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Digital Transformation In IndustryTop 10 Best Crucial Data Transfer Software of 2026
Top 10 Crucial Data Transfer Software options ranked for fast, secure moves across cloud and endpoints, with key tradeoffs for teams.
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.
Azure Data Factory
Self-hosted integration runtime for secure, direct connectivity to on-prem data sources
Built for enterprises building governed data transfer pipelines across cloud and on-prem sources.
AWS DataSync
Editor pickAgent-based NFS and SMB transfers with restartable tasks for resilient migrations
Built for hybrid teams migrating datasets between AWS and NFS or SMB systems.
Google Cloud Data Transfer Service
Editor pickScheduled transfer jobs for recurring ingestion via Google Storage Transfer Service
Built for teams moving data into Google Cloud on schedules without building custom pipelines.
Related reading
Comparison Table
This comparison table evaluates major data transfer tools across cloud services and endpoints using integration depth, data model and schema handling, automation and API surface, and admin and governance controls like RBAC and audit log coverage. Each row highlights how provisioning, configuration, and extensibility choices affect throughput, operational control, and how teams automate recurring transfers through API and job orchestration hooks.
Azure Data Factory
cloud ETL orchestrationOrchestrates scheduled and event-driven data movement and transformation across cloud and on-prem data sources using managed data pipelines.
Self-hosted integration runtime for secure, direct connectivity to on-prem data sources
Azure Data Factory stands out for turning cross-system moves into managed, orchestrated pipelines with visual authoring and code-based control. It supports scheduled and event-driven data movement across on-premises and cloud sources using self-hosted integration runtimes.
Core capabilities include copy activities for batch and streaming sources, a rich connector catalog for data stores, and integration with Azure monitoring and governance controls. The service also supports data flows for transformations and mapping, reducing the need for separate ETL engines.
- +Visual pipeline authoring with parameterization for reusable transfer patterns
- +Self-hosted integration runtime enables secure on-premises to cloud data movement
- +Broad connector support across databases, files, and storage services
- –Managing large estates of pipelines can become complex without strong conventions
- –Deep optimization for performance often requires tuning activities and runtime settings
- –Schema drift and complex transformations can need careful design in data flows
Data engineering teams in enterprises
Orchestrate multi-source batch ingestions
Fewer manual pipeline failures
Platform ops and DevOps teams
Deploy pipelines across environments
Repeatable releases and rollbacks
Show 2 more scenarios
Analytics and BI data teams
Refresh warehouse using data flows
Faster, consistent dashboard refreshes
Teams transform source data with mapping data flows before loading to analytic targets for reporting.
Hybrid IT integration owners
Move data from on-prem systems
Reliable hybrid data transfers
Owners run self-hosted integration runtimes to connect on-prem sources and move data to cloud destinations.
Best for: Enterprises building governed data transfer pipelines across cloud and on-prem sources
More related reading
AWS DataSync
bulk transferPerforms high-speed, secure transfers from on-premises storage to AWS using agent-based or direct connectivity and continuous monitoring.
Agent-based NFS and SMB transfers with restartable tasks for resilient migrations
AWS DataSync focuses on fast, managed data transfers between AWS storage and on-premises locations using storage-aware transfer jobs. It supports common endpoints like Amazon S3 and EFS, plus network file systems via NFS and SMB, with job-level scheduling and restartable transfers.
The service includes bandwidth management, task monitoring, and path-level include and exclude controls for targeting only the required files. Strong operational visibility comes from CloudWatch metrics and event notifications tied to each DataSync task.
- +Storage-aware transfers with built-in parallelism improve throughput for large datasets
- +Supports S3 and EFS plus NFS and SMB endpoints for hybrid migration
- +Job controls include include and exclude filters and scheduling with restartable runs
- +CloudWatch metrics and task-level notifications support production monitoring and alerting
- –Endpoint setup for on-prem networks can require careful routing and firewall tuning
- –Granular tuning options for performance exist but add operational complexity
- –Cross-account and permissions configuration can be cumbersome in larger environments
Cloud migration engineering teams
Migrate file shares into Amazon S3
Reduced downtime during migrations
On-prem infrastructure operators
Replicate NFS directories to AWS
Reliable near-real-time data replication
Show 2 more scenarios
Enterprise application teams
Move data between EFS and on-prem
Consistent application dataset delivery
Transfer POSIX-like file data to and from NFS or SMB endpoints using managed bandwidth limits.
