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Data Science AnalyticsTop 10 Best Financial Data Services of 2026
Ranked comparison of financial data services by coverage, accuracy, and delivery, including Thomson Reuters, S&P Global, Moody’s for analysts.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Moody's Corporation is the best pick if credit risk teams need issuer and debt rating-action data to keep surveillance and reporting pipelines flowing, whereas S&P Global fits when enterprise workflows need coordinated identifiers, pricing, and corporate actions in automated production flows.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Moody's Corporation
Continuous rating surveillance outputs and structured rating-action history tied to issuers and specific obligations.
Built for fits when credit risk teams need issuer and debt rating-action data for surveillance and reporting pipelines..
S&P Global
Editor pickSecurity master and corporate actions handling designed to preserve identifier consistency across updates.
Built for fits when enterprise teams need coordinated identifiers, pricing, and corporate actions in automated production flows..
London Stock Exchange Group
Editor pickEntitlement-governed distribution of LSEG listings and corporate actions content tied to shared instrument identifiers.
Built for fits when teams need governed, exchange-rooted reference and corporate events for enterprise automation..
Related reading
Comparison Table
Moody's Corporation
enterprise_vendorCredit ratings and financial data provider with analytics through Moody's Analytics.
Continuous rating surveillance outputs and structured rating-action history tied to issuers and specific obligations.
Moody's data offerings center on credit ratings and credit events, with outputs that map rating concepts to named issuers and specific obligations for downstream risk workflows. The most common fit signals are surveillance-led processes, rating-action timelines, and governance needs around how credit views change over time. Integration typically emphasizes enrichment and event attribution rather than tick-level market feed replication. Compared with broader market-data publishers, the value concentrates on credit-specific attribution and action history.
A key tradeoff is that Moody's strength is credit and credit-event data, not consolidated order book or deep exchange quote distribution. Teams that need intraday pricing feeds or venue-level market microstructure usually pair Moody's with separate market-data providers. Moody's fits best when monitoring credit spread drivers, updating credit-lifecycle views, or reconciling rating actions across internal risk systems.
- +Strong issuer and obligation linkage for credit surveillance workflows
- +Rating-action history supports audit trails in credit monitoring
- +Credit-focused reference data improves enrichment for internal portfolios
- +Enterprise delivery patterns support automated downstream refresh cycles
- –Requires governance discipline to keep credit views consistent across systems
- –Not a substitute for intraday market quotes and order book data
- –Integration effort rises when mapping to complex internal symbology
- –Event semantics need careful handling for corporate action timing
Credit risk analysts
Update credit views from rating actions
Faster credit monitoring updates
Bank collateral management
Attribute credit events to obligations
Reduced manual event triage
Show 2 more scenarios
Enterprise data teams
Enrich security master with credit attributes
More consistent security reference data
Builds consistent enrichment pipelines that attach issuer and obligation credit attributes to internal IDs.
Regulatory reporting teams
Reconcile credit ratings timelines
Tighter change management
Maintains structured timelines of rating actions for reporting narratives and change tracking.
Best for: Fits when credit risk teams need issuer and debt rating-action data for surveillance and reporting pipelines.
More related reading
S&P Global
enterprise_vendorProvider of credit ratings, market intelligence, and financial data incorporating IHS Markit.
Security master and corporate actions handling designed to preserve identifier consistency across updates.
S&P Global fits teams that need both market data feeds and the supporting reference layers that keep identifiers consistent across systems. Its offering commonly supports production ingestion via managed data delivery plus API access, which reduces custom glue when building quote, pricing, and event-based datasets. The service also aligns with enterprise governance needs through controlled access patterns and audit-ready operational behavior.
A key tradeoff is that the breadth of instruments and datasets increases integration effort when requirements are narrow or when a single trading venue feed is the only focus. S&P Global works well for use cases that require coordinated updates, such as maintaining security master alignment, reflecting corporate actions, and keeping analytics consistent across historical and current periods.
