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Data Science AnalyticsTop 10 Best Data Analytic Software of 2026
Top 10 Data Analytic Software rankings for 2026, with side-by-side reviews of Tableau, Looker, Apache Superset, and other analytics tools.
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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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Tableau
Tableau Dashboard Stories for guided, narrative analytics with drill-down interactions
Built for teams building governed, interactive dashboards for business reporting and exploration.
Looker
Editor pickBigQuery ML for training and running models using SQL inside the warehouse
Built for analytics teams running large SQL workloads with governance and ML add-ons.
Apache Superset
Editor pickSQL Lab with saved datasets feeding interactive dashboard charts
Built for teams building governed BI dashboards with SQL-based data exploration.
Related reading
Comparison Table
This comparison table evaluates data analytic software across integration depth, including how each tool connects to warehouses, streaming sources, and metadata layers. It also compares data model options, automation and API surface for provisioning and extensibility, and admin and governance controls like RBAC, audit logs, and schema governance to show tradeoffs that affect throughput and operations. The entries are selected from top deployments that span BI platforms and Apache-based analytics engines.
Tableau
BI visualizationDelivers interactive analytics dashboards and visual analysis with governed sharing and self-service exploration.
Tableau Dashboard Stories for guided, narrative analytics with drill-down interactions
Tableau stands out for turning connected data into interactive dashboards through a drag-and-drop visual workflow. It supports strong end-user analysis with calculated fields, filters, parameters, and story-driven presentations.
Tableau also excels at serving governed analytics via Tableau Server and embedding dashboards into external experiences. Its performance and usability vary based on data modeling quality and the complexity of highly interactive views.
- +Interactive dashboards built quickly with drag-and-drop visual design
- +Robust calculation language with parameters, sets, and level-of-detail expressions
- +Strong data connectivity across common warehouses and databases
- –Complex dashboards can slow down without careful data modeling and extract strategy
- –Governance and permissions require deliberate configuration for large teams
- –Advanced analytics needs external tooling for predictive modeling workflows
Marketing analytics and experimentation teams
Analyze campaign funnel across segments
Identify highest-converting audience segments
Finance reporting and FP&A teams
Forecast and variance analysis dashboards
Reduce time to variance explanations
Show 2 more scenarios
Operations and supply chain leaders
Monitor inventory and delivery performance
Spot delivery risks sooner
Connect operational data and publish governed dashboards with role-based access controls on Tableau Server.
Data teams building BI governance
Publish reusable metrics with semantic models
Improve metric consistency across teams
Standardize metrics through modeled datasets and distribute interactive views in managed web environments.
Best for: Teams building governed, interactive dashboards for business reporting and exploration
More related reading
Looker
semantic BIProvides a semantic-model-driven analytics platform that enables governed reporting, dashboards, and embedded analytics on a unified data layer.
BigQuery ML for training and running models using SQL inside the warehouse
Google BigQuery stands out for its serverless, columnar data warehouse that runs SQL directly on massive datasets. It supports fast analytics with built-in columnar storage, slot-based concurrency, and tight integration with streaming ingestion, batch loads, and data governance controls. Data teams can combine BI-friendly SQL with machine learning via BigQuery ML and connect to external engines through supported interfaces.
- +Serverless warehouse with near-elastic concurrency for large SQL workloads
- +Columnar storage and vectorized execution improve scan and aggregation performance
- +Built-in streaming ingestion and batch loads support near-real-time analytics
- –SQL-first workflow can require data modeling expertise for cost control
- –Ecosystem complexity rises when combining IAM, datasets, and governance settings
- –Advanced optimization often needs partitioning and clustering discipline
Best for: Analytics teams running large SQL workloads with governance and ML add-ons
Apache Superset
open-source BIOffers web-based interactive analytics with SQL-based exploration, charting, and dashboarding backed by multiple database engines.
SQL Lab with saved datasets feeding interactive dashboard charts
Apache Superset stands out by combining an open analytics web UI with a modular data backend via database connections and SQL. It supports dashboards with interactive charts, SQL Lab for ad hoc querying, and dataset-driven exploration across many data sources.
