#AWS QuickSight Training

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Building a Smart IoT Application with AWS IoT Core

The Internet of Things (IoT) is revolutionizing industries by enabling real-time data collection, monitoring, and automation. AWS IoT Core provides a robust platform for connecting IoT devices securely to the cloud, processing data, and integrating with other AWS services. In this blog, we’ll explore how to build a smart IoT application using AWS IoT Core.

What is AWS IoT Core?

AWS IoT Core is a fully managed cloud service that allows IoT devices to connect to the cloud, send and receive data, and integrate with AWS services like Lambda, S3, and DynamoDB. It supports MQTT, HTTPS, and WebSockets for device communication and offers built-in security features.

Key Components of AWS IoT Core

To build a smart IoT application, it’s essential to understand AWS IoT Core’s key components:

IoT Things: Represents a virtual device in AWS IoT Core, allowing you to manage, monitor, and interact with physical IoT devices.

Device Gateway: Handles device connectivity via MQTT, WebSockets, or HTTPS.

Message Broker: Enables secure device communication using MQTT-based pub/sub messaging.

Rules Engine: Processes and routes data to AWS services like DynamoDB, Lambda, or S3.

Device Shadows: Maintains the last known state of a device, allowing cloud applications to interact even when a device is offline.

Security and Identity Management: Uses AWS IoT policies and certificates to enforce authentication and authorization.

Steps to Build a Smart IoT Application

1. Setting Up AWS IoT Core

Log into AWS and navigate to AWS IoT Core.

Create an IoT Thing to represent your device.

Generate an X.509 certificate for authentication and attach a policy with the necessary permissions.

2. Connecting an IoT Device

Use an MQTT client (e.g., Mosquitto, AWS IoT SDK) to connect your IoT device.

Install the AWS IoT SDK for Python, JavaScript, or other preferred languages.

Publish and subscribe to topics using the MQTT protocol to send and receive data.

Example Python Code (Using AWS IoT SDK):pythonfrom AWSIoTPythonSDK.MQTTLib import AWSIoTMQTTClient client = AWSIoTMQTTClient("MyIoTDevice") client.configureEndpoint("your-endpoint.iot.amazonaws.com", 8883) client.configureCredentials("root-CA.crt", "private.key", "certificate.pem") def messageCallback(client, userdata, message): print(f"Received message: {message.payload} from topic: {message.topic}") client.subscribe("iot/sensors", 1, messageCallback) client.publish("iot/sensors", "Hello from IoT device", 1)

3. Processing IoT Data Using AWS IoT Rules Engine

Create a Rule in AWS IoT Core to route incoming messages to other AWS services.

Example SQL statement to store IoT data in DynamoDB:

sql

SELECT temperature, humidity FROM 'iot/sensors' INTO AWS::DynamoDB::Table[iot_data_table]

4. Visualizing IoT Data

Use AWS Lambda to process data and send alerts when thresholds are exceeded.

Integrate with Amazon QuickSight or Grafana for real-time IoT data visualization.

5. Enabling Device Shadow for Offline Sync

Create a device shadow to store and retrieve device states.

Example shadow document:

json

{ "state": { "reported": { "temperature": 22, "status": "on" } } }

6. Implementing Security Best Practices

Use AWS IoT Policies and Certificates for authentication.

Enable AWS IoT Device Defender to monitor security metrics and detect anomalies.

Use Case: Smart Home Automation

A smart home IoT system can use AWS IoT Core to:

Monitor temperature and humidity using sensors.

Send alerts when a temperature threshold is exceeded.

Automate home appliances (e.g., turn on AC when it gets too hot).

Store and analyze data in DynamoDB for insights.

Conclusion

AWS IoT Core provides a scalable and secure way to build smart IoT applications. By leveraging its features like MQTT communication, device shadows, and the rules engine, developers can create efficient IoT solutions for various industries.

WEBSITE: https://www.ficusoft.in/aws-training-in-chennai/

[sophiamerlin]

A Deep Dive into Amazon Redshift: Your Guide to Cloud Data Warehousing

In the era of big data, organizations are increasingly turning to cloud solutions for efficient data management and analysis. Amazon Redshift, a prominent service from Amazon Web Services (AWS), has become a go-to choice for businesses looking to optimize their data warehousing capabilities. In this blog, we will explore what Amazon Redshift is, its architecture, features, and how it can transform your approach to data analytics.

