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Data Science AnalyticsTop 10 Best Data And Analytics Software of 2026
Top 10 Data And Analytics Software ranking for modern warehouses and BI, comparing strengths and tradeoffs for teams, including BigQuery, Redshift, Fabric.
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%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Google BigQuery
Materialized views for accelerating repeated queries with automatic maintenance
Built for teams running large-scale SQL analytics with streaming ingestion and governance..
Amazon Redshift
Editor pickWorkload Management with query queues and monitoring for workload isolation
Built for organizations running SQL analytics on AWS with strong data engineering support.
Microsoft Fabric
Editor pickOneLake provides a shared data foundation across Lakehouse and Warehouse experiences in Fabric
Built for teams building governed analytics with Lakehouse, streaming, and BI in Microsoft ecosystems.
Related reading
Comparison Table
This comparison table evaluates modern data and analytics platforms for analytics workloads and BI, focusing on integration depth with data sources and external tools. Each row contrasts the data model and schema behavior, then maps automation and API surface for provisioning and workflow control. Admin and governance controls such as RBAC, audit logs, and configuration boundaries are listed alongside throughput and extensibility so tradeoffs are visible.
Google BigQuery
cloud data warehouseServerless cloud data warehouse that runs interactive analytics and batch SQL on structured and unstructured data.
Materialized views for accelerating repeated queries with automatic maintenance
Google BigQuery stands out for its SQL-first analytics engine with serverless scalability and fast ad hoc query performance. It supports large-scale data warehousing with partitioning, clustering, and materialized views for efficient reads.
Batch and streaming ingestion work with Google Cloud storage and Pub/Sub, enabling near real-time analytics. Built-in BI integrations and strong governance features like column-level security support analytics for multiple teams.
- +Serverless SQL analytics with automatic scaling for unpredictable workloads
- +Partitioning, clustering, and materialized views reduce scanned data and speed queries
- +Streaming ingestion via Pub/Sub enables near real-time analytics pipelines
- +Column-level security and fine-grained IAM simplify secure multi-team sharing
- –Cost and performance depend heavily on query patterns and data layout
- –Advanced optimization requires expertise in partitions, clustering, and execution plans
- –Cross-project governance can feel complex in larger organizations
- –Some operational tasks require more hands-on tuning than managed warehouses
Data engineering teams
Build partitioned, clustered warehouse pipelines
Faster analytics for downstream users
Security and governance leads
Enforce column-level security across teams
Governed self-serve reporting
Show 2 more scenarios
Product analytics analysts
Run near real-time KPI queries
Timely KPI visibility
Analysts ingest streaming events and query latest metrics without managing streaming infrastructure.
Platform architects
Optimize repeated reads with materialized views
Lower dashboard query latency
Architects precompute common aggregations so BI dashboards scan fewer bytes during frequent refreshes.
Best for: Teams running large-scale SQL analytics with streaming ingestion and governance.
More related reading
Amazon Redshift
cloud data warehouseManaged cloud data warehouse that supports fast analytics with columnar storage and SQL-based querying.
Workload Management with query queues and monitoring for workload isolation
Amazon Redshift stands out for running columnar analytics data warehouses on AWS with fast SQL performance. It supports federated queries, materialized views, and workload management to balance concurrency across analytical users.
It integrates tightly with AWS data services for ingestion pipelines and governs access with IAM. Operational features like automated maintenance and backups reduce warehouse administration overhead.
- +Columnar storage and vectorized execution accelerate large analytical SQL workloads.
- +Workload management routes queries to queues for predictable concurrency control.
- +Materialized views support faster aggregates without manual summary table management.
- +Supports federated queries to query external data sources from SQL.
- –Schema design and distribution keys require tuning for best performance.
- –Complex ETL and data modeling still demand strong warehouse expertise.
- –Concurrency spikes can still cause queue contention under heavy mixed workloads.
Data warehouse teams in enterprises
Centralize analytics across multiple business units
Fewer silos, faster reporting
BI and analytics engineers
Build semantic layers with materialized views
Lower dashboard latency
Show 2 more scenarios
Platform teams on AWS
Federate queries across data sources
Reduce ETL workload
Federated queries access external systems without fully copying data into the warehouse first.