Built-in authentication and permission controls enable team sharing while keeping data access centralized. The visualization library covers common chart types and supports customizations through dashboards, filters, and SQL-powered datasets.
- +Rich dashboarding with interactive filters and drilldowns
- +SQL Lab supports fast ad hoc exploration and reusable datasets
- +Broad datasource support through standard database connectors
- +Role-based access controls for sharing curated content
- –Dashboards and permissions can be complex to model
- –Performance tuning often requires manual configuration and optimization
- –Advanced custom visuals may require development effort
Revenue ops analysts
Analyze churn and pipeline funnel metrics
Faster insight into funnel dropoffs
Business intelligence teams
Share governed dashboards across departments
Lower risk of unauthorized data
Show 2 more scenarios
Data engineers
Validate models using ad hoc SQL
Earlier detection of transformation issues
Superset dataset definitions and SQL Lab support rapid checks against warehouse tables.
Product managers
Monitor feature adoption with charts
Clear view of feature usage
Interactive visualizations and dashboard filters help track adoption by cohort and release.
Best for: Teams building governed BI dashboards with SQL-based data exploration
Apache Spark
distributed analytics engineRuns distributed data processing for analytics workloads using in-memory computation for ETL, machine learning pipelines, and large-scale transforms.
Catalyst optimizer with whole-stage code generation for DataFrame and SQL queries
Apache Spark stands out for its unified engine that supports batch processing, streaming, and machine learning workloads with the same core runtime. It delivers high-performance distributed data processing through resilient distributed datasets and DataFrame and SQL APIs optimized by a Catalyst query optimizer. Spark also supports scalable ML workflows via MLlib and integrates with common storage and compute backends for building end-to-end analytics pipelines.
- +Unified APIs for batch, streaming, SQL, and ML
- +Catalyst optimizer and Tungsten execution improve analytical query performance
- +Large ecosystem for connectors, data sources, and deployment integrations
- –Cluster tuning and shuffle management require strong engineering skills
- –Operational complexity increases with large streaming and stateful jobs
- –Debugging performance issues can be difficult without deep Spark knowledge
Best for: Teams building large-scale ETL and analytics pipelines on distributed clusters
Apache Flink
stream analyticsProvides stateful stream and batch processing for real-time analytics using event-time semantics and scalable distributed execution.
Event-time processing with watermarks and stateful windowing for out-of-order events
Apache Flink stands out for streaming-first analytics with event-time processing and stateful operators. It supports both streaming and batch workloads using the same runtime, with checkpoints for fault tolerance and exactly-once state consistency.
Rich APIs for Java and Scala enable custom transformations, joins, windowing, and iterative patterns with low latency processing. Built-in connectors and SQL support help operationalize pipelines without abandoning the Flink execution model.
- +Event-time windows and watermarks enable correct out-of-order streaming analytics
- +Stateful processing with checkpoints provides strong failure recovery behavior
- +SQL and Table API accelerate common analytics without building full pipelines
- +Exactly-once processing support with end-to-end state consistency for streaming jobs
- –Operational tuning of state, checkpoints, and backpressure requires expertise
- –Complex event-time semantics can raise debugging difficulty for pipeline failures
- –Advanced use cases often demand deeper knowledge than basic ETL tools
Best for: Teams building low-latency streaming analytics with event-time correctness
Google BigQuery
serverless warehouseProvides a serverless analytics data warehouse for running fast SQL queries across large datasets with built-in integrations for BI and ML.
BigQuery ML for training and running models using SQL inside the warehouse
Google BigQuery stands out for its serverless, columnar data warehouse that runs SQL directly on massive datasets. It supports fast analytics with built-in columnar storage, slot-based concurrency, and tight integration with streaming ingestion, batch loads, and data governance controls. Data teams can combine BI-friendly SQL with machine learning via BigQuery ML and connect to external engines through supported interfaces.
