# Data visualization
Every day, the people in your organization make decisions that affect your business. When they have the right information at the right time, they can make the choices that move your company in the right direction. This gives decision makers the opportunity to explore and interpret information in an interactive visual environment to democratize data and accelerates data-driven insights that are easy to understand and navigate.
Building a BI and data visualization service in the cloud allows you to take advantage of capabilities such as scalability, availability, redundancy, and enterprise grade security. It also lowers the barrier to data connectivity and allows access to far wider range of data sources —both traditional, such as databases, as well as non-traditional, such as SaaS sources. An added advantage to a cloud-based data visualization service is the elimination of undifferentiated heavy lifting related to managing server infrastructure.
# Characteristics
**Scalability:** Ensure that the underlying BI infrastructure is able to scale up vertically and horizontally both in terms of concurrent users as well as data volume. For example, Quick SPICE, and web applications automatically scale up server capacity to accommodate a large number of concurrent users without any manual intervention in terms of provisioning additional capacity for data, load balancing, and other services.
**Connectivity:** BI applications must be able to not only connect with data platforms such as traditional data warehouses and databases, but also support connectivity to a data lake and modern data architectures. The application must also have the capacity to connect to non-traditional sources, such as SaaS applications. Typically, data stores are secured behind a private subnet and BI tools and applications must be able to connect in a secure mechanism using strategies, such as VPC endpoints and secure firewalls.
**Centralized security and compliance:** BI applications must allow for a layered approach for security. This includes: Securing at the perimeter using techniques such as IP allow lists, security groups, ENIs and IAM policies for cloud resource access, securing the data in transit and data at rest using SSL and encryption, and restricting varying levels of access through fine-grained permissions for users to the underlying data and BI assets. The application must also comply with the governmental and industry regulations for the country or region the company is bound by.
**Sharing and collaboration:** BI applications must support data democratization. They must have features that allow sharing of the dashboards with other users in the company as well as for multiple report authors to collaborate with one another by sharing access to the underlying dataset. Not all BI tools have this capability. Quick allows the sharing of assets, such as data sources, data sets, analyses, dashboards, themes, and templates.
**Logging, monitoring, and auditing:** BI applications must provide adequate mechanisms to monitor and audit the usage of the application for security (to prevent unwanted access to data assets and other resources) and troubleshooting. Quick can be used with Amazon CloudWatch, AWS CloudTrail, and IAM to track record of actions taken by a user, user role, or an AWS service. This provides the who, what, when, and where of every user action in QuickSight.
**Perform advanced analytics**
Modern BI applications must be able to discover hidden insights from your data, perform forecasting and what-if analysis, or add easy-to-understand natural language narratives to dashboards. The business users need the ability to perform analytics without deep statistical and machine learning knowledge.
Quick ML Insights provide features that make it easy to discover hidden trends and outliers, identify key business drivers, and perform powerful what-if analysis and forecasting with no technical or ML experience.
**Enable self-service business intelligence**
The common challenges of BI tools are how to make data more accessible to more people without extensive user training and technical understanding. Data must be available in all format - raw, semi-processed and processed. Self-service BI should allow users to interact with data on an as-needed-basis without involving IT.
Quick Q allows user to ask business questions in natural language and receive answers with relevant visualizations that help them gain insights from the data. QuickSight Q uses machine learning to interpret the intent of a question and analyze the correct data to provide accurate answers to business questions quickly
# Reference architecture
![\[Diagram showing QuickSight dashboard end-to-end design\]](http://docs.aws.amazon.com/wellarchitected/latest/analytics-lens/images/quicksight-dashboard-design.png)
**Data sources:** Supports connection with traditional Data Warehouse or databases and also have the capacity to connect to non-traditional sources such as SaaS applications. Supported datasources in QuickSight include Amazon S3, Amazon Redshift, Amazon Aurora, Oracle, MySQL, Microsoft SQL Server, Snowflake, Teradata, Jira, and ServiceNow. Check [here](https://docs.aws.amazon.com/quicksight/latest/user/supported-data-sources.html) for the complete list of data sources supported in QuickSight. These data sources could be secured behind a private subnet and QuickSight can connect in a secure mechanism using strategies such as VPC endpoints, and secure firewalls.
**Visualization Tool:** Quick.
**Consumers:** Visual dashboard consumers accessing a QuickSight console or embedded QuickSight analytics dashboard.
# Configuration notes
**Security:** Implement the principle of least privilege throughout the visualization application stack. Ensure data sources are connected using VPCs and restrict security groups to only the required protocols, sources, and destinations. Enforce that the users as well as applications in every layer of the stack are given just the right level of access permissions to data and the underlying resources. Ensure seamless integration with identity providers—either industry supported or customized. To ease flow and remove confusion, set up QuickSight and single sign-on (SSO) such that email addresses for end users are automatically synced at their first login. In the case of multi-tenancy, use namespaces for better isolation of principals and other assets across tenants. For example, QuickSight follows the least privilege principle and access to AWS resources such as Amazon Redshift, Amazon S3 or Amazon Athena (common services used in data warehouse, data lake or modern data architectures) can be managed through the QuickSight user interface. Additional security at the user or group level is supported using fine-grained access control through a combination of IAM permissions. Additionally, QuickSight features, such as row level security, column level security, and a range of asset governance capabilities that can be configured directly through QuickSight user interface.
**Cost optimization:** Accurately identify the volume of dashboard consumers and embedding requirements to determine the optimal pricing model for the given visualization use case. QuickSight offers two different pricing options (capacity and user based) that allows clients to implement cost-effective BI solutions. Capacity pricing allows large-scale implementations and user-based pricing allows clients to get started with minimal investment (Note: SPICE has a 500M records or 500 GB volume per dataset limitation).
**Low latency considerations:** Use in-memory caching option, such as Memcached, Redis, or the in-memory caching engine in QuickSight called SPICE (Super-fast, Parallel, In-memory Calculation Engine) to prevent latency in dashboard rendering while accommodating any built-in restrictions that the caching technology might have.
**Pre-process data views:** Ensure that the data is cleansed, standardized, enhanced, and pre-processed to allow analysis within the BI layer. If possible, create pre-processed, pre-combined, pre-aggregated data views for analysis purposes. ETL tools, such as AWS Glue DataBrew, or techniques, such as materialized views, can be employed to achieve this. After uploading the dataset, users can add calculated fields to a dataset during the data preparation or from the analysis page for additional insights provided data.