Security and data governance teams
Control access with IAM and policies
Tighter data access control
IAM controls database access for roles that query schemas, views, and underlying tables.
Best for: Organizations running SQL analytics on AWS with strong data engineering support
Microsoft Fabric
unified analyticsUnified analytics platform that provides data engineering, data warehousing, real-time analytics, and BI experiences.
OneLake provides a shared data foundation across Lakehouse and Warehouse experiences in Fabric
Microsoft Fabric unifies data engineering, data science, real-time analytics, and BI in one tenant using shared metadata and workspace-scoped resources. The platform pairs Lakehouse and Warehouse capabilities with pipeline orchestration and governance features like lineage and access controls.
Users can build reports with Power BI semantics while using Fabric notebooks, Spark workloads, and eventing to connect operational data to analytics. Fabric also supports continuous ingestion and streaming use cases through native Spark streaming patterns and integration with Microsoft ecosystems.
- +Unified workspace connects Lakehouse engineering and Power BI reporting
- +Native Spark-based notebooks enable end-to-end transformations
- +Integrated governance adds lineage and consistent access across assets
- +Real-time ingestion and streaming analytics support operational dashboards
- –Learning curve rises for Fabric-specific concepts and resource boundaries
- –Complex governance configurations can require careful planning
- –Some advanced modeling workflows still depend on Power BI expertise
- –Cost visibility can be difficult when workloads span multiple engines
Data engineering teams
Build governed lakehouse pipelines
Fewer pipeline incidents and rework
Data scientists and analysts
Train models and serve features
Faster experimentation cycles
Show 2 more scenarios
Real-time analytics teams
Stream events into analytical models
Timelier operational dashboards
Native streaming patterns move operational events into Lakehouse for low-latency reporting.
BI and reporting teams
Create Power BI semantic layers
Consistent metrics across reports
Fabric enables report authors to reuse warehouse and lakehouse datasets with consistent semantics.
Best for: Teams building governed analytics with Lakehouse, streaming, and BI in Microsoft ecosystems
Snowflake
cloud data platformCloud data platform that supports SQL analytics, data sharing, and workload separation across warehouses.
Data sharing via secure, governed streams between Snowflake accounts
Snowflake stands out for a cloud-native data platform that separates compute from storage for flexible scaling. It supports SQL-based analytics, ELT data loading, and governed data sharing across organizations. Built-in features like time travel, automatic clustering, and robust security controls help teams manage change and protect data across the analytics lifecycle.
- +Compute and storage separation enables independent scaling for analytics workloads
- +Native features like time travel and zero-copy cloning accelerate development iterations
- +Cross-account data sharing supports secure distribution without copying datasets
- +Strong governance tooling includes role-based access controls and audit visibility
- –Advanced tuning and warehouse design choices require experienced data engineering
- –Cost and performance management can be complex for highly variable query patterns
- –Operational workflows can feel heavyweight for small, simple analytics deployments
Best for: Enterprises unifying governed data for analytics, ML, and secure cross-team sharing
Databricks Lakehouse Platform
lakehouse analyticsLakehouse analytics platform that combines data engineering, ML, and collaborative Spark-based analytics.
Delta Lake with ACID transactions for large-scale analytics and streaming updates
Databricks Lakehouse Platform brings data engineering, analytics, and machine learning together on a lakehouse architecture designed for reliable ACID operations on data stored in cloud object storage. It supports unified governance across SQL, notebooks, and Spark workloads with Delta Lake tables as the central abstraction for batch and streaming data. The platform scales from ad hoc exploration to production pipelines with managed Spark execution, job orchestration, and optimized performance features for large datasets.
- +Delta Lake ACID tables support both batch and streaming workloads
- +Unified engine accelerates SQL, notebooks, and Spark jobs in one workspace
- +Lakehouse governance features support auditing, lineage, and secure sharing
- –Operational complexity increases with larger clusters and advanced optimizations
- –Workflow design often requires strong Spark and data modeling knowledge
- –Cost and performance tuning can be nontrivial for unpredictable query patterns
Best for: Teams standardizing lakehouse data pipelines, SQL analytics, and ML workloads
Apache Superset
open-source BIWeb-based analytics and BI dashboard tool that enables interactive charts, SQL exploration, and sharing.