If you want to advance your career at the AWS Course in Pune, you need to take a systematic approach and join up for a course that best suits your interests and will greatly expand your learning path.

What is Amazon Redshift?

Amazon Redshift is a fully managed, cloud-based data warehouse service designed to handle large-scale data processing and analytics. It enables businesses to analyze vast amounts of structured and semi-structured data quickly and efficiently. With its architecture tailored for high performance, Redshift allows users to run complex queries and generate insights in real time.

The Architecture of Amazon Redshift

Understanding the architecture of Redshift is crucial to appreciating its capabilities. Here are the key components:

1. Columnar Storage

Unlike traditional row-based databases, Redshift uses a columnar storage model. This approach allows for more efficient data retrieval, as only the necessary columns are accessed during queries, significantly speeding up performance.

2. Massively Parallel Processing (MPP)

Redshift employs a massively parallel processing architecture, distributing workloads across multiple nodes. This means that queries can be processed simultaneously, enhancing speed and efficiency.

3. Data Compression

Redshift automatically compresses data to save storage space and improve query performance. By reducing the amount of data that needs to be scanned, it accelerates query execution.

4. Snapshots and Backups

Redshift provides automated snapshots of your data warehouse. This feature ensures data durability and allows for easy restoration in case of failure, enhancing data security.

To master the intricacies of AWS and unlock its full potential, individuals can benefit from enrolling in the AWS Online Training.

Key Features of Amazon Redshift

1. Scalability

Redshift is designed to grow with your data. You can start with a small data warehouse and scale up to petabytes as your data needs expand. This scalability is vital for businesses experiencing rapid growth.

2. Integration with AWS Ecosystem

As part of AWS, Redshift integrates seamlessly with other services like Amazon S3, AWS Glue, and Amazon QuickSight. This integration simplifies data ingestion, transformation, and visualization, creating a cohesive data ecosystem.

3. Advanced Security Features

Redshift offers robust security measures, including data encryption, network isolation with Amazon VPC, and user access controls through AWS IAM. This ensures that your data remains secure and compliant with industry standards.

4. Cost-Effectiveness

With a pay-as-you-go pricing model, Redshift allows businesses to optimize costs based on their usage. Options for reserved instances further enhance cost savings, making it an attractive choice for organizations of all sizes.

Redshift supports a wide range of analytical queries, empowering businesses to explore data trends, customer behavior, and operational efficiencies comprehensively.

Conclusion

Amazon Redshift stands out as a powerful solution for cloud data warehousing and analytics. Its scalable architecture, high performance, and seamless integration with other AWS services make it an ideal choice for businesses looking to leverage their data effectively.

Whether you're a small startup or a large enterprise, Redshift can provide the tools you need to make data-driven decisions and stay competitive in today's data-centric landscape.

[govindhtech]

Amazon Redshift: A Quick-Start Guide To Data Warehousing

Amazon Redshift offers the finest price-performance cloud data warehouse to support data-driven decision-making.

What is Amazon Redshift?

Amazon Redshift leverages machine learning and technology created by AWS to provide the greatest pricing performance at any scale, utilizing SQL to analyze structured and semi-structured data across data lakes, operational databases, and data warehouses.

With only a few clicks and no data movement or transformation, you can break through data silos and obtain real-time and predictive insights on all of your data.

With performance innovation out of the box, you may achieve up to three times higher pricing performance than any other cloud data warehouse without paying extra.

Use a safe and dependable analytics solution to turn data into insights in a matter of seconds without bothering about infrastructure administration.

Why Amazon Redshift?

Every day, tens of thousands of customers utilize Amazon Redshift to deliver insights for their organizations and modernize their data analytics workloads. Amazon Redshift’s fully managed, AI-powered massively parallel processing (MPP) architecture facilitates swift and economical corporate decision-making. With AWS’s zero-ETL strategy, all of your data is combined for AI/ML applications, near real-time use cases, and robust analytics. With the help of cutting-edge security features and fine-grained governance, data can be shared and collaborated on safely and quickly both inside and between businesses, AWS regions, and even third-party data providers.

