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Business Intelligence Tools Aws
Organizations have been using data warehouse and business intelligence (DWBI) workloads to support business decision making for years. These workloads are deployed on the Amazon Web Services () platform to take advantage of the cloud. However, this workload is built using multiple vendor tools and technologies, and the customer faces an administrative overhead burden.
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This post provides architectural guidelines for combining multiple DWBI technologies into managed services to help reduce management costs, ease operations, and improve business efficiency. Two scenarios are considered:
Organizations are involved in managing multiple DWBI technologies due to acquisitions, mergers and acquisitions, and workload shifts. This workload uses extract, transform, and load (ETL) tools to read relational data from upstream transaction databases, process it, and store it in a data warehouse. Then, this workload uses business intelligence tools to generate valuable insights and present them to users in the form of reports and dashboards.
This DWBI technology is generally installed and maintained on its own server. Figure 1 shows the increased administrative costs for the organization but also creates challenges in maintaining the overall knowledge of the team.
Each of these functions can be performed efficiently using services. For example, you can use Glue for ETL, Amazon Redshift for data warehousing, and Amazon QuickSight for business intelligence.
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By using the mentioned services, organizations will be able to strengthen their DWBI technology use. Organizations will also be able to quickly adapt to this service, as their engineering teams can more easily use their DWBI knowledge with this service. For example, using SQL knowledge in Glue jobs with SprakSQL, in Amazon Redshift queries and in Amazon QuickSight dashboards.
Figure 2 shows the redesigned architecture of Figure 1 using services. In this architecture, ETL functions are combined in Glue. Glue crawler is used to automatically index the source and target table metadata; then Glue ETL jobs use this catalog to read data from the source and write to a label (data warehouse). Paste jobs also apply necessary transformations (such as merging, filtering, and aggregation) to the data before writing it. In addition, adhesive primer is used for construction planning. Alternatively, managed workflows for Apache Airflow can be used to schedule jobs.
Similarly, a data warehousing function is integrated with Amazon Redshift. Amazon Redshift is used to store and organize enriched data and also enforce appropriate data access control for both workloads and users.
Finally, business intelligence functions are unified using Amazon QuickSight. It was used to create the necessary dashboards that receive data from AmazonRedshift and apply complex business logic to create the necessary charts and graphs needed to gain business insights. It is also used to implement necessary access restrictions to dashboards and data.
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In a situation where the source databases are in an on-premises data center, the overall solution will be similar to Scenario 1, with an additional step to continuously migrate the data from an on-premises database to an Amazon Simple Storage Service (Amazon S3) bucket. The data migration can be handled efficiently with the Database Migration Service (DMS).
Making the source database available to the DMS requires establishing a connection between the cloud and the on-premises network. Based on performance and performance, the company can choose either Direct Connect service or Site-to-Site VPN service to transfer the data securely. For the purposes of this discussion, we are considering Direct Connect.
In Figure 3, a DMS task is used to perform a full load followed by change data capture to continuously move the data to an S3 bucket. In this scenario, Glue is used to record and read the data from an S3 bucket. The rest of the data flow is the same as mentioned in scenario 1.
Also, the updated architecture provides the necessary security using access control, encryption of data at rest and in transit, monitoring and auditing.
What Is Amazon Quicksight? The Aws Bi Tool Explained
Additionally, both Amazon Redshift and Amazon QuickSight provide their own authentication and access control. Therefore, a user can be a local user or a federated user. With the help of these credentials, an organization will be able to control access to data in Amazon Redshift and also access to the dashboard in Amazon QuickSight.
In this blog post, we discussed how to use Glue, Amazon Redshift, and Amazon QuickSight to consolidate DWBI technology. We’ve also discussed how architecture can help an enterprise build scalable, secure workloads with autoscaling, access control, log monitoring, and functional auditing. Amazon QuickSight powers data-driven organizations with unified business intelligence (BI) at hyperscale. With QuickSight, all users can meet their different analytics needs from the same source of truth through modern interactive dashboards, segmented reports, built-in analytics and natural language queries.
Give all your users insights when, where and how they need them. BI users can view modern, interactive dashboards; gain insight into their applications; get scheduled, formatted reports with paginated reports; and make forward-looking decisions with machine learning (ML) insights. To further simplify data exploration, QuickSight allows users to ask questions about the data in natural language.
Accelerate development using a single authoring experience to build 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 rich APIs.
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Because QuickSight is serverless, automatically scale to tens of thousands of users without having to install, configure, or manage your own servers. The QuickSight in-memory calculation engine, SPICE, provides consistently fast response times for users and analysts, removing the need to scale databases for heavy workloads.
Pay only for what you use with QuickSight’s usage-based pricing. No need to purchase thousands of user licenses for large-scale BI or embedded analytics upgrades. With no software or servers to install or manage, you can lower costs by removing upfront costs and complex capacity planning.
Create, schedule, and share reports and data exports from a single, fully managed, cloud-based BI service. Get users critical information 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 is required. QuickSight Q uses ML to interpret the intent of a question and analyze the data to quickly provide answers to business questions.
Data Warehouse And Business Intelligence Technology Consolidation Using Aws
Embed interactive visualizations and dashboards, advanced dashboard authors, or natural language query capabilities into your apps to differentiate user experiences and unlock new monetization opportunities.
Customer Stories Over 100,000 customers use Amazon QuickSight for their BI needs. Check out more QuickSight customer stories >>
“Amazon QuickSight will allow us to quickly build an interactive dashboard that will integrate seamlessly with our Next Gen Stats applications. We can extend these secure, customizable and easy-to-use dashboards to each club without having to provision servers or manage infrastructure – all while only paying for actual usage. We look forward to expanding the use of Amazon QuickSight.”
“We see a need to support students, teachers and leaders in education by helping to 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 being informed more intuitive – by supporting insight-informed workflows and/or embedded prose. By removing the interpretation step, embedded visualizations make insights more useful and actionable. With QuickSight, we were able to deliver on our promise to embed visualizations quickly and support the rapid iteration we need at the scale needed to support our global user community.”
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“Amazon QuickSight’s cost-per-use pricing and serverless architecture enabled Best Western’s analytics team to deliver added value to the business, 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 address their specific questions while reducing operational costs. This will enable our partners to quickly get answers to their business questions by typing and searching their questions in plain language.”
Support for Internet Explorer ends on 31.07.2022. Supported browsers are Chrome, Firefox, Edge and Safari. Learn more »With Amazon QuickSight Q, anyone can ask questions in natural language and get accurate answers with relevant visualizations that help them gain insight into the data.
QuickSight Q provides phrase and business term suggestions and performs error checking so you don’t have to worry about typos or remembering the exact terms in your data.
QuickSight Q uses machine learning (ML) to automatically understand the meaning and relationships of business data, giving you accurate answers with relevant visualizations.
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