Row-level security using user roles and dataset permissions
Apache Superset stands out with a web-based analytics UI that supports ad hoc exploration and governed dashboard publishing from the same workspace. It delivers interactive dashboards, SQL-based dataset creation, and a wide visualization library backed by native query execution.
Superset also supports row-level security and reusable dashboard components, which helps teams standardize reporting across multiple data sources. Built-in integrations for common databases and data warehouses make it suitable for embedding analytics into internal reporting workflows.
- +Rich visualization catalog supports exploratory analysis and consistent reporting
- +SQL lab enables flexible data modeling before dashboard creation
- +Row-level security enables controlled access for multi-tenant reporting
- +Dashboard filters and drilldowns support interactive self-service discovery
- –Building complex metrics can require deeper SQL and semantic layer knowledge
- –Admin setup for connections and permissions adds operational overhead
- –Performance tuning often depends on underlying database and query design
Best for: Teams building interactive BI dashboards with SQL workflows and governed access
Metabase
self-serve BISelf-hosted and cloud BI tool that turns datasets into dashboards with SQL and question-based exploration.
Question and Dashboard Builder with natural-language queries over semantic models
Metabase stands out for fast, web-based analytics that connects directly to common databases without requiring extensive data modeling. It supports interactive dashboards, ad hoc questions via a natural-language query box, and scheduled report delivery for recurring visibility.
Core capabilities include SQL-based querying, model-layer transformations, chart customization, and team sharing with permissions. It also offers alerting and embedded analytics for distributing insights inside internal apps.
- +Natural-language questions speed up exploratory analysis and dashboard ideation
- +Strong SQL support with query editing, variables, and reusable models
- +Shareable dashboards with role-based permissions and embedded views
- +Scheduling and notifications keep stakeholders aligned on key metrics
- –Complex transformations can become harder to manage than full semantic layers
- –Advanced governance features like fine-grained lineage remain limited
- –High concurrency workloads can feel constrained compared to enterprise BI stacks
Best for: Teams needing quick BI dashboards, SQL depth, and easy collaboration
Qlik Sense
enterprise BIAssociative analytics BI platform that supports interactive data discovery and governed dashboard development.
Associative data indexing and selections in Qlik Sense power relationship-driven investigation
Qlik Sense stands out with associative analytics that connects selections across fields to reveal relationships. It provides self-service dashboards, interactive visualizations, and governed data modeling for business users.
Built-in connectors and load scripting support ingestion from common data sources and transformation before analysis. The platform also supports embedding analytics into applications through APIs and managed shares.
- +Associative model enables fast discovery across related data without rigid query paths
- +Strong visual exploration with dynamic filters and selections across app sheets
- +Governed data modeling helps maintain consistent metrics and reusability
- +Robust load scripting supports repeatable data transformations during ingestion
- –Data modeling and scripting add complexity for teams without analytics engineers
- –Performance can degrade with large in-memory datasets and heavy selections
- –Collaboration features require careful governance to avoid metric inconsistencies
- –Advanced charting and layout controls take practice for consistent design
Best for: Organizations needing associative, governed analytics for self-service exploration
Tableau
visual BIVisual analytics platform for building interactive dashboards, publishing views, and connecting to many data sources.
Tableau’s Data Model and calculated fields with level of detail expressions
Tableau stands out for turning governed business data into interactive dashboards through drag-and-drop authoring. It connects to many data sources, supports calculated fields, and enables visual exploration with filters, parameters, and drill-downs.
Tableau’s analytics workflow includes publishing governed workbooks, sharing views, and collaborating via comments and subscriptions. The platform also includes server-side capabilities for monitoring, performance tuning, and controlled access.
- +Interactive dashboard authoring with powerful visual filtering and drill-down
- +Broad connectivity across databases, spreadsheets, and cloud data sources
- +Strong governance via Tableau Server permissions and workbook management
- +Live visual analytics supports rapid exploration without custom code
- –Advanced modeling can become complex when handling many data relationships
- –Large workbook performance depends heavily on data extracts and indexing choices
- –Self-service can lead to metric inconsistency without clear semantic standards
- –Calculations and table logic can be harder to maintain in complex views
Best for: Organizations building governed BI dashboards with strong visual exploration needs
Looker
semantic BIAnalytics and semantic modeling platform that uses LookML to standardize metrics and power BI dashboards.
