Data visualization is an important part of understanding and making sense of large datasets. Visual representations of data can help identify patterns, trends, and relationships that may not be immediately obvious from looking at raw numbers alone. As one of the largest companies in the world, with massive amounts of data across its various businesses, Amazon relies heavily on data visualization to gain insights and make informed decisions.
Amazon QuickSight
The name of Amazon’s primary data visualization tool and service is Amazon QuickSight. Launched in 2016, Amazon QuickSight provides cloud-based business intelligence capabilities that let users easily connect to data sources, perform ad-hoc analysis, and create interactive dashboards and visualizations. Some key features of Amazon QuickSight include:
- Interactive dashboards with auto-refresh option
- Ad-hoc exploration and pivot analysis
- Visualizations like charts, graphs, and geospatial maps
- Natural language query and search
- Data prep and ML-powered forecasts
- Integration with AWS data sources and BI tools
- Pay-per-session pricing model
Amazon QuickSight is designed to scale across large datasets while delivering fast performance. It also provides role-based access controls, encryption at rest and in transit, and audit logging for security. The service caters to a wide range of users from business analysts to executives, enabling self-service access to data insights.
Capabilities and Use Cases
As a fully managed business intelligence service, Amazon QuickSight can handle large volumes of data from a variety of sources. It connects natively to AWS data stores such as Amazon Redshift, Amazon Aurora, Amazon RDS, Amazon S3, and more. Other database sources can be accessed using custom or pre-built connectors. QuickSight’s data prep features let users join, shape, and cleanse data for analysis.
Some common use cases for Amazon QuickSight include:
- Sales and marketing analytics – track KPIs, segment customers, analyze campaigns
- Operations analytics – analyze supply chain, logistics, cost optimizations
- Financial analytics – reporting, variance analysis, forecasts
- Web and mobile analytics – monitor engagement, conversion, funnel
- IoT and sensor analytics – real-time monitoring, anomaly detection
The visualizations and dashboards created in Amazon QuickSight can be easily shared with other authorized users. This makes it simple to distribute reports and insights across teams, from executives to front-line managers. The responsive design allows dashboards to be accessed on any device from desktops to mobile.
Comparing QuickSight to Other AWS Data Visualization Options
Within the AWS stack, there are a few other options that provide data visualization and business intelligence capabilities:
Amazon Quicksight vs Amazon Athena
Athena is an interactive query service that enables running SQL queries against data in Amazon S3 without needing to set up infrastructure. It works great for ad-hoc querying of log data and object stores, but does not have native visualization features. The query results are tabular and would need additional tools to visualize.
Amazon Quicksight vs Amazon EMR
EMR provides managed Hadoop clusters for big data processing and analytics. While EMR handles tasks like data processing, machine learning, and Spark workloads well, it does not include built-in visualization. So EMR would need to be combined with QuickSight or other tools to visualize and dashboard the data.
Amazon Quicksight vs Amazon Redshift
Redshift is Amazon’s cloud data warehouse solution. It manages massive datasets for analytics and business intelligence workloads. Redshift has some basic reporting and charting capabilities through integration with third-party BI tools. But advanced visualization and dashboards would require something like QuickSight on top of Redshift.
Amazon Quicksight vs Amazon Kinesis
Kinesis offers capabilities for working with real-time streaming data. While Kinesis ingest and processes streaming data, it does not look to provide visualization. QuickSight could be used on top of Kinesis to add real-time dashboards of streaming data sources.
The key distinction for Amazon QuickSight is it provides an end-to-end business intelligence platform tailored for data visualization, dashboards, and sharing analytics. The other services can power data processing, but QuickSight handles the robust visualization capabilities on top of those underlying data sources.
Key Features of Amazon QuickSight
Some of the main capabilities and features of Amazon QuickSight include:
| Feature | Description |
| Ad-hoc exploration | Intuitively explore and analyze data without scripts or coding |
| Visualizations | Charts, graphs, geo maps to represent data visually |
| Dashboards | Interactive dashboards with advanced features like filtering, drill downs |
| Natural language query | Query data using conversational business language |
| ML Insights | Surface trends, forecasts, outliers through ML algorithms |
| Embedded analytics | Embed and integrate dashboards into apps and workflows |
| Security | Robust access controls, encryption, data governance |
| Scalability | Handle massive data volumes across SPICE, ML, and VPC capacities |
| Connectivity | Broad data source support including AWS, third-party databases, on-prem |
QuickSight Architecture Overview
Amazon QuickSight utilizes a serverless, scalable architecture to deliver high performance analytics. Some key architectural components include:
- SPICE in-memory engine – optimized caching/querying
- Management plane – manages control API actions
- Data plane – handles querying and data retrieval
- Reader instances – parallelizes data processing
- ML Insights – runs auto ML on datasets
- Visualization plane – generates interactive dashboards
- Global infrastructure – ensures low latency access
This separation of components allows QuickSight to independently scale each piece to match usage patterns. The SPICE engine minimizes query times, while ML compute capacity can expand as needed. Visualization nodes provide responsive dashboard front-ends.
SPICE In-Memory Engine
SPICE stands for Super-fast, Parallel, In-memory Calculation Engine. It is a proprietary in-memory engine optimized for fast data loading, aggregation, and query serving. SPICE ingests source data and stores it in a columnar format across clustered compute nodes. This allows for parallel processing of data and high performance even with large datasets and complex queries.
