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Amazon EMR

How AWS Business Intelligence Tools Boosts Efficiency

A cornerstone of business intelligence is the data that can be gathered from customer interaction. Gathering data and processing it for relevant information can provide better-informed decisions for business operations. AWS provides tools that can not only automate this process, but make data much more legible and process data more efficiently.

How Amazon Handles Business Intelligence

There are many benefits from having data-intensive applications or business operations situated in the cloud.  Hardware and software are easier to procure, security is tighter, scaling is easier, and there are plenty of opportunities to save on costs and avoid other pitfalls of cloud-based hardware.  Users are provided AWS QuickSight for Business Intelligence more specifically and Amazon EMR for more general big data workloads.

AWS QuickSight

AWS QuickSight is an excellent service for data-driven business operations. It makes it easier to send easy-to-understand dashboards and data visualizations to employees and different levels of an organization with information they can apply to their tasks. Along with some default templates to work with, users can compile custom formats for dashboards using simple click-and-drag functionality. There are plenty of options for how users can embed, implement into APIs, and utilize them in applications. Because this is a serverless tool, it can scale endlessly to encompass tens of thousands of simultaneous users.

Enterprise Workloads

QuickSight streamlines data integration from various sources without needing extra infrastructure, thanks to its blazingly fast Parallel, In-memory Calculation Engine called SPICE (super-fast, parallel, in-memory calculation engine). This engine allows for rapid on-the-fly calculations, enabling users to quickly generate dynamic visualizations that can be tailored and reorganized to meet specific business requirements. This eliminates traditional complexities involved in data preparation, such as manual extraction, transformation, and loading. Moreover, AWS QuickSight offers interactive tools, including graphs, tables, charts, stories, and sheets, enhancing the overall data analysis experience and aiding in swift decision-making.  It also has built-in security features, extensive API capabilities, and can easily be shared with global partners sporting localization options in ten different major languages.

AWS QuickSight Dashboard

Embedded Analytics

The complicated process to embed an API has been significantly simplified in favor of single-click operation.  Without needing to procure additional servers or acquire infrastructure licensing, users can set up dashboards in wikis, portals, applications, and email reports.  Analytics can go so far as to provide better insight into anomalies and future forecasting or tailor answers for very specific questions.  Managing architecture and serverless operations is now much simpler, making it easier to isolate specific users, move dashboards, manage single sign-ons (SSOs), and automate deployments.

 

Adapts to User Needs

QuickSight uses machine learning to quickly adjust to the needs of the user.  It can find correlations in data, adjust to specific jargon from distinct industries, improves steadily with the number of questions asked, and faster.  It adds semantic data to datasets to reduce the amount of time needed to start asking questions.

Amazon QuickSight is capable of analyzing a wide variety of data types. It can directly integrate with and import data from files stored in AWS S3 buckets as well as those located within on-premise networks. The supported file formats include CSV, TSV, ELF, CLF, both flat and semi-structured JSON files, and XLSX documents. Additionally, it can connect to various SaaS platforms to utilize their data. It’s important to note that any files stored in AWS S3 need to be in either zip or gzip format to be compatible with QuickSight’s import requirements. Files compressed in other formats must be decompressed prior to import.

Amazon EMR

EMR is what will help users scale these big workloads.  It is flexible, incredibly simple to use, and is compatible with different storage types from either the AWS catalog or otherwise depending on the need and functionality of an application.  Amazon EMR can procure any number of clusters, automatically configure them for specific frameworks, and provide extensive control to users on how to optimize them fully.

Open Source Applications

Clusters with EMR will automatically adapt to the users’ open-source applications of choice.  Open-source data tools from the Apache catalog are available but are mostly confined to Spark, Hadoop, and HBase.  Alternatively, Presto is an SQL query engine that is optimized for low-latency data analysis, also capable of supporting multiple operations.

 

Big Data Tools

Data scientists can get ample use out of EMR with its extensive support of deep learning and machine learning tools such as Hadoop applications.  For more specific cases, users can add specific libraries or tools through the use of bootstrapping.  Data analysts will frequently use the EMR Studio, Notebooks, and Hue for more interactive development, authorizing certain Apache jobs, and submitting SQL queries.  EMR provides a solid data pipeline for development and processing while simplifying data management and privacy significantly.

Amazon EMR User Interaction Diagram

Internal Security Services

With the sensitive nature of the data being processed, there are a few options for how users can protect their data.  AWS Lake Formation allows the implementation of authorization policies for accessing databases, columns, and tables.  If Apache tools are preferred, EMR does allow the native integration of Apache Ranger to dictate how authorizations are distributed.  Apache Ranger does offer distinct controls for access at individual levels.  Then there is EMR’s User Role Mapper for users who are more familiar with the controls offered by AWS Identity Access Manager.  Permission configuration can be done either between individuals or groups of users.

 

Hybrid Infrastructure

AWS Outposts extends services, infrastructure, and APIs to virtually any data center, location, or physical infrastructure capable of hosting the necessary software.  Using the same Command Line Interface or Management Console for controlling the EMR, users can deploy using Outposts to whatever they need.

Efficient Data Processing

AWS provides a good number of tools a company could need if the company objectives required them to orient business structure around the cloud.  Amazon has already adapted their environment to process heavy workloads and massive amounts of data and it is possible to set up a work cycle to continuously process data at the end of a transaction or data gathered from customer interactions for further refining that cycle.  Adjustments can be made both more accurately and significantly faster compared to other business intelligence solutions.

Billing

Amazon QuickSight is available in two primary editions, each catering to different user needs and offering distinct pricing structures:

Standard Edition: Designed primarily for personal use, such as individual data analysis and exploring datasets. This edition is accessible to those publishing dashboards or creating content, commonly referred to as authors. The pricing for the Standard Edition is set at $9 per month if paid annually or $12 monthly.

Enterprise Edition: This edition is aimed at larger organizations with more extensive data analytics and dashboard consumption requirements. It supports not only authors but also readers. For authors, the cost is $18 monthly with an annual agreement or $24 month-to-month. Readers are charged $5 per month per user. Additionally, a specific pricing option for high-volume usage costs $250 per month for every 500 reader sessions.

These options provide flexibility depending on the scale of use and the specific needs of the user, ranging from individual professionals to large enterprises.

Dolan Cleary

Dolan Cleary

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.

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