- +Enterprise coverage spanning reference data, pricing, and corporate actions
- +API and file delivery patterns support automated ingestion pipelines
- +Security master and identifier consistency reduce downstream reconciliation work
- +Governance-friendly access patterns fit controlled production environments
- –Integration setup requires more governance discipline than narrower feeds
- –Narrow use cases can feel heavier due to broad dataset scope
- –Instrument mapping requirements can add project time for custom symbology
- –Workflow depth can require dedicated integration ownership
Quant research teams
Build analytics datasets from market and reference
Fewer mapping errors and restatements
Data engineering teams
Automate enterprise market data ingestion
Higher pipeline reliability
Show 2 more scenarios
Risk and compliance teams
Monitor corporate actions and valuation inputs
Audit-ready event traceability
Maintains consistent reference updates so risk and reporting align with security changes.
Investment operations teams
Reconcile pricing and instrument identifiers
Lower operational reconciliation workload
Reduces manual reconciliation by keeping security identifiers aligned across datasets.
Best for: Fits when enterprise teams need coordinated identifiers, pricing, and corporate actions in automated production flows.
London Stock Exchange Group
enterprise_vendorFinancial markets infrastructure and data provider incorporating Refinitiv and FTSE Russell.
Entitlement-governed distribution of LSEG listings and corporate actions content tied to shared instrument identifiers.
London Stock Exchange Group is distinct in how its coverage maps to its own trading, listing, and corporate events, which helps when internal systems need consistent security reference and lifecycle event handling. Core capabilities include reference data for instrument identifiers, market data delivery in real-time and end-of-day forms, and corporate actions content designed to attach to the same security master context. The integration approach typically emphasizes structured delivery over ad hoc scraping, which supports stable ingestion into existing datastores.
A tradeoff appears when teams require a single uniform normalization layer for every venue and every vendor-style symbology, since LSEG still expects integration work for house identifiers and internal data models. LSEG fits most when an organization already runs automated feed handlers and wants governed access controls plus auditability around entitlements and change history. It also fits firms building workflows around corporate actions processing, where security mapping consistency matters more than quote-only delivery.
- +Exchange-linked reference content supports consistent instrument and event mapping
- +Corporate actions workflows align with security master identifiers for lifecycle processing
- +Governed entitlements and operational controls fit regulated data distribution
- +Multiple delivery shapes support both intraday ingestion and end-of-day pipelines
- –Venue-by-venue symbology mapping still needs internal identifier translation
- –Some normalization and enrichment requires additional configuration and QA
Enterprise market data engineering
Automated intraday ingestion across multiple venues
Lower ingestion failures
Risk analytics teams
Reference-linked pricing and corporate actions adjustments
More reliable risk factors
Show 2 more scenarios
Wealth platform data ops
Instrument master and lifecycle event enrichment
Fewer manual reconciliations
Common identifier context supports downstream orchestration of eligibility and event-aware data views.
Quant research teams
Historical series ingestion for backtesting
Cleaner research datasets
Repeatable end-of-day and reference alignment supports stable dataset construction across studies.
Best for: Fits when teams need governed, exchange-rooted reference and corporate events for enterprise automation.
FactSet
enterprise_vendorFinancial data and analytics platform serving investment professionals and asset managers.
Entitlement-aware data provisioning paired with curated security master and event processing for ongoing research workflows.
FactSet is a financial data service used by research and market teams to source both market and fundamental data through a controlled delivery workflow. FactSet’s core strengths include entitlement-aware access to curated datasets, standardized reference and instrument mapping, and automated content delivery for ongoing analytics use.
The service also supports API-driven consumption and integration patterns that reduce manual file handling in enterprise pipelines. FactSet is distinct for how deeply its data products are packaged for research workflows rather than only quote distribution.
- +Strong instrument mapping coverage across identifiers for analytics consistency
- +API and automation support for ingesting updates into downstream systems
- +Clear entitlement management workflows for controlled data access
- +Well-structured corporate actions handling for event-driven research updates
- –Enterprise integration can require more governance work than file-only providers
- –Some specialized datasets depend on add-on packages rather than core bundles
- –Large multi-entity deployments can increase operational overhead
- –Real-time coverage expectations vary by venue and subscription scope
Best for: Fits when research groups need governed market and fundamentals feeds with automation into internal tooling.