Platform operations and compliance
Consolidate regulated data into AWS
Measurable transfer compliance evidence
Target specific paths while monitoring transfer events tied to each DataSync task for auditability.
Best for: Hybrid teams migrating datasets between AWS and NFS or SMB systems
Google Cloud Data Transfer Service
managed transferMoves data between Google Cloud services and external sources using managed transfer jobs with options for CDC and scheduled runs.
Scheduled transfer jobs for recurring ingestion via Google Storage Transfer Service
Google Cloud Data Transfer Service provides managed, scheduled data movement into Google Cloud using connectors like Storage Transfer Service and Database Migration Service. It supports recurring transfers from common sources, plus one-time migrations for structured workloads.
Workload control includes scheduling, transfer job management, and operational visibility through job status and logs. For enterprises, it aligns with other Google Cloud services such as BigQuery, Cloud Storage, and managed database platforms to reduce custom integration work.
- +Managed scheduled transfers reduce custom ETL orchestration effort
- +Connector coverage supports common migration and ingestion patterns
- +Job monitoring and status tracking improve operational control
- –Source-specific setup can be complex for nonstandard systems
- –Advanced routing and transformation often require external tooling
- –Troubleshooting may require deeper familiarity with transfer internals
Data platform teams
Schedule recurring source-to-Cloud Storage syncs
Reduces manual transfer operations
Analytics engineering teams
Move structured data into BigQuery
Accelerates analytics data readiness
Show 2 more scenarios
Database migration teams
Migrate databases with managed transfer jobs
Lowers migration coordination effort
Coordinates migration activities with operational visibility and repeatable job execution controls.
Enterprise IT operations
Standardize cross-team migration workflows
Improves operational consistency
Centralizes transfer scheduling and monitoring to reduce custom scripts and manual handoffs.
Best for: Teams moving data into Google Cloud on schedules without building custom pipelines
More related reading
IBM Aspera
high-speed file transferAccelerates large file transfers with adaptive bandwidth control, UDP-based transport, and server-client deployment for high-throughput movement.
Aspera TCP-based acceleration with adaptive rate control for long-distance transfers
Aspera on Cloud stands out for accelerating large file transfers over WAN using adaptive, TCP-friendly transfer technology. It supports managed file transfer workflows with centralized orchestration, host-and-region connectivity, and integration options for enterprise data movement.
Core capabilities center on high-throughput transfers, policy-driven access controls, and operational visibility for transfer status. The platform is typically strongest when reliably moving large datasets between clouds, datacenters, and partners.
- +High-speed WAN transfers using Aspera performance acceleration technology
- +Centralized orchestration supports consistent transfer policies across environments
- +Detailed transfer monitoring improves operational troubleshooting
- –Setup and tuning can be complex for non-specialized teams
- –Enterprise integration effort is required for best end-to-end workflow fit
- –Advanced performance outcomes depend on correct endpoint configuration
Best for: Enterprises moving large datasets between clouds and datacenters with strong governance
Aspera on Cloud
cloud file deliveryProvides cloud-hosted transfer orchestration for high-speed large file delivery with policy controls and integration options.
Aspera TCP-based acceleration with adaptive rate control for long-distance transfers
Aspera on Cloud stands out for accelerating large file transfers over WAN using adaptive, TCP-friendly transfer technology. It supports managed file transfer workflows with centralized orchestration, host-and-region connectivity, and integration options for enterprise data movement.
Core capabilities center on high-throughput transfers, policy-driven access controls, and operational visibility for transfer status. The platform is typically strongest when reliably moving large datasets between clouds, datacenters, and partners.
- +High-speed WAN transfers using Aspera performance acceleration technology
- +Centralized orchestration supports consistent transfer policies across environments
- +Detailed transfer monitoring improves operational troubleshooting
- –Setup and tuning can be complex for non-specialized teams
- –Enterprise integration effort is required for best end-to-end workflow fit
- –Advanced performance outcomes depend on correct endpoint configuration
Best for: Enterprises moving large datasets between clouds and datacenters with strong governance
WinSCP
secure file automationAutomates secure file transfer over SFTP, SCP, and FTP with scripting, scheduled transfers, and session management.