Advantages

At whatever size, get the optimal price-performance ratio

With a fully managed, AI-powered, massively parallel processing (MPP) data warehouse designed for speed, scale, and availability, you can outperform competing cloud data warehouses by up to six times.

Use zero-ETL to unify all of your data

Use a low-code, zero-ETL strategy for integrated analytics to quickly access or ingest data from your databases, data lakes, data warehouses, and streaming data.

Utilize thorough analytics and machine learning to optimize value

Utilize your preferred analytics engines and languages to run SQL queries, open source analytics, power dashboards and visualizations, and activate near real-time analytics and AI/ML applications.

Use safe data cooperation to innovate more quickly

With fine-grained governance, security, and compliance, you can effortlessly share and collaborate on data both inside and between your businesses, AWS regions, and even third-party data sets without having to move or copy data by hand.

How it works

In order to provide the best pricing performance at any scale, Amazon Redshift leverages machine learning and technology created by AWS to analyze structured and semi-structured data from data lakes, operational databases, and data warehouses using SQL.

Use cases

Boost demand and financial projections

Allows you to create low latency analytics apps for fraud detection, live leaderboards, and the Internet of Things by consuming hundreds of megabytes of data per second.

Make the most of your business intelligence

Using BI tools like Microsoft PowerBI, Tableau, Amazon QuickSight, and Amazon Redshift, create insightful reports and dashboards.

Quicken SQL machine learning

To support advanced analytics on vast amounts of data, SQL can be used to create, train, and implement machine learning models for a variety of use cases, such as regression, classification, and predictive analytics.

Make money out of your data

Create apps using all of your data from databases, data lakes, and data warehouses. To increase consumer value, monetize your data as a service, and open up new revenue sources, share and work together in a seamless and safe manner.

Easily merge your data with data sets from outside parties

Subscribe to and merge third-party data in AWS Data Exchange with your data in Amazon Redshift, whether it’s market data, social media analytics, weather data, or more, without having to deal with licensing, onboarding, or transferring the data to the warehouse.

Amazon Redshift concepts

Amazon Redshift Serverless helps you examine data without provisioning a data warehouse. Automatic resource provisioning and intelligent data warehouse capacity scaling ensure quick performance for even the most demanding and unpredictable applications. The data warehouse is free when idle, so you only pay for what you use. The Amazon Redshift query editor v2 or your favorite BI tool lets you load data and query immediately. Take advantage of the greatest pricing performance and familiar SQL capabilities in a zero-administration environment.

If your company is eligible and your cluster is being formed in an AWS Region without Amazon Redshift Serverless, you may be eligible for the free trial. Choose Production or Free trial to answer. For what will you use this cluster? Free trial creates a dc2.large node configuration. AWS Regions with Amazon Redshift Serverless are included in the Amazon Web Services General Reference’s Redshift Serverless API endpoints.

Key Amazon Redshift Serverless ideas are below

Namespace: Database objects and users are in a namespace. Amazon Redshift Serverless namespaces contain schemas, tables, users, datashares, and snapshots.

Workgroup: A collection of computer resources. Amazon Redshift Serverless computes in workgroups. Redshift Processing Units, security groups, and use limits are examples. Configure workgroup network and security settings using the Amazon Redshift Serverless GUI, AWS Command Line Interface, or APIs.

Important Amazon Redshift supplied cluster concepts:

Cluster: A cluster is an essential part of an Amazon Redshift data warehouse’s infrastructure.

A cluster has compute nodes. Compiled code runs on compute nodes.

An additional leader node controls two or more computing nodes in a cluster. Business intelligence tools and query editors communicate with the leader node. Your client application only talks to the leader. External apps can see computing nodes.

Database: A cluster contains one or more databases.

One or more computing node databases store user data. SQL clients communicate with the leader node, which organizes compute node queries. Read about compute and leader nodes in data warehouse system design. User data is grouped into database schemas.

Amazon Redshift is compatible with other RDBMSs. It supports OLTP functions including inserting and removing data like a standard RDBMS. Amazon Redshift excels at batch analysis and reporting.

Amazon Redshift’s typical data processing pipeline and its components are described below.