Bloomberg
enterprise_vendorGlobal provider of financial data, news, and analytics through terminal and data license services.
Bloomberg security master integration that consistently connects instrument identifiers to corporate actions and pricing across feed workflows.
Bloomberg delivers market data, reference data, and analytical content through enterprise feed services and its desktop and API access. The service is distinguished by broad instrument coverage, tight integration between identifiers, pricing, and corporate actions workflows, and strong operational tooling for entitlement management.
Data is delivered as real-time market data, delayed market data, and historical market data with multiple distribution formats suited to internal systems. Automation is supported through documented programmatic access and repeatable feed management patterns used for downstream analytics and risk processes.
- +Cross-linked security identifiers tie quotes, reference data, and corporate actions together
- +Wide coverage of global markets with consistent delivery across real-time and historical
- +Strong data governance patterns for entitlements and controlled access across teams
- +Comprehensive analytics content packaged alongside data feeds for faster research workflows
- –Enterprise feed setup can require deeper systems integration than lighter providers
- –Normalization work is still required for internal data models and symbol conventions
- –API and feed access often needs careful configuration to avoid performance bottlenecks
- –Custom workflows for specialized instruments may depend on specific product entitlements
Best for: Fits when global market data needs tight identifier consistency and controlled enterprise access across research, risk, and trading systems.
Preqin
enterprise_vendorAlternative assets data provider covering private equity, hedge funds, and private debt.
Preqin’s private markets research datasets combine fund and investor context with deal activity for end-to-end screening.
Preqin is a financial data service used for alternative asset and corporate finance research workflows that need faster sourcing than manual collection. Its core capabilities center on market intelligence datasets for private markets, deal activity, fund information, and industry benchmarks alongside structured exports.
Delivery commonly supports bulk files and API-based access patterns that feed internal research databases and screening tools. Strong coverage for private markets and corporate finance questions makes it a frequent choice for teams building deal and manager selection models.
- +Strong coverage for private markets research, including funds, investors, and deal activity
- +Structured dataset exports fit screening, benchmarking, and internal model training
- +API availability supports programmatic pull patterns for ongoing research refreshes
- +Industry-focused curation reduces manual mapping work for common workflows
- –Less suitable for streaming tick or deep order book workflows
- –Integration requires attention to identifier normalization across internal security master records
- –Automation depth depends on dataset packaging and entitlements per use case
- –Some research outputs require analyst review before model ingestion at scale
Best for: Fits when research teams need repeatable private markets data collection for screening and benchmarking.
Cboe Global Markets
enterprise_vendorExchange operator providing market data and analytics across options, equities, and futures.
Entitlement-driven access and controlled distribution for Cboe venue data products.
Cboe Global Markets delivers exchange-grade market data tied to its operating venues and trading infrastructure. Its offering is strongest for market participants needing Cboe-specific feeds plus reference content that supports consistent instrument identification across systems.
Data delivery workflows emphasize structured integration for real-time and historical use, rather than ad hoc file exports. Governance practices for entitlements and access controls fit organizations that require controlled distribution of market data products.
- +Venue-tied coverage that maps directly to Cboe execution ecosystems
- +Clear entitlement-focused access patterns for regulated data distribution
- +Structured delivery options for both real-time consumption and historical workflows
- +Reference content supports consistent instrument matching across downstream systems
- –Integration effort rises when consolidating multiple exchanges into one view
- –Some enterprise workflows depend on specialized account setup processes
- –Instrument mapping needs extra validation for non-standard internal identifiers
- –High-volume ingestion benefits from dedicated integration engineering
Best for: Fits when firms need Cboe venue feeds, controlled entitlements, and reliable historical pulls.
MSCI
enterprise_vendorProvider of index, analytics, and ESG data services for institutional investors.
MSCI factor models and methodology-governed index construction support repeatable risk and performance analytics.
MSCI delivers financial reference and analytical datasets that track global equities and fixed income across countries, sectors, and risk factors. Its distinct strength is the combination of broad instrument coverage with long-lived methodology-driven indices and factor models that feed downstream scoring and risk reporting.
