QuickSight’s integration with the AWS ecosystem is seamless. Connecting to S3, Redshift, or Athena happens in minutes without complex configuration or workarounds. The SPICE in-memory engine delivers fast query performance even with large datasets, which keeps dashboards responsive. I appreciate the natural language query feature (Amazon Q) that allows me to ask questions in plain English and get visual answers without writing SQL. It democratizes access to insights for non-technical stakeholders who need quick answers.

The platform’s embedded analytics capability is a standout feature. I can place dashboards directly into internal applications or customer portals without additional tools. This centralizes data consumption and eliminates the need to jump between platforms. QuickSight also offers built-in machine learning features like anomaly detection and forecasting, which provide advanced insights without requiring custom algorithms. When revenue spikes unexpectedly, the ML engine surfaces an explanation automatically.

The AutoGraph feature intelligently recommends chart types based on data field characteristics. This removes guesswork for team members who aren’t visualization experts and ensures consistency across reports. Security is robust with role-based access controls, single sign-on, and comprehensive auditing. QuickSight supports FedRAMP, HIPAA, PCI DSS, ISO, and SOC compliance, which covers most regulatory requirements. Review collected by and hosted on G2.com.

QuickSight lacks the visualization variety found in Power BI or Tableau. Gantt charts, candlestick charts, and comparative high-low charts are missing, which limits design flexibility for specific use cases.

The data model approach differs from traditional BI tools in a way that feels restrictive. Instead of building relationships between fact and dimension tables, I often need to create large denormalized datasets with repeated data. This makes data preparation more cumbersome and less efficient. Review collected by and hosted on G2.com.

It has built-in data storage with SPICE, making it both fast and convenient to use. UI is extraordinery for devloping dashboards. easy to change datatypes of fields and select viesuals accordingly Review collected by and hosted on G2.com.

Quicksight should include date series to show the data-gaps properly Review collected by and hosted on G2.com.

I like how well QuickSight integrates with other AWS services, making it easy to pull data from sources like Redshift and S3. The pricing model is competitive, especially for large organizations with many read-only users. I also appreciate the recent improvements in Generative BI (Amazon Q), which adds a layer of user-friendliness through natural language interactions. It’s quick to deploy, relatively easy to maintain, and ideal for dashboards and operational reporting in cloud-native environments. Review collected by and hosted on G2.com.

While QuickSight works well within the AWS ecosystem, it lacks flexibility compared to more mature BI tools like Power BI or Tableau. Custom visualizations and formatting options are limited, and the interface can feel unintuitive at times. Advanced data modeling and transformations also require workarounds or external preprocessing. Additionally, integration with non-AWS data sources isn’t as seamless as advertised, which can slow things down in hybrid environments. Review collected by and hosted on G2.com.

I really like how easy it is to connect QuickSight to multiple AWS data sources like Athena, S3, and Redshift. It’s fast, serverless, and scales automatically. The dashboards are interactive, visually clear, and great for sharing insights with teammates without heavy setup. It also integrates well with ML models and predictive analytics, which helps automate business insights. Review collected by and hosted on G2.com.

The UI feels a bit limited compared to tools like Power BI or Tableau, especially in custom formatting and advanced visualization. Some features (like calculated fields or parameters) can be unintuitive. Performance drops slightly with large datasets, and debugging permission or SPICE issues sometimes takes longer than expected. Review collected by and hosted on G2.com.

I find Amazon QuickSight particularly valuable for its speed and efficiency in generating analytics and insights from user data without the need for extensive coding. It seamlessly connects within the AWS ecosystem, allowing instantaneous data source connections, which significantly eases the setup process. The platform supports writing natural language queries, enabling users to obtain necessary data quickly and effortlessly. This feature has been a game-changer, providing user-specific insights on demand without the complexity of writing custom code. Furthermore, I appreciate its flexibility in working both within and outside the AWS ecosystem, making data integration straightforward with both internal and external sources. Review collected by and hosted on G2.com.

I dislike that Amazon QuickSight isn't available as a standalone product. It would be easier if I could deploy it on other platforms rather than relying solely on AWS Cloud and using it on-premise. Review collected by and hosted on G2.com.