A example Amazon Redshift data processing path is shown below.Image credit to AWS

An enterprise-class relational database query and management system is Amazon Redshift. Business intelligence (BI), reporting, data, and analytics solutions can connect to Amazon Redshift. Analytic queries retrieve, compare, and evaluate vast volumes of data in various stages to obtain a result.

Multiple data sources upload structured, semistructured, and unstructured data to the data storage layer at the data ingestion layer. This data staging section holds data in various consumption readiness phases. Storage may be an Amazon S3 bucket.

The optional data processing layer preprocesses, validates, and transforms source data using ETL or ELT pipelines. ETL procedures enhance these raw datasets. ETL engines include AWS Glue.

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amazon web server

Amazon Web Services (AWS) is a comprehensive and widely adopted cloud platform, offering over 200 fully featured services from data centers globally. AWS provides a range of infrastructure services such as computing power, storage options, and networking capabilities, making it possible for businesses to host applications, manage databases, and leverage various tools for machine learning, analytics, and artificial intelligence.

Key Components and Services of AWS:

Compute Services:

Amazon EC2 (Elastic Compute Cloud): Virtual servers that allow users to run applications.

AWS Lambda: Serverless compute service that automatically runs code in response to events.

Amazon ECS (Elastic Container Service): Fully managed container orchestration service.

Amazon EKS (Elastic Kubernetes Service): Managed Kubernetes service.

Storage Services:

Amazon S3 (Simple Storage Service): Scalable object storage service.

Amazon EBS (Elastic Block Store): Block storage for use with EC2 instances.

Amazon Glacier: Low-cost archival storage.

Database Services:

Amazon RDS (Relational Database Service): Managed relational database service.

Amazon DynamoDB: NoSQL database service.

Amazon Redshift: Data warehousing service.

Networking Services:

Amazon VPC (Virtual Private Cloud): Isolated networks within the AWS cloud.

Amazon Route 53: Scalable domain name system (DNS) web service.

AWS Direct Connect: Dedicated network connection to AWS.

Security and Identity Services:

AWS IAM (Identity and Access Management): Manage access to AWS services and resources securely.

AWS KMS (Key Management Service): Create and manage cryptographic keys.

AWS Shield: Managed DDoS protection service.

Analytics Services:

Amazon Kinesis: Real-time data processing and streaming.

Amazon EMR (Elastic MapReduce): Big data processing using Hadoop.

Amazon QuickSight: Business intelligence service.

AI and Machine Learning Services:

Amazon SageMaker: Build, train, and deploy machine learning models.

Amazon Rekognition: Image and video analysis.

Amazon Comprehend: Natural language processing.

Developer Tools:

AWS CodePipeline: Continuous integration and continuous delivery service.

AWS CodeBuild: Build and test code.

AWS CodeDeploy: Automate software deployments.

Benefits of Using AWS:

Scalability: Easily scale applications up or down based on demand.

Cost-Effectiveness: Pay-as-you-go pricing model helps optimize costs.

Flexibility: Wide range of services and tools to choose from.

Security: Built-in security features and compliance certifications.

Global Reach: Extensive network of data centers around the world.

visit: https://www.izeoninnovative.com/izeon/

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Power of Data with Amazon QuickSight Certification training with Koenig Solutions.

Data is one of the most valuable assets in the modern world. Companies of all sizes are leveraging the power of data to make informed decisions and drive growth. However, to unlock the full potential of data, you need the right tools and skills. This is where Amazon QuickSight certification comes in. Amazon QuickSight is a powerful business analytics tool that makes it easy to visualize data and share insights across your organization. With QuickSight training, you can learn how to create and publish interactive dashboards that provide actionable insights, all in real-time. Why Amazon QuickSight Certification? In today’s data-driven world, AWS QuickSight certification is highly sought after. The certification validates your ability to work with Amazon QuickSight, AWS's cloudbased business intelligence service. It shows employers that you have the skills needed to analyze data effectively and make data-driven decisions. Moreover, the AWS QuickSight course covers essential topics like creating analysis, adding visualizations, connecting to data sources, and more. This comprehensive training ensures you are well-prepared to leverage QuickSight's full capabilities. How to Get Amazon QuickSight Certification? Getting certified in Amazon QuickSight is a straightforward process with Koenig Solutions. The training provider offers an in-depth course that covers all aspects of Amazon QuickSight. From setting up your QuickSight account to advanced features like creating calculated fields and using machine learning insights, the course has it all. Upon completing the course, you'll be ready to sit for the AWS Certified Big Data - Specialty exam, which includes Amazon QuickSight topics. Passing this exam will earn you the coveted AWS QuickSight certification. Conclusion Whether you're a data analyst, a business intelligence professional, or just someone who wants to make the most of their data, Amazon QuickSight certification is a valuable tool in your arsenal. With the right training, you can unlock the power of your data, providing the insights your company needs to thrive in the data-driven world. To know more about how you can become certified in Amazon QuickSight, visit Koenig Solutions

