodrome, and others

📑 Schema List the columns exactly as delivered. Names/types are stable.

liquidity_pools (base registry) • uid BIGINT NOT NULL - stable pool identifier (derived from address or pool_id) • tracing_id BYTEA - deterministic row-level hash (proof-of-derivation) • parent_tracing_ids BYTEA - salted hash(es) of immediate parent rows in the derivation graph • genesis_tracing_ids BYTEA - salted hash(es) of original sources (genesis of the derivation path) • genesis_block_number BIGINT NOT NULL - factory create event block • genesis_tx_index INTEGER NOT NULL - create event tx index • genesis_log_index INTEGER NOT NULL - create event log index • contract_address BYTEA NULL - 20-byte pool address (v2/v3-style) • pool_id BYTEA NULL - 32-byte pool id (v4-style / manager-based) • factory BYTEA NOT NULL - DEX factory / pool manager address • type_id INTEGER NOT NULL - pool type FK (constant-product, concentrated, stable/weighted, etc.) • pairnum NUMERIC(6) NULL - optional pair ordinal/descriptor • tokens BYTEA[] NOT NULL - array of 20-byte token addresses (order matches protocol convention) • asset_managers BYTEA[] NULL - per-token managers (e.g., Balancer) • amp NUMERIC(6) NULL - amplification for stable/weighted math • pool_type TEXT NULL - optional human-readable type label • weights NUMERIC(6,5)[] NULL - per-token weights in 0..1 (5 dp) • tick_spacing SMALLINT NULL - grid size for concentrated liquidity

liquidity_pool_fee_terms (versioned, non-overlapping) • pool_uid BIGINT NOT NULL - FK → liquidity_pools(uid) • tracing_id BYTEA - Row identity hash (proof-of-derivation) • parent_tracing_ids BYTEA - Salted hashes of contributing parents • genesis_tracing_ids BYTEA - Salted hashes of ultimate sources • genesis_block_number BIGINT NOT NULL - Fee-update event block • genesis_block_time TIMESTAMPZ NOT NULL - Fee-update event timestamp • genesis_tx_index INTEGER NOT NULL - Transaction index • genesis_log_index INTEGER NOT NULL - Log index • pool_fee NUMERIC(18,18) NOT NULL - Total fee fraction (e.g. 0.003 = 0.30 %) • user_fee_bps SMALLINT NULL - Optional user-side fee share (0–10 000) • protocol_fee_bps SMALLINT NULL - Optional protocol-side fee share (0–10 000) • fee_source TEXT NOT NULL - Provenance of fee rate (e.g. onchain:event) • fee_share_source TEXT NOT NULL - Provenance of fee split (e.g. onchain:param, docs)

Checks • At least one identifier present (contract_address or pool_id) and lengths enforced (20B/32B).

Notes • Fee terms are non-overlapping; each record defines a valid block-range. • Use liquidity_pool_fee_terms for historical fee reconstruction or to obtain the active fee at a given block.

🔑 Keys & joins • Primary key: uid • Lineage triple for joins to raw events: (genesis_block_number, genesis_tx_index, genesis_log_index) • Foreign keys: • (genesis_block_number, genesis_tx_index, genesis_log_index) → logs(block_number, tx_index, log_index) • factory → decentralized_exchanges(contract_address) • type_id → liquidity_pool_types(id)

🧬 Lineage & Reproducibility Every row has a verifiable path back to the originating raw events via the lineage triple and tracing graph: • tracing_id - this row’s identity • parent_tracing_ids - immediate sources • genesis_tracing_ids - original on-chain sources This supports audits and exact reprocessing to source transactions/logs/function calls.

📈 Common uses • Building the complete DEX pool registry for routing and analytics • Filtering pools by fee, type, or token pair • Integrating with reserves, price, and swap datasets for liquidity intelligence • MEV routing, arbitrage path optimization, and chain-wide pool analytics • Constructing pool-level AI or quantitative features

🚚 Delivery By default • WebSocket (API/WSS) reorg-aware live emissions when a new update is available; <140 ms median latency on ETH streams (7-day). • SFTP server for archives and daily End-of-Day (EOD) snapshots. • Model Context Protocol (MCP) for AI workflows (pull slices, schemas, lineage). Optional • Integrations to Amazon S3, Azure Blob Storage, Snowflake, and other enterprise platforms on request.

...