Scalable Compute Resources
QuickSight leverages a range of compute resources that automatically scale up and down based on usage. These include:
- Reader instances – Scale out to parallelize data queries
- ML instances – Expand as needed to run ML Insights algorithms
- Visualization instances – Add frontend nodes to serve more dashboards
- SPICE capacity – Increase memory/compute for source data
By orchestrating these different resources, QuickSight can tune its architecture in real-time based on incoming query and analysis workloads. Resources activate on-demand so capacity matches usage metrics.
QuickSight Dashboards and Visualizations
Amazon QuickSight enables users to create interactive dashboards with advanced visualization features. Some key capabilities include:
Visualization Types
QuickSight supports a robust set of visualization options to represent data, including:
- Line, bar, column, area charts
- Pie, donut, scatter plots
- Pivot tables
- Heat maps
- Tree maps, network graphs
- Geo maps, choropleths
- Gauge charts, bullet graphs
Developers can also build custom visual types. The broad range empowers users to pick optimal visuals for different analysis needs.
Dashboard Features
Dashboard functionality includes:
- Filtering to narrow focus on subsets of data
- Cross filtering between visuals to drive insights
- Drill downs to navigate from overview to details
- Parameter controls for inputs
- Scrolling narratives to tell data stories
These capabilities allow users to dynamically slice data, uncover relationships between elements, and tell compelling stories.
Responsive Design
QuickSight dashboards support responsive design for phone, tablet, and desktop experiences. Dashboards resize and reflow automatically across form factors. This enables users to stay connected to their data from anywhere.
Sharing and Embedding
Finished dashboards can be easily shared, embedded, and published for broad accessibility:
- Share with other QuickSight users
- Embed into workflows and apps
- Publish dashboards to the web
Secure dashboards give users fine-grained control over content visibility while enabling distribution. Dashboards can be accessed outside QuickSight via embedding into custom apps.
QuickSight Integrations with AWS
As part of the AWS stack, Amazon QuickSight integrates seamlessly with a range of AWS data and analytics services. Some examples include:
Amazon Redshift
QuickSight provides direct connectivity to Redshift data warehouses. Redshift’s columnar storage improves query performance. QuickSight dashboards can also embed Redshift SQL so data stays in Redshift without duplication.
Amazon Athena
Athena’s serverless query access to Amazon S3 makes a powerful data source for QuickSight analytics. Query Athena results directly from QuickSight for easy S3 analytics.
Amazon EMR
QuickSight integrates with EMR clusters for big data processing. Use EMR for ETL, then pipe that refined data into QuickSight dashboards and reports.
AWS Lambda
Lambda functions can power ETL data pipelines feeding QuickSight. Serverless ETL minimizes overhead while providing clean, analysis-ready data.
Amazon Kinesis
Stream data from Kinesis sources directly into QuickSight for real time dashboards over streaming data.
This broad AWS ecosystem integration makes it simpler for organizations to leverage QuickSight’s visualization capabilities on top of their existing AWS data landscape.
QuickSight Alternatives
Some top alternative platforms to Amazon QuickSight include:
Microsoft Power BI
Power BI is a popular standalone BI offering from Microsoft. It provides self-service analytics and AI-powered visualizations. Power BI offers robust connectivity across data sources, custom visualization development, and content publishing.
Tableau
Tableau leads the business intelligence space overall in market share. It enables interactive visualization capabilities for dashboards and analytics. Tableau offers strong analytics functionality with premium enterprise capabilities.
Sisense
Sisense focuses on embedded BI use cases for app developers. It makes it easy to integrate analytics into custom applications. Sisense is cloud-native and handles large, complex datasets.
Looker (Google)
Google acquired Looker in 2019, integrating it into Google Cloud’s analytics portfolio. Looker provides data exploration and dashboards fueled by its in-database architecture.
While these platforms have varying strengths, Amazon QuickSight is arguably the most robust cloud-native BI option. Its serverless architecture and deep AWS integrations make it optimal for AWS-centric organizations.
QuickSight Pricing Overview
Amazon QuickSight uses a pay-per-session pricing model based on timed user access. There are three main pricing tiers:
Standard Edition
- $0.30 per user session hour
- First 1 million sessions per month free
- SPICE capacity of 50 GB/user included
Enterprise Edition
- $2 per user session hour
- SPICE capacity of 500 GB/user included
- Adds capabilities like external sharing, data sets, machine learning
Embedded Analytics
- $5 per user session hour
- For fully embedding QuickSight into apps
- Allows custom branding
So pricing scales directly based on user usage time. More active users result in higher monthly costs due to more hourly sessions. Unused SPICE capacity does not incur charges.
Conclusion
Amazon QuickSight provides a robust cloud-native business intelligence and data visualization service. It enables users across organizations to easily connect to data, perform ad-hoc queries, and create rich interactive dashboards. With serverless auto-scaling architecture, QuickSight can handle massive datasets and provide consistently fast performance.
QuickSight deeply integrates with AWS data stores and services, making it an optimal analytics choice for companies leveraging the AWS stack. Between its broad capabilities, flexible pricing model, and rapid innovation pace, Amazon QuickSight clearly emerges as a leader in the cloud BI space.