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Data Analytics in AWS

Data analytics in AWS involves leveraging a suite of services and tools to derive valuable insights from vast amounts of data stored on the Amazon Web Services platform. At its core, AWS provides scalable and flexible storage solutions such as Amazon S3 (Simple Storage Service) and Amazon Redshift for storing structured and unstructured data. Data ingestion tools like AWS Glue and Amazon Kinesis enable users to efficiently collect, process, and prepare data for analysis, whether it's streaming data or batch processing. AWS also offers a variety of analytics services, including Amazon Athena for interactive query analysis of data stored in S3, Amazon EMR (Elastic MapReduce) for big data processing using popular frameworks like Hadoop and Spark, and Amazon QuickSight for data visualization and business intelligence.

Furthermore, AWS provides machine learning (ML) and artificial intelligence (AI) services that enable advanced analytics capabilities, such as Amazon SageMaker for building, training, and deploying ML models, and Amazon Comprehend for natural language processing (NLP) tasks like sentiment analysis and entity recognition. Additionally, AWS Data Lakes solutions allow organizations to build secure and scalable data lake architectures for storing and analyzing large volumes of data across various sources. By leveraging these comprehensive data analytics offerings, organizations can gain actionable insights, drive data-driven decision-making, and unlock new opportunities for innovation and growth in today's data-driven world.

[learning-code-ficusoft]

Building a Smart IoT Application with AWS IoT Core

The Internet of Things (IoT) has revolutionized industries by enabling devices to connect, communicate, and make intelligent decisions. AWS IoT Core provides a robust platform to build, manage, and secure IoT applications at scale. This guide outlines the key steps to develop a smart IoT application using AWS IoT Core.

What is AWS IoT Core?

AWS IoT Core is a managed cloud platform that enables connected devices to interact securely with cloud applications and other devices. It supports MQTT, HTTP, and WebSocket protocols for device communication.

Key Features of AWS IoT Core

✅ Secure device communication via X.509 certificates.  ✅ Supports real-time data processing with AWS Lambda and Amazon Kinesis.  ✅ Integrates seamlessly with AWS services like DynamoDB, S3, and CloudWatch.  ✅ Offers Device Shadow for offline state management.

Step 1: Set Up AWS IoT Core

Sign in to the AWS Management Console.

Navigate to AWS IoT Core.

In the left menu, go to Manage → Things.

Click Create Thing and provide:

Name: A meaningful identifier for your IoT device.

Device Shadow: Enable it if you want AWS IoT to store device state information.

Click Next, generate certificates, and download them securely for authentication.

Step 2: Register and Connect IoT Devices

Go to Secure → Certificates in AWS IoT Core.

Upload the certificate generated during Thing creation.

Attach an IoT Policy that grants permissions for device connectivity.

Install the certificate and private key on your IoT device.

Use the MQTT protocol to connect your device using the provided endpoint.

Example Code (Python — MQTT Connection):pythonimport paho.mqtt.client as mqttclient = mqtt.Client() client.tls_set("path/to/AmazonRootCA1.pem", certfile="path/to/device-certificate.pem.crt", keyfile="path/to/private.pem.key") client.connect("your-iot-endpoint.amazonaws.com", 8883) client.publish("iot/topic", "Hello from IoT Device!")

Step 3: Create an IoT Rule for Data Routing

In AWS IoT Core, navigate to Act → Rules.