- +Serverless warehouse with near-elastic concurrency for large SQL workloads
- +Columnar storage and vectorized execution improve scan and aggregation performance
- +Built-in streaming ingestion and batch loads support near-real-time analytics
- –SQL-first workflow can require data modeling expertise for cost control
- –Ecosystem complexity rises when combining IAM, datasets, and governance settings
- –Advanced optimization often needs partitioning and clustering discipline
Best for: Analytics teams running large SQL workloads with governance and ML add-ons
Grafana
observability analyticsCreates time series dashboards and operational analytics with alerts using data sources such as Prometheus, Loki, and time series databases.
Grafana Unified Alerting with rule groups and alert evaluations from dashboard queries
Grafana stands out for turning time-series and metric data into interactive dashboards with drill-downs, annotations, and reusable components. It integrates tightly with major data sources through query plugins and supports alerting so dashboards can drive operational workflows. Strong panel customization, transformations, and dashboard versioning help teams keep visual analytics consistent across environments.
- +Rich dashboard building with flexible panels, variables, and drill-down links
- +Powerful alerting on queries with routing for operational workflows
- +Extensive data source integrations through plugins and query adapters
- +Strong time-series focus with transformations and query-side optimizations
- –Dashboard design can become complex with many variables and transformations
- –Advanced query authoring often requires deep knowledge of each backend
- –Non-time-series analytics needs extra modeling and may feel limited
- –Managing permissions and shared assets across many dashboards adds overhead
Best for: Teams monitoring systems and analyzing operational metrics with interactive dashboards
IBM Cognos Analytics
enterprise BICognos Analytics delivers self-service analytics with reports, dashboards, and governed metrics over relational data sources.
Row-level security that restricts dashboard and report access by user roles
IBM Cognos Analytics stands out for governed enterprise reporting combined with self-service analytics and dashboarding. It supports interactive dashboards, scheduled reporting, and narrative-style insights driven by data models.
Authoring tools connect to common enterprise data sources and can apply row-level security for controlled sharing. Strong metadata management and workflow-friendly delivery make it a central analytics layer for organizations with existing BI governance.
- +Enterprise-grade governed reporting with dashboards and scheduled delivery
- +Robust data modeling and metadata support for consistent metrics
- +Row-level security enables controlled sharing across business groups
- +Works with relational sources and integrates into existing BI ecosystems
- –Power-user configuration can be complex for smaller teams
- –Modeling and permission setup can slow initial time to value
- –Advanced analytics workflows may feel heavier than lightweight BI tools
Best for: Organizations needing governed BI dashboards and reporting without custom coding
SAS Visual Analytics
enterprise analyticsSAS Visual Analytics supports interactive data exploration, guided analysis, and dashboard creation for analytic reporting.
Visual Analytics’ interactive linked analysis with drill paths and dynamic filters
SAS Visual Analytics focuses on turning governed SAS and enterprise data into interactive dashboards with guided exploration. It supports analytic storytelling via report objects like filters, data-driven insights, and calculated items that work directly inside the visual workspace.
Strong administrative controls and integrated SAS analytics enable consistent metrics across reports. Visual exploration and collaboration exist, but building complex logic can still feel SAS-centric and less flexible than some pure-play BI tools.
- +Enterprise-grade governance through SAS-backed data and metadata
- +Interactive dashboards with cross-filtering and responsive report objects
- +Built-in calculated items and parameters for reusable analytic logic
- +Strong collaboration with shared report collections and controlled access
- –Advanced modeling often depends on SAS-centric workflows
- –Designing complex dashboards can require deeper platform knowledge
- –User experience can lag behind more modern self-serve BI interfaces
Best for: Organizations needing governed, SAS-integrated analytics dashboards for decision teams
Zoho Analytics
self-service BIZoho Analytics builds and shares dashboards and reports from connected data sources with a SQL-like query layer.
Zoho Analytics Zoho CRM dashboards with drill-down reporting and scheduled distribution
Zoho Analytics stands out with deep Zoho ecosystem connectivity, including native handling for Zoho CRM and Zoho Books data. The platform supports dashboard creation, scheduled report distribution, and analytics across SQL sources and spreadsheets.
Interactive dashboards include drill-down, calculated fields, and role-based access controls to limit visibility by user group. Built-in data preparation and query building reduce the effort required to standardize and visualize data from multiple systems.