Session scripting with automation-friendly command language for SFTP and SCP workflows
WinSCP stands out for file transfers centered on SFTP, SCP, and FTP with a strong focus on secure, scriptable workflows. It supports GUI browsing and command-line automation, with features like synchronization, resumable transfers, and detailed session logging.
It also integrates key management, including SSH key authentication, which reduces reliance on passwords for critical transfers. WinSCP is geared toward operators who need repeatable transfer tasks and reliable integrity controls on endpoints.
- +Robust SFTP and SCP support with strong SSH key authentication options
- +GUI and scripting support enables repeatable transfers without extra tooling
- +Resume and transfer settings help handle interruptions during large downloads
- –Powerful options can feel overwhelming for simple one-off transfers
- –Advanced automation often requires scripting knowledge and careful setup
- –Core workflow stays Windows-focused, limiting cross-platform operational convenience
Best for: Operations teams needing secure, scriptable SFTP transfers on Windows systems
More related reading
FileZilla
open-source transferTransfers files via FTP, FTPS, and SFTP using a client interface and automation-friendly configuration for site management.
Transfer Queue with pause and resume controls during active uploads and downloads
FileZilla is distinguished by a two-pane interface that makes directory browsing and file transfers visually straightforward. It supports FTP, FTPS, and SFTP connections with per-session transfer controls like pause and resume.
Core capabilities include queued transfers, transfer logs, and configurable connection timeouts. It also provides granular settings for speed limits, passive mode, and retry behavior during unstable connections.
- +Two-pane layout speeds up browsing, drag-and-drop transfers, and queue management
- +Supports FTP, FTPS, and SFTP with session-level connection configuration
- +Pause and resume works well for large file transfers
- +Transfer queue and detailed logs help track retries and errors
- –No built-in user management or role controls for teams
- –Advanced automation and workflow orchestration require external scripting
- –Large-scale enterprise governance features are limited
Best for: Individual users and small teams transferring files between servers
rclone
multi-cloud syncSynchronizes and transfers data between local storage and many cloud backends using a command-line interface and mount capabilities.
Mount remote storage via FUSE with consistent paths using rclone mount
rclone stands out for turning many cloud and storage backends into one unified file-transfer tool controlled from the command line. It supports syncing, copying, mirroring, and scheduled transfers across providers like S3, Google Drive, OneDrive, and many SFTP and WebDAV endpoints.
Core capabilities include encryption options, bandwidth throttling, resume behavior, and detailed logging for traceable migrations. It also offers a mount mode to expose remote storage as a local filesystem for applications that expect POSIX-like paths.
- +Unified interface across dozens of cloud and on-prem backends
- +Powerful sync and copy modes with retry and resumable transfers
- +Flexible crypto, throttling, and include exclude filtering options
- –Command-line configuration requires time to master safely
- –Complex filters and flags can increase mistakes during migrations
- –Mount mode needs tuning to match application performance
Best for: IT teams migrating data across mixed storage systems via automation scripts
More related reading
TeraCopy
desktop copy accelerationSpeeds up file copying and supports verification and pause-resume features for reliable large data transfers.
File verification after copy to detect and prevent integrity issues during transfers
TeraCopy focuses on reliable file transfers by replacing Windows copy behavior with a queue-based transfer engine. It prioritizes integrity checks by verifying transferred files and supports pause, resume, and transfer scheduling workflows.
The app also surfaces detailed progress and error reporting, which helps during critical migrations between drives and systems. It is most useful when transfers are large, interruptions are likely, or accuracy verification matters more than raw throughput.
- +Verifies copied files to reduce silent corruption during critical transfers
- +Queue and transfer management features support multi-step data migrations
- +Pause, resume, and retry behavior helps recover from interruptions
- –Main focus stays on file copy operations, not broader backup and replication
- –Advanced controls can feel heavy for simple one-off copies
- –Performance benefits depend on source drive speed and file layout
Best for: Teams transferring large, critical files that need integrity checks and recoverability
Resilio Sync
peer-to-peer syncEnables peer-to-peer file synchronization and folder sharing across networks for continuous transfer and replication.