LookML semantic layer for reusable metric definitions and governed data modeling
Looker stands out for its LookML semantic modeling layer that standardizes metrics and dimensions across dashboards and explores. It supports governed self-service analytics through curated datasets, parameterized explores, and drill paths built on a consistent model.
Embedded analytics and operational reporting are supported via APIs and scheduled delivery workflows. Its core analytics experience centers on interactive exploration, reusable definitions, and permission-aware access tied to data sources.
- +LookML enforces consistent metrics across reports and explorers
- +Strong governed self-service via role-based access controls
- +Reusable explores and dashboard components speed up analytics delivery
- –LookML modeling adds setup effort before teams can self-serve
- –Advanced governance can slow down rapid exploratory analysis
- –Complex environments require careful tuning of data connections and caching
Best for: Enterprises needing governed semantic modeling and reusable analytics definitions
Conclusion
After evaluating 10 data science analytics, Google BigQuery 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 And Analytics Software
This buyer’s guide covers data and analytics platforms across warehouses, lakehouses, BI dashboards, and semantic modeling using Google BigQuery, Amazon Redshift, Microsoft Fabric, Snowflake, Databricks Lakehouse Platform, Apache Superset, Metabase, Qlik Sense, Tableau, and Looker.
The focus stays on integration depth, data model design choices, automation and API surface for moving data and deployments, and admin and governance controls that hold across teams and environments.
Systems that turn governed data into queryable analytics and reusable BI semantics
Data and analytics software provisions storage and compute for analytics, defines how data is modeled or structured for reuse, and delivers BI interfaces for dashboards and exploration. These tools connect ingestion and transformation to query execution so teams can run batch and streaming pipelines, publish governed dashboards, and share governed datasets across users.
Google BigQuery and Snowflake show how a governed warehouse with SQL-first analytics handles scaling, security, and cross-team data sharing. Looker and Apache Superset show how semantic modeling and row or dataset permissions shape dashboard consistency and access control.
Integration, modeling, automation, and governance controls to validate before purchase
Integration depth determines whether data pipelines, notebooks, SQL workloads, and BI publishing move through one consistent control plane. Data model design choices decide whether metrics and relationships remain consistent across dashboards, explores, and ad hoc queries.
Automation and API surface affects repeatable provisioning and deployment of pipelines, governance objects, and embedded analytics workflows. Admin and governance controls decide who can read, share, clone, and audit changes across environments and teams.
API and automation surface for pipeline and deployment control
BigQuery supports production data flows with SQL execution plus ingestion wiring to Google Cloud storage and Pub/Sub. Databricks Lakehouse Platform pairs managed Spark jobs and orchestration with notebooks anchored on Delta Lake tables for end-to-end automation. Looker and Qlik Sense also support distributing analytics beyond dashboards via APIs and scheduled delivery workflows.
Performance acceleration through governed physical design
BigQuery uses partitioning, clustering, and materialized views to reduce scanned data and speed reads. Redshift uses materialized views and workload management to stabilize concurrency across analytical users. Snowflake applies automatic clustering and supports time travel and zero-copy cloning for faster development iterations.
Shared data foundations across lakehouse and warehouse experiences
Microsoft Fabric uses OneLake as a shared data foundation across Lakehouse and Warehouse experiences inside the same tenant. Databricks Lakehouse Platform anchors batch and streaming workloads on Delta Lake tables with ACID transactions. This reduces the need to duplicate logic across engines when streaming and batch must share governance.
Governed access control with row or column level enforcement
BigQuery includes column-level security and fine-grained IAM for secure multi-team sharing. Apache Superset supports row-level security using user roles and dataset permissions. Snowflake provides role-based access controls and audit visibility to protect data across analytics lifecycle operations.