The web screen scraping tools market, valued at $2831.7 million in 2025, is projected to experience robust growth, driven by the escalating demand for real-time data across diverse sectors. The market's Compound Annual Growth Rate (CAGR) of 4.6% from 2025 to 2033 indicates a steady expansion, fueled primarily by the increasing adoption of data-driven decision-making in e-commerce, investment analysis, and the burgeoning cryptocurrency industry. The "Pay-to-Use" segment currently dominates, reflecting businesses' preference for reliable, feature-rich solutions. However, the "Free-to-Use" segment shows promising growth potential, particularly among smaller businesses and individual developers seeking cost-effective data extraction solutions. Geographic growth is expected to be broad, with North America and Europe maintaining significant market share, while the Asia-Pacific region presents considerable untapped potential due to increasing digitalization and e-commerce adoption. Competitive pressures amongst established players like Import.io, Scrapinghub, and Apify are driving innovation and improvements in ease-of-use, data accuracy, and scalability. The market faces challenges related to legal and ethical concerns surrounding data scraping, as well as the ongoing evolution of website structures that can render scraping tools ineffective, necessitating constant updates and adaptations. The sustained growth trajectory of the web screen scraping tools market is anticipated to continue due to several factors. Firstly, the increasing complexity of data management across various sectors necessitates efficient data acquisition tools. Secondly, the expansion of e-commerce and the growth of the global digital economy fuels demand for accurate, up-to-date product information and market intelligence. Thirdly, the rise of big data analytics and the associated need for large datasets will continue to propel the adoption of web screen scraping solutions. The evolving regulatory landscape regarding data scraping will necessitate solutions that emphasize ethical and compliant data acquisition practices. This will drive innovation within the industry towards more responsible and robust web scraping tools that cater to the needs of businesses while respecting data privacy and copyright regulations. This will also favor the development of specialized tools optimized for specific sectors such as finance and e-commerce, rather than universal solutions.

Crypto Treasury Operations Platform Market Outlook

According to our latest research, the global Crypto Treasury Operations Platform market size reached USD 1.47 billion in 2024, driven by the surging adoption of digital assets for enterprise treasury management and the increasing need for streamlined, secure, and compliant operations. The market is expected to grow at a robust CAGR of 18.2% during the forecast period, reaching USD 6.25 billion by 2033. This remarkable growth is primarily fueled by the proliferation of institutional participation in the crypto ecosystem, rising regulatory scrutiny, and the demand for advanced tools that enable real-time visibility, risk mitigation, and automation in crypto asset management.

One of the key growth factors propelling the Crypto Treasury Operations Platform market is the rapid institutionalization of cryptocurrencies and digital assets. As major corporations, financial institutions, and fintech innovators increasingly integrate crypto assets into their balance sheets and transaction flows, the complexity of treasury operations has escalated. Enterprises now require robust platforms that offer end-to-end capabilities for liquidity management, risk assessment, compliance, and seamless settlements. The need for real-time visibility into holdings, automated reconciliation, and integration with both traditional finance and decentralized finance (DeFi) ecosystems is pushing organizations to invest in specialized crypto treasury solutions. Furthermore, the growing sophistication of crypto markets, including the advent of stablecoins, tokenized assets, and cross-border payment rails, is expanding the functional requirements for treasury platforms, driving both innovation and market expansion.

Another significant driver is the evolving regulatory landscape, which is compelling organizations to prioritize compliance and risk management in their crypto operations. With global regulators tightening their oversight on digital asset transactions, particularly concerning anti-money laundering (AML), know-your-customer (KYC), and tax reporting, treasury teams face mounting pressure to implement platforms that ensure transparency, auditability, and adherence to jurisdiction-specific rules. Crypto treasury operations platforms are rising to meet these demands by embedding advanced compliance modules, automated reporting tools, and real-time monitoring of wallet activities. This not only reduces the risk of regulatory penalties but also enhances stakeholder confidence and facilitates smoother engagement with banking partners and auditors.

The relentless pace of technological innovation is also catalyzing market growth. The integration of artificial intelligence, blockchain analytics, and API-driven architectures within crypto treasury platforms is enabling unprecedented levels of automation, data-driven decision-making, and interoperability. These advancements are particularly valuable for enterprises managing multi-chain portfolios, engaging in decentralized finance protocols, or seeking to optimize liquidity across global subsidiaries. The ability to automate complex workflows, predict cash flows, and dynamically allocate assets is transforming treasury operations from a back-office function into a strategic enabler of growth. As a result, organizations are increasingly viewing crypto treasury platforms as mission-critical infrastructure, further accelerating adoption across diverse industry verticals.