- +Strong Zoho connector coverage for faster reporting from CRM and books data
- +Dashboard drill-through and calculated fields enable analyst-grade exploration
- +Scheduled reports and alerts support repeatable distribution workflows
- –Advanced modeling and customization lag behind dedicated BI specialists
- –Dashboard performance can degrade with complex calculations and large datasets
- –Workflow depth for governance and automation trails more enterprise BI stacks
Best for: Teams needing Zoho-friendly dashboards, scheduled reporting, and controlled access
Conclusion
After evaluating 10 data science analytics, Tableau 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 Data Analytic Software
This buyer's guide covers data analytic software tools including Tableau, Looker, Apache Superset, Apache Spark, Apache Flink, Google BigQuery, Grafana, IBM Cognos Analytics, SAS Visual Analytics, and Zoho Analytics. It maps integration depth, data model mechanics, automation and API surface, and admin and governance controls to concrete capabilities in each tool.
Readers can compare governed interactive dashboards in Tableau, Looker, and Apache Superset against warehouse-first SQL analytics in Google BigQuery and semantic-model-driven reporting in Looker. The guide also contrasts operational observability in Grafana with distributed processing engines like Apache Spark and event-time streaming correctness in Apache Flink.
Analytics software that turns connected data into governed dashboards, SQL exploration, and analytic workflows
Data analytic software provides an interactive analytics layer that connects to data sources, defines a data model or semantic layer, and renders governed views for reporting, exploration, and monitoring. It solves problems like metric consistency, controlled sharing, repeatable dashboard logic, and self-service query interfaces.
Tableau fits teams that need drag-and-drop dashboard authoring plus calculated fields, parameters, and story-driven Tableau Dashboard Stories for guided drill-down. Looker fits teams that enforce metric definitions through LookML on top of warehouse data connected to BigQuery, then delivers dashboards and embedded analytics using that unified semantic layer.
Evaluation criteria centered on integration, data model control, and governance automation
Integration depth determines whether dashboards and models stay consistent across warehouses, BI ecosystems, and embedded app surfaces. Data model control determines whether metrics and joins can be reused safely instead of redefined in every dashboard.
Automation and API surface controls whether scheduling, exports, and provisioning can be driven by workflows rather than manual clicks. Admin and governance controls determine whether RBAC and access rules can be configured and audited at scale.
Semantic layer or metric logic that centralizes definitions
Looker uses LookML to centralize business logic for metrics like revenue, churn, and cohorts so dashboards reuse the same semantic model. IBM Cognos Analytics emphasizes robust data modeling and metadata support, while Tableau provides governed calculation constructs like sets and level-of-detail expressions for metric logic reuse.
Proven mechanisms for governed sharing and row-level access
IBM Cognos Analytics supports row-level security to restrict dashboard and report access by user roles. Tableau and Apache Superset both provide permissions for sharing curated content, but Tableau also requires deliberate configuration for large teams and complex dashboards.
Dashboard interaction model with drill-down and guided narratives
Tableau Dashboard Stories provides guided, narrative analytics with drill-down interactions, which supports controlled exploration paths. Apache Superset combines SQL Lab with saved datasets feeding interactive dashboard charts, and Grafana offers drill-down links from panels with time-series focused variable controls.
Automation and scheduled delivery built into reporting workflows
Grafana supports alerting that triggers operational workflows through Grafana Unified Alerting rule groups and dashboard query evaluations. Zoho Analytics supports scheduled report distribution and alerts for repeatable delivery, while IBM Cognos Analytics supports scheduled reporting delivery for governed enterprise use.
Extensibility and API-driven compute integration paths
Apache Spark exposes unified DataFrame and SQL APIs optimized by the Catalyst optimizer for building analytics workloads that can connect into broader pipelines. Apache Flink exposes rich Java and Scala APIs for stateful stream transformations with SQL support through Table API patterns, which supports automation of real-time analytics jobs.
Data processing correctness controls for complex event and distributed workloads
Apache Flink provides event-time processing with watermarks and stateful windowing for correct out-of-order streaming analytics. Apache Spark provides Catalyst optimizer features like whole-stage code generation to improve analytical query performance on DataFrame and SQL workloads.