Peer-to-peer folder synchronization with resumable transfers and selective device syncing
Resilio Sync stands out by using peer-to-peer syncing to move critical files without routing everything through a central server. It supports folder-level continuous synchronization, selective syncing, and fast restart after interruptions. It also includes access control, device management, and strong options for handling large directory trees with minimal operational overhead.
- +Peer-to-peer transfers reduce server load during crucial file migrations
- +Continuous sync and selective folder syncing support ongoing operational use
- +Resumable transfers recover efficiently after network disruptions
- +Granular device permissions support controlled access to sensitive data
- –Initial setup of peers, keys, and permissions can be complex
- –Troubleshooting replication issues may require deeper network knowledge
- –Advanced governance and reporting are limited versus enterprise sync suites
- –Operational success depends on consistent connectivity across endpoints
Best for: Teams syncing sensitive files between offices without relying on central storage
Conclusion
After evaluating 10 digital transformation in industry, Azure Data Factory 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 Crucial Data Transfer Software
This buyer's guide covers how to choose Crucial Data Transfer Software tools for fast, secure data movement across cloud and endpoints. It compares Azure Data Factory, AWS DataSync, Google Cloud Data Transfer Service, IBM Aspera, Aspera on Cloud, WinSCP, FileZilla, rclone, TeraCopy, and Resilio Sync.
The focus stays on integration depth, data model, automation and API surface, and admin and governance controls. The guide maps these criteria to concrete mechanisms like Azure self-hosted integration runtime, AWS DataSync include and exclude filters, and rclone mount via FUSE.
Crucial data transfer software for governed, automated movement between cloud and endpoints
Crucial Data Transfer Software packages turn data moves into repeatable jobs, scheduled runs, or scripted transfers across cloud services and on-prem endpoints. These tools reduce manual copying by adding transfer orchestration, resumable behavior, and operational visibility through job status, logs, and monitoring hooks like CloudWatch.
Enterprise teams typically use governed pipeline tools like Azure Data Factory for scheduled and event-driven cross-system copies with self-hosted integration runtime, while hybrid teams often use AWS DataSync for storage-aware transfers from on-prem storage to AWS endpoints such as S3 and EFS.
Evaluation criteria tied to transfer orchestration, schemas, automation, and control
Transfer tools should align the transfer engine with the data model and the governance model, not just the protocol. Azure Data Factory, AWS DataSync, and Google Cloud Data Transfer Service each expose job-level controls that affect throughput, scope, and restart behavior.
The strongest selections also expose automation and integration surfaces that fit existing workflows and admin requirements. IBM Aspera and Aspera on Cloud focus on WAN acceleration and policy controls, while rclone and WinSCP focus on scriptable transfer execution over many endpoints.
Integration runtime and connector reach across on-prem and cloud
Azure Data Factory provides a self-hosted integration runtime for secure connectivity to on-prem data sources, which reduces the need for custom gateway code. AWS DataSync pairs agent-based or direct connectivity with storage-aware jobs to reach on-prem NFS and SMB shares and then land data in AWS destinations like S3 and EFS.
Job scoping controls like include and exclude filters
AWS DataSync supports path-level include and exclude controls so transfers target only required files. Azure Data Factory supports parameterization for reusable pipeline patterns, which can be used to enforce repeatable scoping conventions across many jobs.
Restartability and resumable transfer behavior
AWS DataSync restartable transfers help recover from disruptions during hybrid migrations between on-prem file systems and AWS storage. WinSCP and FileZilla provide pause and resume behaviors with detailed session logging for interrupted SFTP or FTP flows.
Data model fit for pipelines versus file-level delivery
Azure Data Factory supports orchestrated copy activities plus data flows for mapping transformations, which makes it suitable when schema-aware transformation is part of the transfer plan. TeraCopy emphasizes file verification after copy to detect integrity issues, which fits high-stakes file moves where correctness checks matter more than schema transformations.