From a regional perspective, North America currently dominates the Crypto Treasury Operations Platform market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The region’s leadership is underpinned by a mature digital asset ecosystem, a high concentration of institutional investors, and proactive regulatory engagement. However, Asia Pacific is expected to exhibit the fastest growth rate through 2033, fueled by rapid fintech innovation, increasing enterprise crypto adoption, and supportive regulatory frameworks in key markets such as Singapore, Hong Kong, and Japan. Meanwhile, Europe continues to benefit from harmonized digital asset regulations and strong demand from both traditional financial institutions and emerging fintech players.

Component Analysis

The Crypto Treasury Operations Platform market is segmented by component into Software and Services, each playing a critical role in enabling

AI API Market Outlook

According to our latest research, the global AI API market size reached USD 8.7 billion in 2024, reflecting robust adoption across diverse industry verticals. The market is poised for significant expansion, with a projected CAGR of 25.2% during the forecast period. By 2033, the market is expected to reach USD 65.4 billion, driven by increasing integration of artificial intelligence capabilities into business processes and digital platforms. The primary growth factor underpinning this trend is the surging demand for scalable, plug-and-play AI solutions that empower enterprises to accelerate innovation, enhance customer engagement, and streamline operational efficiency.

A major driver for the AI API market is the rapid proliferation of digital transformation initiatives across both mature and emerging economies. Enterprises are increasingly seeking to leverage AI-powered APIs to unlock new revenue streams and differentiate their offerings in highly competitive markets. With the explosion of data generated from IoT devices, social media, and enterprise systems, organizations are turning to AI APIs for advanced analytics, real-time insights, and intelligent automation. This surge in data-driven decision-making is fueling demand for AI API solutions that can be seamlessly integrated into existing IT infrastructures, reducing time-to-market for new applications and services. Furthermore, the growing popularity of cloud-native architectures has made it easier for businesses of all sizes to access and deploy AI capabilities via APIs, further accelerating market growth.

Another significant growth factor is the rising sophistication and accessibility of AI models, which are increasingly being exposed through APIs by leading technology vendors. The democratization of AI through APIs allows even small and medium-sized enterprises (SMEs) to harness advanced functionalities such as natural language processing, computer vision, and predictive analytics without the need for in-house data science expertise. This trend is lowering barriers to entry and fostering a vibrant ecosystem of AI-powered applications across sectors like healthcare, finance, retail, and manufacturing. Additionally, the emergence of industry-specific AI APIs is enabling organizations to address unique business challenges, comply with regulatory requirements, and unlock new levels of operational efficiency.

The AI API market is also benefiting from the convergence of AI with other emerging technologies such as edge computing, 5G, and blockchain. This convergence is enabling the development of next-generation AI applications that are more secure, responsive, and scalable. For instance, AI APIs are increasingly being deployed at the network edge to support real-time analytics in autonomous vehicles, smart factories, and healthcare monitoring systems. The integration of AI APIs with blockchain is enhancing data integrity and transparency in applications like supply chain management and digital identity verification. These technological synergies are creating new opportunities for innovation and expanding the addressable market for AI API providers.

AI Middleware is becoming an integral component in the AI API market, acting as a bridge that facilitates seamless communication between different AI services and applications. This middleware layer is crucial for managing the complexities of integrating various AI functionalities, ensuring that data flows smoothly across platforms. By providing a standardized interface, AI Middleware enables developers to focus on creating innovative solutions without worrying about the underlying infrastructure. It also enhances the scalability and flexibility of AI deployments, allowing businesses to adapt quickly to changing market demands. As the demand for AI solutions grows, the role of AI Middleware in simplifying and streamlining AI integration processes will become increasingly important.

From a regional perspective, North America continues to dominate the AI API market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The regionÂ’s leadership is underpinned by the presence of major technology firms, a mature digital infrastructure, and a strong culture of innovation. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid economic development, expanding IT investments, and supportive government policies. Coun

🟦 What this is Reserves and state for Uniswap V3 Dex Protocol liquidity pools, including current tick, full tick-range from a minimum of -887272 to a maximum of 887272, Q64.96 sqrt price, liquidity and computed token amounts. Built for mid-price, depth/slippage, and routing research. • Schema-stable, versioned, audit-ready • Historical snapshots and real-time updates

🌐 Chains / Coverage ETH, BSC, Base, Arbitrum, Unichain, Avalanche, Polygon, Celo, Linea, Optimism (others on request). Full history from chain genesis; reorg-aware real-time ingestion and updates. Covers all Uniswap V3 factories across supported EVM chains.