Choose the analytics tool that matches data model ownership and governed delivery needs
Start with how metric logic and schemas will be owned, because tools differ on whether business metrics live in a semantic layer, calculated fields, or SQL datasets. Then confirm the governance model, because row-level security and RBAC setup impacts time to deliver controlled dashboards.
Finally, validate automation and operational control paths such as scheduled reporting, exports, and alert evaluations, because those determine whether analytics can run as part of data operations rather than ad hoc work.
Assign the source of truth for metrics and joins
If metric definitions must be reused consistently across dashboards and embedded analytics, pick Looker because LookML centralizes business logic and dashboards reuse that unified semantic layer. If calculation logic must live close to interactive authoring, pick Tableau because it offers calculated fields, parameters, sets, and level-of-detail expressions for repeatable dashboard logic.
Select the governance control that matches required access granularity
If users need access restricted at the row level, pick IBM Cognos Analytics because it supports row-level security for roles. If the requirement is governed permissions over curated dashboards and shared assets, pick Tableau or Apache Superset because both support authentication and permission controls for sharing while keeping data access centralized.
Map interaction requirements to the dashboard execution model
If guided drill-down and narrative exploration are required, pick Tableau because Tableau Dashboard Stories supports guided, narrative analytics with drill-down interactions. If the requirement centers on SQL-based ad hoc exploration feeding reusable dashboard charts, pick Apache Superset because SQL Lab with saved datasets drives interactive dashboard charts.
Confirm whether automation and alert-driven workflows are core or optional
If operational monitoring needs query-driven alerts with routing behavior, pick Grafana because Grafana Unified Alerting evaluates dashboard queries with rule groups. If repeatable scheduled reporting and controlled distribution are required for business teams, pick IBM Cognos Analytics or Zoho Analytics because both support scheduled reporting and distribution workflows.
Choose the compute and correctness engine for the analytics workload type
If the workload is large-scale ETL, analytics, or ML on distributed clusters, pick Apache Spark because Catalyst optimizer features and unified batch, streaming, SQL, and ML APIs support that end-to-end pattern. If the workload is low-latency streaming with event-time correctness for out-of-order data, pick Apache Flink because watermarks and stateful windowing provide correct event-time processing.
Use warehouse-first SQL when the main execution plane is the warehouse
If SQL workloads need near-elastic concurrency and built-in streaming ingestion with ML inside the warehouse, pick Google BigQuery because it provides serverless columnar execution, slot-based concurrency, and BigQuery ML. If the governance requirement also needs a semantic modeling layer on top of that warehouse SQL, pick Looker because LookML sits above warehouse datasets such as BigQuery.
Who each analytics tool fits based on data model ownership and operational needs
Different analytics tools fit different data ownership models, dashboard interaction styles, and automation requirements. The best fit depends on whether governed metrics are authored in a semantic layer, constructed as interactive calculations, or produced by streaming and distributed processing jobs.
The sections below map the most direct best-fit audiences from the tool profiles to practical implementation expectations.
Business reporting teams that require governed interactive dashboards and narrative drill-down
Tableau is a strong match because Tableau Dashboard Stories delivers guided narrative analytics with drill-down interactions and Tableau Server supports governed sharing. Apache Superset also fits teams that want governed BI dashboards with interactive filters and drilldowns powered by SQL Lab saved datasets.
Analytics teams enforcing metric consistency with a semantic model over warehouse data
Looker fits teams that want LookML to centralize metric definitions and reuse them across dashboards and embedded analytics. Google BigQuery fits teams that run large SQL workloads with governance controls and also want BigQuery ML for training and running models using SQL inside the warehouse.
Platform and data engineering teams building distributed analytics pipelines at scale
Apache Spark fits teams running large-scale ETL and analytics pipelines because it offers unified batch, streaming, SQL, and ML APIs with Catalyst optimization and whole-stage code generation. Apache Flink fits teams building low-latency streaming analytics with correct event-time handling using watermarks and stateful windowing.