Automation surface with API-like extensibility via pipelines or scripting
Azure Data Factory uses visual pipeline authoring with parameterization and code-based control, which gives teams a repeatable automation surface for transfer logic. rclone uses a unified command-line interface with features like copy, sync, and mount mode, which supports automation scripts across S3, Google Drive, OneDrive, and SFTP targets.
Admin governance controls and audit-ready operational visibility
Azure Data Factory integrates with Azure monitoring and governance controls to support enterprise oversight of managed pipelines. AWS DataSync ties monitoring to CloudWatch metrics and task notifications per DataSync task, which supports operational governance for recurring migrations.
WAN throughput acceleration with policy controls
IBM Aspera and Aspera on Cloud use Aspera TCP-based acceleration with adaptive rate control for long-distance transfers, which can reduce transfer windows for large datasets. Both platforms add centralized orchestration and detailed transfer monitoring so consistent transfer policies apply across environments.
Decision framework for selecting the right transfer engine for the data and governance model
Start by mapping the transfer type to the execution model. Azure Data Factory fits governed pipeline execution with scheduled and event-driven copies and optional self-hosted integration runtime, while AWS DataSync fits storage-oriented jobs between on-prem NFS or SMB and AWS storage with restartable runs.
Then confirm whether governance needs land on job monitoring, access control, and audit logs, or whether correctness and recoverability matter most. IBM Aspera and Aspera on Cloud focus on WAN acceleration with centralized orchestration, while TeraCopy, WinSCP, and rclone center on file transfer integrity, scripting, and operational logging.
Classify the transfer as pipeline execution or file delivery
Use Azure Data Factory when transfer logic must include orchestrated copy activities and data flows for mapping transformations, because it combines movement and transformation in one managed pipeline model. Use TeraCopy when the goal is replacing Windows copy with a queue-based engine that performs file verification after copy to detect integrity issues.
Match the endpoint topology to the integration mechanism
Choose Azure Data Factory when on-prem access requires a self-hosted integration runtime that connects directly and securely to on-prem data sources. Choose AWS DataSync when the target includes AWS endpoints like S3 and EFS and the source is on-prem NFS or SMB where storage-aware transfer jobs help throughput and reliability.
Define how scope and repeatability must work at scale
If only subsets of files should move, use AWS DataSync path-level include and exclude filters plus job scheduling for controlled migrations. If repeatability must be enforced as reusable logic, use Azure Data Factory parameterization for transfer patterns that keep conventions consistent across many pipelines.
Validate restart and interruption handling for the failure modes at hand
For hybrid migrations that must recover from partial failures, use AWS DataSync restartable transfers or Aspera on Cloud where centralized orchestration and adaptive transport behavior support long-haul reliability. For operator-driven endpoint transfers over SFTP and SCP, use WinSCP or FileZilla because both provide pause and resume controls and detailed session or transfer logs.
Choose the automation surface that fits existing workflows
Use Azure Data Factory when automation must be represented as managed pipelines with code-based control and parameterization, because pipeline execution becomes the workflow contract. Use rclone when automation needs a unified command-line interface and mount mode via FUSE so apps can access remote storage as a filesystem with consistent paths.
Account for network distance and throughput constraints
For large datasets moving across long distances, choose IBM Aspera or Aspera on Cloud because Aspera TCP-based acceleration and adaptive rate control target WAN throughput. For steady file moves and directory sharing across offices without central routing, choose Resilio Sync for peer-to-peer folder synchronization with resumable transfers and selective syncing.
Which teams each transfer engine fits best based on real execution focus
Different tools are optimized for different operational models, so selection should reflect the team’s transfer responsibilities. Azure Data Factory fits enterprises that need governed, repeatable pipelines across cloud and on-prem sources with a self-hosted integration runtime.
For teams that own migration throughput and file scope, AWS DataSync and Aspera products match hybrid and long-haul constraints. For operator-driven transfers or script-based automation, WinSCP, FileZilla, and rclone fit day-to-day execution.
Enterprises building governed cross-system pipelines across cloud and on-prem
Azure Data Factory fits this segment because it orchestrates scheduled and event-driven data movement with copy activities and data flows, and it uses self-hosted integration runtime for secure on-prem connectivity.