Extensibility via SQL, notebooks, and governed sharing primitives
Databricks Lakehouse Platform runs SQL, notebooks, and Spark workloads inside a single workspace using a unified engine. Snowflake supports governed data sharing streams between accounts without copying datasets. Qlik Sense provides governed data modeling plus embedding and managed shares that distribute analytics outside its UI.
Semantic modeling for consistent metrics and drill paths
Looker uses LookML to standardize metrics and dimensions across dashboards and explores with permission-aware access. Tableau relies on Tableau’s data model and calculated fields using level of detail expressions for repeatable logic. Metabase adds model-layer transformations plus question and dashboard building over semantic models to keep exploration aligned to reusable definitions.
A checklist for selecting a warehouse and BI stack that matches real operating needs
Start with the workload shape and access pattern. Choose a warehouse or lakehouse when throughput, concurrency isolation, and physical performance features dominate. Choose BI and semantic layers when reusable metrics and controlled interactivity dominate.
Then confirm that integration depth matches the way data moves today. Validate automation and API surface by tracing how provisioning and publishing would work for pipelines, dashboards, and embedded views under RBAC and audit requirements.
Match the storage and compute architecture to batch plus streaming requirements
Teams needing near real-time analytics with serverless scaling should evaluate Google BigQuery for streaming ingestion via Pub/Sub. Teams standardizing both batch and streaming on ACID lakehouse tables should evaluate Databricks Lakehouse Platform for Delta Lake. Teams in Microsoft ecosystems that need unified workspace capabilities across lakehouse and warehouse should evaluate Microsoft Fabric with OneLake.
Validate performance controls against concurrency and repeated-query patterns
If repeated aggregates drive most workload, validate materialized views in BigQuery or Redshift as named acceleration mechanisms. If mixed workloads require predictable concurrency, validate Redshift Workload Management with query queues and monitoring. If development iteration speed matters, validate Snowflake time travel and zero-copy cloning for schema and dataset change workflows.
Prove data model consistency using semantic layer or defined metric objects
If metric consistency across dashboards and self-service exploration is the priority, evaluate Looker for LookML-based governed semantic modeling. If the organization uses BI workbooks and wants calculated-field logic tied to a structured data model, evaluate Tableau for data model and level of detail expressions. If fast exploration must still map to reusable model-layer transformations, evaluate Metabase for models plus its question and dashboard builder.
Confirm admin governance can enforce access and traceability across assets
If access must be enforced at column level with fine-grained IAM, validate BigQuery column-level security and audit controls for secure multi-team sharing. If access must be enforced at row level for multi-tenant dashboards, validate Apache Superset row-level security with user roles and dataset permissions. If cross-account distribution must be governed without dataset duplication, validate Snowflake governed data sharing via secure streams and role-based access controls.
Assess automation and extensibility for provisioning, embedding, and operational workflows
If repeated pipeline delivery and governed lineage matter, validate Microsoft Fabric deployment tooling and pipeline orchestration across resources. If embedded analytics and operational reporting require scheduled delivery workflows, validate Looker’s embedded analytics via APIs and scheduled delivery workflows. If analytics distribution relies on embedding plus managed sharing, validate Qlik Sense for embedding and managed shares.
Which teams get measurable value from these data and analytics platforms
Different tools win for different operating constraints. Warehouses and lakehouses win when data throughput, optimization levers, and governance at query time matter. BI and semantic layers win when metric reuse, interactive exploration, and permissions need to remain consistent.
Each segment below maps directly to the best-fit scenarios listed for the tools in this set.
SQL-focused analytics teams running large-scale workloads with streaming ingestion
Google BigQuery fits because it runs SQL-first analytics with serverless scalability and streaming ingestion via Pub/Sub plus materialized views for repeated queries. Teams with unpredictable workloads also benefit from BigQuery automatic scaling and partitioning and clustering for efficient reads.
AWS organizations that need concurrency isolation and engineered data modeling support
Amazon Redshift fits because it includes Workload Management with query queues and monitoring for workload isolation. Redshift also supports federated queries and materialized views, which suits SQL analytics teams with strong data engineering processes.
Microsoft ecosystem teams building governed analytics across lakehouse engineering and BI reporting
Microsoft Fabric fits because it uses OneLake as a shared data foundation across Lakehouse and Warehouse experiences while keeping governance tied to workspace-scoped resources. Fabric also supports Spark workloads and real-time ingestion for operational dashboards built with consistent access controls.