Operations teams monitoring systems with query-driven alerts and time-series dashboards
Grafana fits operational monitoring needs because Grafana Unified Alerting evaluates dashboard queries using rule groups and supports interactive drill-down from panels. Grafana is best when time-series data sources like Prometheus and Loki are already part of the stack.
Enterprise reporting groups that need governed dashboards plus role-based access and row-level restrictions
IBM Cognos Analytics fits organizations that need governed enterprise reporting with row-level security for controlled sharing and metadata-managed metrics. SAS Visual Analytics fits organizations that want governed dashboards tied to SAS-backed data and metadata and interactive linked analysis with drill paths.
Common failure modes when governance, data modeling, and automation are treated as afterthoughts
Several recurring issues appear across these tools when teams start authoring dashboards before deciding how metric logic, permissions, and automation will be managed. These mistakes increase rework during rollout and degrade performance when dashboards grow in complexity.
The corrective actions below name tools that handle the issue well and tools that require tighter discipline.
Authoring complex interactive dashboards without a modeling and extract strategy
Tableau dashboards can slow down when interactions become highly complex without careful data modeling and extract strategy, so dashboard performance needs explicit planning in Tableau. Apache Superset dashboards and permissions can also become complex to model, so dashboard structure and permission mapping should be designed early.
Treating SQL-first analytics as purely ad hoc when cost and governance controls depend on modeling
Looker and BigQuery both can require modeling expertise for cost control, and Looker adds ongoing LookML maintenance as metrics and joins evolve. Apache Spark also requires engineering discipline for cluster tuning and shuffle management when workloads scale.
Skipping row-level access requirements until late in the rollout plan
IBM Cognos Analytics provides row-level security by user roles, so access granularity needs to be captured before dashboards and reports are published. Tableau and Apache Superset can enforce permissions, but complex permission modeling can slow teams when requirements are discovered late.
Ignoring event-time semantics for streaming analytics that must handle out-of-order events
Apache Flink is built around event-time processing with watermarks and stateful windowing, and skipping those concepts causes incorrect results for out-of-order events. Grafana can visualize and alert on time-series data but it is not a substitute for event-time correctness when streaming correctness is required.
Assuming operational alerting will match dashboard visuals without validating alert evaluation behavior
Grafana Unified Alerting evaluates dashboard queries with rule groups, so alert behavior must be validated against the actual query logic. Tableau and Apache Superset support interactive exploration, but operational alert evaluation is more naturally implemented in Grafana.
How We Selected and Ranked These Tools
We evaluated Tableau, Looker, Apache Superset, Apache Spark, Apache Flink, Google BigQuery, Grafana, IBM Cognos Analytics, SAS Visual Analytics, and Zoho Analytics using the provided feature ratings, ease-of-use ratings, and value ratings. We scored each tool using features as the largest contributor at 40% and used ease of use and value each at 30% to shape the overall rating.
This editorial ranking is based on the included capability descriptions such as LookML semantic modeling in Looker, row-level security in IBM Cognos Analytics, SQL Lab saved datasets in Apache Superset, Catalyst optimizer features in Apache Spark, event-time watermarks in Apache Flink, and Grafana Unified Alerting. Tableau stands apart in this set through Tableau Dashboard Stories for guided narrative analytics with drill-down interactions, which lifts both usability for interactive exploration and dashboard feature depth for governed sharing workflows.
Frequently Asked Questions About Data Analytic Software
How do Tableau, Looker, and Apache Superset handle the definition of business metrics across teams?
Which tool best supports interactive dashboard building on a warehouse without ad hoc spreadsheet workflows?
What integration paths and API capabilities matter for embedding analytics in external apps?
How do admin controls and RBAC models differ across Tableau Server, Apache Superset, and Grafana?
How do data migration workflows differ when moving from a spreadsheet-heavy workflow to a governed BI setup?
Which platforms are better suited for event-time correct streaming analytics and why?
When teams need governance and large-scale SQL performance, how do BigQuery, Spark, and Looker compare?
What security controls should be evaluated for row-level access and auditability?
How do admin and developer extension points differ between Superset, Spark, and Grafana?
Which tool fits teams that already operate inside the SAS or Zoho ecosystems without rewriting data pipelines?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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