Hybrid teams migrating datasets between AWS storage and on-prem NFS or SMB
AWS DataSync matches this segment because it performs storage-aware transfers with agent-based or direct connectivity, supports NFS and SMB endpoints, and provides restartable transfers plus path include and exclude filters.
Large-dataset movers facing WAN latency or long-distance throughput windows
IBM Aspera and Aspera on Cloud fit teams moving large datasets between clouds and datacenters because they use Aspera TCP-based acceleration with adaptive rate control and centralized orchestration with detailed monitoring.
Operations teams running secure SFTP and SCP transfers with repeatable scripts
WinSCP fits this segment because it supports SSH key authentication, session scripting with an automation-friendly command language, and resume controls with detailed session logging on Windows-focused workflows.
IT teams needing automation across many storage backends or exposing remote storage to apps
rclone fits this segment because it unifies dozens of cloud and on-prem backends under a command-line interface and supports rclone mount via FUSE for consistent POSIX-like paths.
Common selection pitfalls that cause failed migrations or weak governance
Many failures come from picking a protocol tool for a pipeline governance problem or picking a pipeline engine without aligning it to endpoint reality. WinSCP and FileZilla support secure transfers but they do not provide the role and enterprise governance surface that governed pipeline tools are built to operate at scale.
Other issues come from ignoring interruption recovery or data correctness checks. TeraCopy adds file verification after copy, while Aspera endpoint performance depends on correct endpoint configuration, so both must be planned before the first large run.
Choosing a file transfer client when governed automation and schema-aware transformations are required
Azure Data Factory supports both orchestration and transformations through copy activities and data flows, while WinSCP and FileZilla focus on SFTP and FTP transfer execution with scripting and logs.
Assuming restart behavior exists without validating the transfer engine’s recovery model
AWS DataSync explicitly supports restartable transfers, while WinSCP and FileZilla provide pause and resume but still rely on session-level operational setup for interrupted transfers.
Underestimating network distance tuning requirements for large WAN transfers
IBM Aspera and Aspera on Cloud deliver advanced performance outcomes only when endpoint configuration is correct, which means endpoint setup and tuning must be treated as a migration prerequisite.
Relying on plain copy without integrity validation for critical data moves
TeraCopy verifies files after copy to detect silent corruption, while rclone and Resilio Sync provide resumable behavior and logging but integrity verification after copy is a TeraCopy specialty.
Building automation around a CLI tool without safe scoping and filter discipline
rclone’s include exclude options and complex filter flags can increase mistakes during migrations, while AWS DataSync offers path-level include and exclude controls designed for job scoping.
How We Selected and Ranked These Tools
We evaluated Azure Data Factory, AWS DataSync, Google Cloud Data Transfer Service, IBM Aspera, Aspera on Cloud, WinSCP, FileZilla, rclone, TeraCopy, and Resilio Sync using three scoring buckets: features, ease of use, and value. Each tool received an overall rating built as a weighted average where features carries the most weight at forty percent while ease of use and value each account for thirty percent. This criteria-based scoring reflects the review summaries and the stated capabilities and limitations rather than private lab testing.
Azure Data Factory stood apart because its self-hosted integration runtime enables secure, direct connectivity to on-prem data sources, and it combines that with visual pipeline authoring plus scheduled and event-driven orchestration. That combination lifted features and supported higher ease-of-use and value outcomes since transfer logic can be managed as governed pipelines instead of scattered scripts.
Frequently Asked Questions About Crucial Data Transfer Software
Which tool fits governed, cross-system data movement with orchestration and monitoring?
How do the cloud transfer services handle large migrations and restart after interruptions?
What is the best option for recurring scheduled transfers into cloud storage without custom pipeline code?
Which tools support integrations and APIs for automation and external orchestration?
When is peer-to-peer file syncing a better fit than centralized transfer jobs?
Which option is most suitable for SFTP and SSH-key based operator workflows?
How do throughput and long-distance behavior differ for WAN transfers of large datasets?
Which tool offers strong file integrity checks during transfers on Windows desktops or workstations?
What configuration and access-control controls are typically needed for managed transfers versus manual file operations?
Tools reviewed
Primary sources checked during evaluation.
aws.amazon.comReferenced in the comparison table and product reviews above.
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