Enterprises needing governed sharing across accounts and strong change tracking
Snowflake fits because it separates compute from storage, supports governed data sharing via secure streams between accounts, and includes time travel plus zero-copy cloning for safe development iterations. Its role-based access controls and audit visibility match multi-team governance needs.
Organizations standardizing semantic metrics and reusing governed definitions for self-service
Looker fits because LookML standardizes metrics and dimensions across dashboards and explores with permission-aware access. It also supports reusable explores and components, which reduces metric inconsistency when self-service expands.
Common purchase and rollout mistakes that break governance, consistency, or throughput
Mistakes usually show up when teams underestimate modeling effort, governance configuration complexity, or workload tuning requirements. They also happen when the tool choice ignores how ingestion and access need to integrate with existing pipelines.
These pitfalls map to concrete issues called out across multiple tools in this set.
Assuming performance will hold without aligning physical design to query patterns
BigQuery cost and performance depend heavily on query patterns and data layout, so partitions and clustering must match the way queries filter and aggregate. Redshift also requires tuning of schema design and distribution keys, so warehouse design effort is not optional for best results.
Treating concurrency isolation as a BI-only concern
Workload Management in Amazon Redshift addresses queue contention by routing queries to queues for predictable concurrency control, which BI alone cannot fix. BigQuery’s automatic scaling helps, but heavy mixed workloads still benefit from explicit workload controls like Redshift queueing.
Installing semantic controls without committing to the right modeling workflow
LookML modeling in Looker adds setup effort before self-service, so teams must plan the upfront build of governed metrics and dimensions. Superset can require deeper SQL and semantic knowledge for complex metrics, and Metabase transformations can become harder to manage than full semantic layers when logic grows.
Overcomplicating governance configurations without validating the resource boundaries
Microsoft Fabric governance configurations can require careful planning due to workspace-scoped resources, so teams should validate governance behavior before large rollout. Snowflake provides strong governance tooling, but cross-team operational workflows can feel complex when warehouse design and sharing change across environments.
Choosing a BI-first tool when ingestion, lineage, and lakehouse transformations dominate effort
Apache Superset and Tableau focus on dashboard and authoring workflows, so performance tuning and permissions depend on underlying database design and extract and indexing choices. Teams doing heavy engineering and streaming transformations should validate lakehouse foundations in Databricks Lakehouse Platform with Delta Lake or Fabric with OneLake.
How We Selected and Ranked These Tools
We evaluated each tool on features, ease of use, and value, and each overall rating reflects a weighted average where features carries the most weight. Features account for about 40% of the overall score, while ease of use and value each account for about 30% because adoption and operational fit matter after capabilities are validated.
Editorial scoring used only the provided review attributes such as named standout features, listed pros and cons, and the specific features, ease of use, and value ratings. Google BigQuery stood out from lower-ranked tools because it combines serverless SQL analytics with named performance accelerators like materialized views plus streaming ingestion via Pub/Sub and governance with column-level security and fine-grained IAM.
That combination lifted the features factor through acceleration primitives, lifted ease of use through serverless scaling for unpredictable workloads, and lifted value through reduced operational tuning compared with systems that require more hands-on warehouse design.
Frequently Asked Questions About Data And Analytics Software
Which tool fits best for SQL-first analytics on a serverless warehouse?
How do workload management and concurrency isolation differ between BigQuery and Redshift?
Which platform is better when analytics must share a data foundation across lakehouse and warehouse?
What integration pattern supports near real-time ingestion for warehouse and BI in these tools?
How do semantic layer and data model approaches differ across Looker and Tableau?
Which tool provides governed, permission-aware dashboard publishing with role-based controls?
What data migration approach works best when moving from an existing SQL warehouse to a cloud platform?
How do SSO and access controls typically map to admin governance in enterprise deployments?
Which option is best for embedding analytics into internal applications with an API-first workflow?
What extensibility or customization model matters most for automated reporting and reusable definitions?
Tools reviewed
Primary sources checked during evaluation.
aws.amazon.comReferenced in the comparison table and product reviews above.
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