📑 Schema liquidity_pools_reserves - pool-level snapshots • id BIGINT - identity primary key • pool_uid BIGINT - FK → liquidity_pools(uid) • tracing_id BYTEA - deterministic row-level lineage hash • parent_tracing_ids BYTEA - hashes of immediate parent sources • genesis_tracing_ids BYTEA - hashes of original on-chain sources • genesis_block_number BIGINT - block of observed state • genesis_block_time TIMESTAMPTZ - timestamp of the block • genesis_tx_index INTEGER - transaction index within the block • genesis_log_index INTEGER - log index within the transaction • current_tick INTEGER - active tick for concentrated-liquidity pools • current_sqrt_price NUMERIC(49,0) - Q64.96-encoded sqrt price (sqrtP = value / 2^96)

liquidity_pools_reserves_details - granular tick/bin distribution • snapshot_id BIGINT - FK → liquidity_pools_reserves(id) • pool_uid BIGINT - denormalized pool reference • tick INTEGER - single-tick record (Uniswap V3-style) • lower_tick INTEGER - lower bound of a range (position-based) • upper_tick INTEGER - upper bound of a range • liquidity NUMERIC(38,0) - engine-native liquidity metric (Uniswap V3 L) • amount0 NUMERIC(78,0) - token0 amount at this locator (raw units) • amount1 NUMERIC(78,0) - token1 amount at this locator (raw units)

Notes • All reserve and amount values are in raw on-chain token units. • Apply ERC-20 decimals from erc20_tokens when scaling for price or display.

🔑 Keys & Joins • Primary key: id • Lineage triple for joins to raw events: (genesis_block_number, tx_index, log_index) • Foreign keys: • pool_uid → liquidity_pools(uid) • (genesis_block_number, genesis_tx_index, genesis_log_index) → logs(block_number, tx_index, log_index)

🧬 Lineage & Reproducibility Every row has a verifiable path back to the originating raw events via the lineage triple and tracing graph: • tracing_id - this row’s identity • parent_tracing_ids - immediate sources • genesis_tracing_ids - original on-chain sources This supports audits and exact reprocessing to source transactions/logs/function calls.

📈 Common uses • Compute mid-price, tick-to-price conversions, and per-tick depth curves • Slippage and routing analytics for v3-style AMMs • Risk/monitoring (price jumps vs. liquidity distribution changes)

🚚 Delivery By default • WebSocket (API/WSS) reorg-aware live emissions when a new update is available; <140 ms median latency on ETH streams (7-day). • SFTP server for archives and daily End-of-Day (EOD) snapshots. • Model Context Protocol (MCP) for AI workflows (pull slices, schemas, lineage). Optional • Integrations to Amazon S3, Azure Blob Storage, Snowflake, and other enterprise platforms on request.

🗂️ Files (time-partitioned in UTC, compressed) • Parquet • CSV • XLS • JSON

💡 Quality and operations • Reorg-aware ingestion. • 99.95% uptime target with SLA. • Backfills to chain genesis. • Versioned, schema-stable datasets; changes are additive and announced.

🔄 Change policy Schema is stable. Any breaking change ships as a new version (e.g., liquidity_pools_reserves_uniswap_v3_v2) with migration notes. Content updates are additive (new rows/fields filled); types aren’t changed in place.

🟦 What this is Canonical registry of all verified Aerodrome V1 liquidity pools, normalized into the BlockDB schema for deterministic cross-chain analysis. Fees are modeled separately as versioned terms, so fee changes over time are tracked precisely and are joinable back to the pool.

This dataset provides the structural backbone for connecting Aerodrome V1 pool states with liquidity, price, and swap datasets.

Key traits: • Schema-stable, versioned, and lineage-verifiable • Deterministic pool UIDs across chains and factory contracts • Real-time and historical coverage with factory-level lineage • Fully joinable with reserves, prices, swaps, and fee-term timelines

🌐 Chains / Coverage ETH, BSC, Base, Arbitrum, Unichain, Avalanche, Polygon, Celo, Linea, Optimism (others on request). Full backfill to chain genesis with reorg-aware real-time ingestion. Covers all verified Aerodrome V1 deployments across supported EVM networks.