Delivery patterns typically center on bulk data, structured identifiers, and analytics-ready outputs rather than a primary real-time market data channel. Integration is geared toward model and enterprise workflows that require stable identifiers, lineage-aware transformations, and controlled entitlement access.
- +Methodology-driven indices and factor analytics support consistent cross-time comparisons
- +Coverage across equity and fixed income markets supports unified reference workflows
- +Security master style identifiers reduce symbology mapping churn across systems
- +Enterprise entitlement and workflow alignment supports multi-team governance needs
- –Real-time tick and order book delivery is not the core MSCI strength
- –Higher integration effort is typical for end-to-end normalization into existing schemas
- –Granular automation for streaming updates is limited versus dedicated market data vendors
- –Advanced analytics often require careful feature parity checks across releases
Best for: Fits when analytics-led enterprises need consistent MSCI methodologies for reference, indices, and risk scoring.
Nasdaq
enterprise_vendorGlobal exchange and technology company offering market data, index data, and analytics services.
Nasdaq corporate actions and security reference content designed to keep instrument history consistent across ongoing data refresh cycles.
Nasdaq delivers market data and security-related reference services used by trading, analytics, and investment workflows. Its core capabilities center on distribution of real-time and delayed market data streams plus reference content needed to align instruments to identifiers and company entities.
Nasdaq also supports corporate actions workflows and operational delivery formats used for automated refresh and downstream ingestion. Integration is typically handled via API access and file-based delivery patterns for applications that need consistent updates and controlled entitlements.
- +Wide range of market and reference datasets tied to Nasdaq ecosystems
- +Operationally oriented delivery for scheduled refresh and programmatic ingestion
- +Corporate actions coverage supports ongoing instrument history maintenance
- +API-based access supports application automation and entitlement control
- –Coverage depth varies by asset class and requires careful entitlement planning
- –Stream onboarding can demand more integration work than file-only providers
- –Some reference normalization tasks still require downstream mapping rules
- –Governance requires disciplined monitoring across multiple feed endpoints
Best for: Fits when workflows need Nasdaq-aligned market and reference content with automated refresh and corporate-actions updates.
Wilshire
enterprise_vendorInvestment technology and analytics firm providing index, risk, and consulting data services.
Methodology-driven benchmark and portfolio datasets that preserve consistent calculation assumptions across reporting cycles.
Wilshire serves institutional organizations that need portfolio and risk workflows backed by curated investment data and analytics. Delivery centers on Wilshire’s investment research, benchmarks, and methodology-driven datasets that map to internal identifiers for consistent downstream reporting.
The service tends to work best when data governance, controlled distribution, and repeatable production processes matter as much as raw market coverage. Integration is oriented around practical file and system handoffs for analytics pipelines rather than low-latency trading feeds.
- +Benchmark and methodology-linked datasets support consistent performance measurement
- +Data outputs align with institutional reporting needs instead of ad hoc dashboards
- +Governed distribution helps keep downstream entitlement and versioning under control
- +File-based delivery fits repeatable end-of-day production workflows
- –Less suited to real-time trading use cases compared with exchange-grade data providers
- –Coverage focus skews toward institutional research outputs over broad market tick feeds
- –Integration effort is higher when internal identifier standards differ from Wilshire outputs
- –API automation surface is not the primary interface for most data delivery workflows
Best for: Fits when institutional teams need benchmark-linked datasets and governed distribution for reporting and risk analytics.
Conclusion
After evaluating 10 data science analytics, Moody's Corporation stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right financial data
Financial data services typically cover reference data, corporate actions, and pricing and can extend into issuer-linked credit surveillance outputs, security master support, and exchange-rooted event feeds. This guide compares Moody’s, S&P Global, and Thomson Reuters alongside eight other providers to map coverage breadth to automation and governance realities across enterprise workflows.
Moody’s is positioned around continuous rating surveillance outputs and structured rating-action history tied to issuers and specific obligations. S&P Global is positioned around a security master and corporate actions handling workflow designed to preserve identifier consistency as updates arrive. Thomson Reuters is evaluated alongside firms that emphasize entitlement-governed distribution and exchange-linked mappings, including LSEG and Nasdaq.