I love using Amazon QuickSight for its seamless integration with AWS services, enabling smooth data connectivity and efficient large dataset analysis. Its interactive dashboards, machine learning-powered insights, and real-time analytics enhance my data visualization and reporting. The cost-effective, scalable architecture and the easy deployment with the pay-per-session pricing model stand out. I appreciate the natural language query feature for accessibility, fast SPICE engine performance, and secure data sharing for effective team collaboration. Review collected by and hosted on G2.com.

I find some of the more complex functions in Amazon QuickSight, like calculated fields and custom visuals, a bit clunky, requiring extra navigations and clicks. It would be beneficial if the user interface for advanced analytics was made more intuitive. This would particularly help new users or non-technical members to understand and use it more easily. While QuickSight is excellent for creating dashboards and visualizations, improving the user interface and simplifying the access to advanced features would enhance its user-friendliness. Additionally, there is a slight learning curve for the advanced features, which can be a barrier initially. Review collected by and hosted on G2.com.

I love Amazon QuickSight's SPICE engine, which significantly enhances performance by quickly loading large datasets and updating data in real-time. The user-level access control is fantastic, allowing easy permission management and data security when sharing dashboards with stakeholders. The scheduled email reports help ensure everyone stays informed by delivering key metrics directly to inboxes. Furthermore, the natural language query feature is incredibly user-friendly for nontechnical users to explore data simply by typing questions. Review collected by and hosted on G2.com.

I find the customization of visuals somewhat limited in Amazon QuickSight compared to other tools like Power BI or Tableau. The lack of flexibility in layout designs, conditional formatting, and custom themes can be frustrating, especially when trying to match internal branding or create highly tailored dashboards. Review collected by and hosted on G2.com.

Amazon QuickSight stands out as an impressive BI tool—fast, intuitive, and highly scalable. Its smooth integration with AWS and the robust SPICE engine make analyzing data straightforward, even when working with large datasets. The machine learning insights feature is particularly useful for quickly identifying trends and anomalies, which saves a significant amount of time. Additionally, the pay-per-session pricing model helps keep expenses manageable. All in all, it’s a smart, dependable, and user-friendly option for anyone looking to make data-driven decisions. I highly recommend it. Review collected by and hosted on G2.com.

While Amazon QuickSight offers excellent speed and integration, it has a few limitations. The dashboard customization options can feel restricted compared to other BI tools, and the interface—though improving—may not be as polished or intuitive for new users. Some users also find data preparation and formatting a bit rigid, especially for complex datasets. Lastly, exporting reports and managing visuals could be more flexible. Despite these issues, QuickSight remains a solid choice for AWS-based analytics. Review collected by and hosted on G2.com.

Amazon QuickSight integrates smoothly with other AWS services, making it convenient for users already working within the AWS ecosystem. It provides fast, scalable analytics and allows easy creation of interactive dashboards without requiring complex setup. The SPICE engine helps in handling large datasets efficiently, and the web-based interface is accessible and responsive. Review collected by and hosted on G2.com.

While Amazon QuickSight offers strong integration with AWS and solid performance, its user interface can feel less intuitive compared to some other BI tools. Customization options for visuals and layouts are somewhat limited, and advanced features may require additional AWS knowledge or setup. Pricing can also become complex to estimate when scaling to multiple users or large datasets. Review collected by and hosted on G2.com.

Amazon QuickSight integrates seamlessly with AWS services, making it very easy to connect to different data sources like Redshift, S3, and Athena. The dashboards are interactive, responsive, and easy to share across teams. I like the pay-as-you-go pricing model which makes it cost-effective compared to other BI tools, and the natural language query feature (“Q”) is a big plus for quickly generating insights without needing technical SQL knowledge. Review collected by and hosted on G2.com.

The customization options for visuals are more limited compared to competitors like Power BI or Tableau. The initial learning curve can feel steep for non-technical users, and sometimes dashboards take longer to load when handling very large datasets. Integration outside the AWS ecosystem could also be more flexible. Review collected by and hosted on G2.com.