Amazon.com Business Intelligence Tools – Amazon QuickSight powers data-driven organizations with unified business intelligence (BI) at hyperscale. With QuickSight, all users can meet a variety of analytical needs from the same source of truth through modern interactive dashboards, paginated reports, embedded analytics and natural language queries.
Deliver information to all your users when, where, and how they need it. BI users can explore modern, interactive dashboards; gain insight into their applications; Receive scheduled, formatted reports with paginated reports; and make forward-looking decisions with machine learning (ML) insights. To make data exploration even easier, QuickSight allows users to ask questions about data in natural language.
Amazon.com Business Intelligence Tools
Accelerate development using a single authoring experience to create modern dashboards and paginated reports. Application developers can quickly integrate rich analytics and ML-powered natural language query capabilities into applications with one-step embedding, public embedding, and a rich API.
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Since QuickSite is serverless, automatically scale to thousands of users without having to set up, configure, or manage your own servers. QuickSight’s in-memory calculation engine, SPICE, provides consistently fast response times for end users and analysts, eliminating the need to scale databases for high workloads.
Pay only for what you use with QuickSight usage-based pricing. No need to purchase thousands of end-user licenses for large-scale BI or embedded analytics deployments. With no software or servers to install or manage, you can reduce costs by eliminating upfront costs and complex capacity planning.
Create, schedule and share reports and data exports from a single, fully-managed, cloud-based BI service. Get business-critical information to users how and when they need it, with critical operational reports and dashboards in the same solution.
Ask conversational questions about your data and use the Amazon QuickSight Q ML-powered engine to get relevant visualizations. No BI training required. QuickSight Q uses ML to interpret the intent of the question and analyze the data to quickly answer business questions.
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Embed interactive visualizations and dashboards, sophisticated dashboard authoring or natural language query capabilities into your applications to differentiate user experiences and unlock new monetization opportunities.
Customer Stories Over 100,000 customers use Amazon QuickSight for their BI needs. See more QuickSight customer stories >>
“Amazon QuickSite will allow us to create fast, interactive dashboards that will seamlessly integrate with our next-generation Stats applications. We’re able to scale these secure, customized and easy-to-use dashboards to each club without having to provision servers or manage infrastructure – just real while paying for usage. We look forward to expanding the use of Amazon QuickSite.”
“We see a need to support learners, teachers and leaders in education by helping them transform their relationship with data and information. A big part of this strategy involves embedding information directly where our users are collaborating, teaching and learning. We are particularly interested in making the experience of becoming informed more intuitive – favoring insight-informed workflows and/or embedded prose. By eliminating the interpretation step, embedded visualizations make insights more useful and actionable. With QuickSight, we were able to quickly deliver on our promise to embed visualizations, supporting the rapid iteration we needed, at the large scale needed to support our global user community.”
Database Business Intelligence (bi) & Analytics Software
“Amazon QuickSite’s pay-per-use pricing and serverless architecture enabled Best Western’s analytics team to deliver increased business value faster and at less than half the cost of our previous analytics architecture. With Amazon QuickSight Q, we look forward to enabling our business partners to self-service their ad-hoc queries while reducing operational overhead. This will allow our partners to quickly get answers to their business questions by simply typing and searching for their questions in plain language.”
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Organizations have been using data warehouse and business intelligence (DWBI) workloads to support business decision making for many years. These workloads are brought to the Amazon Web Services () platform to take advantage of the cloud. However, these workloads are created using multiple vendor tools and technologies, and the customer is burdened with administrative overhead.
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This post provides architectural guidance for integrating multiple DWBI technologies into managed services to help reduce administrative overhead, drive operational simplicity, and drive business efficiency. Two scenarios are explored:
Organizations are busy managing multiple DWBI technologies due to acquisitions, mergers, and the lift-and-shift of workloads. This workload uses extract, transform, and load (ETL) tools to read relational data from upstream transactional databases, process it, and store it in a data warehouse. Then, these workloads use business intelligence tools to generate valuable insights and present them to users in the form of reports and dashboards.
These DWBI technologies are usually installed and maintained on their own servers. Figure 1 shows the increase in administrative overhead for the organization but also creates challenges in maintaining the overall knowledge of the team.
Each of these functions can be efficiently performed using the Service. For example, Glue can be used for ETL, Amazon Redshift for data warehouse and Amazon QuickSight for business intelligence.
Amazon Quicksight Q
By using the mentioned services, organizations will be able to consolidate their use of DWBI technology. Organizations will also be able to adopt these services faster, as their engineering teams can more easily leverage their DWBI knowledge with these services. For example, using SQL knowledge in Glue jobs with SprakSQL, in Amazon Redshift queries, and in Amazon QuickSight dashboards.
Figure 2 shows a redesign of the architecture of Figure 1 using services. In this architecture, ETL functions are integrated into Glue. Glue crawler is used to auto-catalog source and target table metadata; Then, Glue ETL jobs use this catalog to read data from the source and write it to the target (data warehouse). Glue jobs also apply necessary transformations (such as joins, filters, and aggregates) to the data before writing them. Additionally, glue triggers are used to ensure job execution. Alternatively, Managed Workflow for Apache Airflow can be used to schedule jobs.
Similarly, the data warehousing function is integrated with Amazon Redshift. Amazon Redshift is used to store and organize rich data and also implement appropriate data access controls for both workloads and users.
Finally, business intelligence functions are integrated using Amazon QuickSite. It is used to create the required dashboards that source data from AmazonRedshift and apply complex business logic to create the charts and graphs needed for business insights. It is also used to enforce necessary access restrictions to dashboards and data.
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In a scenario where the source database is in an on-premises datacenter, the overall solution would be the same as Scenario 1, with an additional step to continuously move data from the on-premises database to an Amazon Simple Storage Service (Amazon S3) bucket. Data movement can be handled efficiently by a database migration service (DMS).
To make the source database accessible to the DMS, it is necessary to establish a connection between the cloud and the on-premise network. Depending on performance and throughput requirements, an organization can choose a Direct Connect service or a site-to-site VPN service to move data securely. For the purpose of this discussion, we are considering Direct Connect.
In Figure 3, the DMS task is used to perform a full-load followed by data capture changes to continuously move the data to the S3 bucket. In this scenario, glue is used to list and read data from an S3 bucket. The remaining part of the dataflow is similar to the part mentioned in scenario 1.
Also, updated architectures provide the necessary security using access control, data encryption at-rest and in transit, monitoring and auditing.
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Additionally, both Amazon Redshift and Amazon QuickSite offer their own authentication and access controls. So, a user can be a local user or federated. With the help of these credentials, the organization will be able to control access to data in Amazon Redshift and also control access to dashboards in Amazon QuickSite.
In this blog post, we discussed how Glue, Amazon Redshift, and Amazon QuickSight can be used to integrate DWBI technologies. We also discussed how the architecture can help an organization build scalable, secure workloads with auto scaling, access control, log monitoring, and activity auditing. Imagine you are a business analyst at a fast fashion brand. You are tasked with understanding why sales of a new clothing line are declining in a given region. Your job is to figure out how to increase sales while achieving desired profit benchmarks. Customer buyer personas, website reviews, social media mentions, sales figures by day and hour at various store locations, holidays or other events, expected pay dates at local companies, even heat map data for each store are some of the variables to consider.
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