Financial data services for reference, pricing, and issuer or event-linked delivery
Financial data is the structured set of identifiers and market and corporate facts used to power pricing, portfolio analytics, risk reporting, and regulatory and internal audit trails. It usually includes security reference content, pricing data at defined frequencies, and corporate actions records that must stay synchronized with the security master over time.
Moody’s focuses on credit surveillance use cases through continuous rating surveillance outputs and a structured rating-action history tied to issuers and specific obligations. S&P Global focuses on identifier consistency across updates through security master handling and corporate actions workflows that support automated ingestion patterns for enterprise pipelines.
Financial data capabilities that determine coverage, consistency, and automation
Financial data buyers tend to fail when identifier consistency breaks across reference data, corporate actions, and pricing deliverables. This shows up as failed joins in production pipelines, mismatched instrument history, and manual remediation when entitlement changes or new listings appear.
Issuer and obligation linkage for credit surveillance pipelines
Moody’s provides continuous rating surveillance outputs and a structured rating-action history tied to issuers and specific obligations. This supports credit monitoring workflows that need issuer history and obligation-scoped actions in reporting.
Security master and corporate actions handling that preserves identifier continuity
S&P Global is strong in security master and corporate actions handling designed to preserve identifier consistency across updates. Bloomberg also connects security identifiers to corporate actions and pricing across its feed workflows.
Exchange-rooted entitlement and distribution controls for reference and events
LSEG delivers entitlement-governed distribution of listings and corporate actions content tied to shared instrument identifiers. Cboe Global Markets similarly emphasizes entitlement-driven access and controlled distribution for venue data products.
Provisioning that fits enterprise automation and internal tooling workflows
FactSet pairs entitlement-aware provisioning with curated security master and event processing for ongoing research workflows. It supports API and automation approaches for ingesting updates into downstream systems.
Methodology-governed analytics for consistent index and factor reporting
MSCI provides methodology-governed index construction and factor analytics for repeatable risk and performance comparisons. Wilshire focuses on methodology-linked benchmark and portfolio datasets that preserve consistent calculation assumptions across reporting cycles.
Private markets datasets designed for screening and benchmarking workflows
Preqin’s standout is structured private markets research datasets that include fund and investor context plus deal activity for end-to-end screening. FactSet can serve broader research automation, but Preqin is the clearer fit for repeatable screening and benchmarking workflows.
How to choose financial data services for integration depth and governance control
The primary decision is whether the target workflow needs issuer or obligation-scoped history, identifier continuity across corporate actions, or venue-governed reference and event feeds. The secondary decision is how much governance discipline and internal normalization work each provider requires once data enters production schemas.
Map the workflow join points before selecting a provider family
If credit risk teams need issuer and obligation-scoped rating-action history for surveillance reporting, Moody’s aligns with that linkage. If enterprise production pipelines need consistent joins across security master updates and corporate actions, S&P Global and Bloomberg align more directly.
Choose based on whether identifier consistency is native or needs translation
LSEG and Nasdaq both emphasize exchange-rooted corporate actions and reference content that support lifecycle processing tied to their ecosystems. When venue-by-venue symbology mapping requires internal identifier translation, internal mapping work rises for consolidated enterprise views.
Decide how much automation effort is acceptable for your delivery path
FactSet and S&P Global support API and file delivery patterns that fit automated ingestion pipelines into internal tooling. Cboe Global Markets can still work for ingestion, but consolidation across multiple exchanges increases integration effort when one view must be built across venue entitlements.
Separate streaming tick needs from research and scheduled refresh needs
Preqin is less suitable for streaming tick or deep order book workflows, so it fits structured screening and benchmarking exports rather than intraday market feeds. MSCI and Wilshire are similarly not positioned around tick and order book delivery, since they focus on methodology-driven analytics and benchmark calculations.
Validate governance controls against the number of systems that consume the data
Moody’s requires governance discipline to keep credit views consistent across systems, which matters when multiple downstream applications consume rating outputs. Bloomberg and S&P Global place heavy emphasis on identifier consistency, so governance failures still appear as internal data model mismatches and symbol convention drift.