📑 Schema List the columns exactly as delivered. Names/types are stable.

liquidity_pools (base registry) • uid BIGINT NOT NULL - stable pool identifier (derived from address or pool_id) • tracing_id BYTEA - deterministic row-level hash (proof-of-derivation) • parent_tracing_ids BYTEA - salted hash(es) of immediate parent rows in the derivation graph • genesis_tracing_ids BYTEA - salted hash(es) of original sources (genesis of the derivation path) • genesis_block_number BIGINT NOT NULL - factory create event block • genesis_tx_index INTEGER NOT NULL - create event tx index • genesis_log_index INTEGER NOT NULL - create event log index • contract_address BYTEA NULL - 20-byte pool address (v2/v3-style) • pool_id BYTEA NULL - 32-byte pool id (v4-style / manager-based) • factory BYTEA NOT NULL - DEX factory / pool manager address • type_id INTEGER NOT NULL - pool type FK (constant-product, concentrated, stable/weighted, etc.) • pairnum NUMERIC(6) NULL - optional pair ordinal/descriptor • tokens BYTEA[] NOT NULL - array of 20-byte token addresses (order matches protocol convention) • asset_managers BYTEA[] NULL - per-token managers (e.g., Balancer) • amp NUMERIC(6) NULL - amplification for stable/weighted math • pool_type TEXT NULL - optional human-readable type label • weights NUMERIC(6,5)[] NULL - per-token weights in 0..1 (5 dp) • tick_spacing SMALLINT NULL - grid size for concentrated liquidity

liquidity_pool_fee_terms (versioned, non-overlapping) • pool_uid BIGINT NOT NULL - FK → liquidity_pools(uid) • tracing_id BYTEA - Row identity hash (proof-of-derivation) • parent_tracing_ids BYTEA - Salted hashes of contributing parents • genesis_tracing_ids BYTEA - Salted hashes of ultimate sources • genesis_block_number BIGINT NOT NULL - Fee-update event block • genesis_block_time TIMESTAMPZ NOT NULL - Fee-update event timestamp • genesis_tx_index INTEGER NOT NULL - Transaction index • genesis_log_index INTEGER NOT NULL - Log index • pool_fee NUMERIC(18,18) NOT NULL - Total fee fraction (e.g. 0.003 = 0.30 %) • user_fee_bps SMALLINT NULL - Optional user-side fee share (0–10 000) • protocol_fee_bps SMALLINT NULL - Optional protocol-side fee share (0–10 000) • fee_source TEXT NOT NULL - Provenance of fee rate (e.g. onchain:event) • fee_share_source TEXT NOT NULL - Provenance of fee split (e.g. onchain:param, docs)

Checks • At least one identifier present (contract_address or pool_id) and lengths enforced (20B/32B).

Notes • Fee terms are non-overlapping; each record defines a valid block-range. • Use liquidity_pool_fee_terms for historical fee reconstruction or to obtain the active fee at a given block.

🔑 Keys & joins • Primary key: uid • Lineage triple for joins to raw events: (genesis_block_number, genesis_tx_index, genesis_log_index) • Foreign keys: • (genesis_block_number, genesis_tx_index, genesis_log_index) → logs(block_number, tx_index, log_index) • factory → decentralized_exchanges(contract_address) • type_id → liquidity_pool_types(id)

🧬 Lineage & Reproducibility Every row has a verifiable path back to the originating raw events via the lineage triple and tracing graph: • tracing_id - this row’s identity • parent_tracing_ids - immediate sources • genesis_tracing_ids - original on-chain sources This supports audits and exact reprocessing to source transactions/logs/function calls.

📈 Common uses • Building the complete pool registry for routing and analytics • Filtering pools by fee, type, or token pair • Integrating with reserves, price, and swap datasets for liquidity intelligence • MEV routing, arbitrage path optimization, and chain-wide pool analytics • Constructing pool-level AI or quantitative features

🚚 Delivery By default • WebSocket (API/WSS) reorg-aware live emissions when a new update is available; <140 ms median latency on ETH streams (7-day). • SFTP server for archives and daily End-of-Day (EOD) snapshots. • Model Context Protocol (MCP) for AI workflows (pull slices, schemas, lineage). Optional • Int...