Key Features
Quicksight is capable of taking in countless variations of requests that are then pooled and organized into a language and format that is easy to interpret and allows for making data-driven decisions that will best benefit the organization. Everything that is happening within is completely summarized on a dashboard with charts to visualize all of the relevant analytics.
Quicksight Q
This is machine learning capabilities utilized to better allow Quicksight to understand queries and spit out relevant information in a legible format in an incredibly short time. It already comes pre-trained with terms and data sets from a variety of industries so that it’s already primed to understand and regurgitate information relative to users’ intentions. Finally, the longer users interact with Q, the better it becomes accustomed to the internal lingo of an organization and their inquiry habits.
Embedded Interactive Elements
The dashboard is highly customizable with many options to change the look and feel from coloring to font. With just a single click, it’s incredibly easy to embed these tools into apps, wikis, and portals without the need to call embedding APIs. The Enterprise Edition of Quicksight also sports the option to generate custom emails embedded with dashboard reports, including the header and the footer of the emails. Furthermore, it’s capable of deeper insight with ad-hoc analysis and machine learning tools for anomaly detection, forecasting, and natural language queries. Finally, the serverless architecture can be arranged to keep multiple end-users separate, moving dashboards between accounts, automating payments, and more.
ML (machine learning) Insights
Outliers and trends can very easily fall under the radar under tides of other data points. ML Insights can continuously analyze all data gathered to find anomalous patterns and understand the causes. With that, it can also establish accurate estimates for future business metrics. These predictions are then woven into the dashboard with all of the necessary context to fully explain to the users along with controls to customize the story.
Built for Enterprise-scale Workloads
Quicksight scales automatically to compensate tens of thousands of users at any time without infrastructure or capacity to plan ahead. Additionally, it can import from data sources either on-premises or in the cloud, including various SaaS applications. Quicksight’s Super-fast, Parallel, In-memory, Calculation Engine - also known as SPICE - automatically replicates data for multiple users to run analysis simultaneously. It’s designed with being a global application in mind and is localized in ten major languages and is available in most AWS availability regions.
Additional Features
Native AWS Integration
-
- Private VPC connectivity for secure AWS access to Amazon Redshift, Snowflake, Exasol, Amazon RDS, and more
- Native IAM permissions for Amazon S3 and Amazon Athena with fine-grained access control for serverless data exploration
- Amazon SageMaker integration allows easy integration of sophisticated ML models
without complex data pipelines
Minimal Maintenance Required
-
- Serverless architecture auto-scales to hundreds of thousands of users with high availability; no need to over-provision for peak usage
- SPICE in-memory engine auto-scales to provide consistent fast response times for end-users and analysts; no need to scale databases for high workloads
- Pay-per-session optimizes costs by only paying for actual usage; no need to buy thousands of end-user licenses for large-scale BI or embedded analytics
Minimal Maintenance Required
-
- Serverless architecture auto-scales to hundreds of thousands of users with high availability; no need to over-provision for peak usage
- SPICE in-memory engine auto-scales to provide consistent fast response times for end-users and analysts; no need to scale databases for high workloads
- Pay-per-session optimizes costs by only paying for actual usage; no need to buy thousands of end-user licenses for large-scale BI or embedded analytics
Cost and Pricing
Standard Edition
-
- $9/month with a yearly subscription or $12/month for each month individually
- 10 GB/user of SPICE capacity
- $0.25 per GB of extra capacity
- $9/month with a yearly subscription or $12/month for each month individually
Enterprise Edition
-
- Authors - create and share dashboards with other users.
| Month-to-month | Yearly Subscription | |
| Authors | $24/month | $18/month |
| Authors with Q | $34/month | $28/month |
-
- Readers - can explore dashboards, download data, and receive email reports.
|
Month-to-month |
|
|
Readers |
$0.30/session up to a maximum of $5/month |
|
Readers with Q |
$0.30/session up to a maximum of $10/month |
Capacity Pricing
-
- Reader Capacity is for hosting multiple Q sessions in tandem, making it ideal for mass deployments.
| Type | # of sessions | Price | Cost of additional sessions |
| Monthly Plan | 500/month | $250/month | $0.50 |
| Annual Plan | 50,000/year | $20,000/year | $0.40 |
| Annual Plan | 200,000/year | $56,700/year | $0.28 |
| Annual Plan | 400,000/year | $96,000/year | $0.24 |
| Annual Plan | 800,000/year | $162,000/year | $0.20 |
| Annual Plan | 1,600,000/year | $258,000/year | $0.16 |
| Annual Plan | 3,000,000+/year | Situation-dependent | Situation-dependent |
-
- Q question Capacity
| Type | # of sessions | price | Cost of additional questions |
| Monthly Plan | 500/month | $250/month | $0.50 |
| Annual Plan | 60,000/year | $18,000/year | $0.30 |
| Annual Plan | 600,000/year | $120,000/year | $0.20 |
| Annual Plan | 1,000,000+/year | Situation-dependent | Situation-dependent |
Anomaly Detection
|
Metrics evaluated |
Price/1,000 metrics evaluated |
|
1 - 1,000,000 |
$0.50 |
|
1,000,001 - 10,000,000 |
$0.25 |
|
10,000,001 - 100,000,000 |
$0.10 |
|
>100,000,000 |
$0.05 |
SPICE usage
|
Per GB/month |
|
$0.38 |
I am a recent graduate from the University of Wisconsin - Stout and am now working with AllCode as a web technician. Currently working within the marketing department.