Who should buy which financial data service and why
Different teams buy financial data for different failure points in production, such as incorrect joins, inconsistent instrument lifecycles, or missing methodology alignment. The buyer should match provider strengths to the operational bottleneck that will otherwise create manual work or reporting delays.
Credit surveillance and issuer risk teams
Moody’s fits when surveillance workflows depend on continuous rating surveillance outputs and a structured rating-action history tied to issuers and specific obligations.
Enterprise reference data, corporate actions, and downstream analytics teams
S&P Global and Bloomberg fit when production pipelines require coordinated identifiers across reference updates, corporate actions, and pricing deliverables.
Exchange ecosystem teams that need governed event and reference content
LSEG and Cboe Global Markets are well matched to entitlement-governed distribution tied to exchange-rooted listings and venue delivery patterns.
Analytics teams focused on index construction and factor methodology consistency
MSCI and Wilshire fit when consistent calculation assumptions and methodology govern index or benchmark reporting and risk analytics.
Private markets research and screening teams
Preqin is the clearer fit when fund, investor, and deal activity must be collected into structured datasets for repeatable screening and benchmarking.
Common mistakes when buying financial data services for production pipelines
Most buying errors come from treating identifier alignment, entitlement boundaries, and update workflows as secondary to raw coverage. The result is churn in integration work after onboarding when joins fail, corporate actions misalign, or governance rules are not mapped to consumer systems.
Selecting a provider based on broad market coverage but underestimating the internal identifier translation required for consolidated views
LSEG’s exchange-linked content still needs venue-by-venue symbology mapping when consolidated into one enterprise identifier set. Nasdaq coverage also varies by asset class, which increases entitlement planning and mapping work.
Assuming issuer and obligation-scoped credit surveillance can be handled by general market data feeds
Moody’s is positioned around continuous rating surveillance outputs and obligation-scoped rating-action history tied to issuers. Other providers can support research and reference pipelines, but Moody’s credit linkage is the distinguishing fit for credit monitoring reporting.
Buying private markets datasets for intraday streaming market workflows
Preqin’s strengths are structured private markets research datasets for screening and benchmarking rather than streaming tick or deep order book delivery. For real-time trading signals, the gap shows up quickly in latency and data granularity expectations.
Overlooking governance discipline when multiple systems consume the same dataset with different expectations
Moody’s requires governance discipline to keep credit views consistent across systems, which becomes visible when different consumers interpret rating history differently. Bloomberg and S&P Global also require internal normalization for symbol conventions and data model alignment even with strong identifier consistency.
How We Selected and Ranked These Providers
We evaluated Moody’s, S&P Global, Thomson Reuters, and the other listed providers on features, ease, and value, with features contributing 40% of the score and ease and value contributing 30% each. Moody’s set the pace with continuous rating surveillance outputs plus a structured rating-action history tied to issuers and specific obligations, which directly supports surveillance and audit-trail style reporting pipelines.
S&P Global rated highest in enterprise coverage across reference data, pricing, and corporate actions and maintained identifier consistency through its security master and corporate actions handling. Across the remaining providers, we weighed how strongly each one fit its named workflow in the cards, such as LSEG’s entitlement-governed distribution, FactSet’s entitlement-aware provisioning for research automation, and MSCI and Wilshire’s methodology-governed analytics.
Frequently Asked Questions About financial data
Which providers handle credit ratings surveillance and structured rating-action histories most directly?
How do integration and API patterns differ between S&P Global, Bloomberg, and FactSet for production feeds?
When are corporate actions workflows a priority, and which service best matches that operational need?
What breaks if identifier consistency across updates fails in security master operations?
Where does MSCI fall short for firms that expect primary real-time market data channels?
How should data migration be planned when moving from manual downloads to automated entitlement-governed feeds?
Which provider is best suited for private markets screening workflows that require bulk exports plus API access?
What technical requirements commonly appear when onboarding exchange-grade venue data from Cboe Global Markets and Nasdaq?
Which service is a stronger fit for methodology-driven benchmark datasets used in institutional reporting and risk?
How do admin controls and auditability concerns show up across these financial data services?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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