Click Create a rule and define the following:

Name: Provide a meaningful name.

SQL Statement: Use SQL-like syntax to filter data from specific MQTT topics.

Example Rule Statement:sqlSELECT temperature, humidity FROM 'iot/sensor/data' WHERE temperature > 30

Choose an Action, such as:

Sending data to Amazon S3 for storage.

Triggering AWS Lambda for real-time processing.

Publishing alerts to Amazon SNS for notifications.

Step 4: Implement Data Storage and Processing

Use Amazon DynamoDB to store structured IoT data for real-time queries.

Utilize AWS Lambda to process data and trigger automated actions.

Leverage Amazon Kinesis for real-time analytics and dashboards.

Step 5: Visualizing Data with Amazon QuickSight

In Amazon QuickSight, create a new dataset using data stored in Amazon S3 or DynamoDB.

Build interactive dashboards to track device performance, environmental conditions, or system health.

Use visual charts to analyze patterns, trends, and anomalies.

Step 6: Enhancing Security

Use AWS IoT Device Defender to monitor security threats.

Enable AWS IoT Policies to manage fine-grained permissions for each device.

Secure communication using TLS 1.2 encryption and X.509 certificates.

Step 7: Testing and Deployment

Simulate multiple devices to validate your application’s scalability.

Monitor data flow in AWS IoT Core’s Test console.

Deploy the application using AWS IoT Greengrass for edge computing scenarios.

Conclusion

Building a smart IoT application with AWS IoT Core empowers developers to connect devices securely, process data in real-time, and build scalable solutions. By integrating AWS services like Lambda, DynamoDB, and QuickSight, you can create powerful IoT applications that drive actionable insights.

WEBSITE: https://www.ficusoft.in/aws-training-in-chennai/

[sophiamerlin]

Amazon Web Services

Amazon Web Services (AWS) is a comprehensive cloud computing platform offered by Amazon, providing a wide array of cloud services that allow individuals and organizations to access and utilize computing resources over the internet.

AWS is a leading provider in the cloud computing industry and offers a scalable and flexible infrastructure to support various applications and business needs.

Here are some key aspects and uses of AWS:

Compute Services: AWS provides various compute services such as Amazon EC2 (Elastic Compute Cloud), which allows users to run virtual servers in the cloud. This is useful for hosting applications, websites, and handling other computing tasks.

Storage Services: AWS offers different storage options like Amazon S3 (Simple Storage Service), which provides scalable and highly available object storage. It's used for storing and retrieving any amount of data, making it useful for backups, content distribution, and more.

Databases: AWS provides managed database services like Amazon RDS (Relational Database Service) and Amazon DynamoDB, offering scalable and reliable database solutions without the need for managing the underlying infrastructure.

Networking: AWS offers a range of networking services including Virtual Private Cloud (VPC) for creating isolated network environments, AWS Direct Connect for dedicated network connections to AWS, and Amazon Route 53 for domain registration and DNS management.

Content Delivery and CDN: AWS has services like Amazon CloudFront, which is a content delivery network (CDN) that helps distribute content globally with low latency and high transfer speeds.

Machine Learning and Artificial Intelligence: AWS provides services such as Amazon SageMaker for building, training, and deploying machine learning models, as well as AI-powered services like Amazon Comprehend for natural language processing and Amazon Recognition for image and video analysis.

Serverless Computing: AWS offers serverless computing through services like AWS Lambda, where users can run code in response to events without managing servers, enabling cost-efficient and scalable applications.

Analytics: AWS provides analytics services like Amazon Redshift for data warehousing, Amazon EMR for big data processing, and Amazon QuickSight for business intelligence and visualization.

Internet of Things (IoT): AWS IoT Core enables connecting and managing IoT devices, collecting and processing data from them securely in the cloud.

Security and Identity: AWS offers various security services, including AWS Identity and Access Management (IAM) for access control, AWS Key Management Service (KMS) for encryption, and AWS WAF (Web Application Firewall) for web application security.

Overall, AWS is used for various purposes such as hosting websites, running applications, storing and analyzing data, implementing machine learning and AI solutions, and managing a wide range of computing needs in a flexible, scalable, and cost-effective manner.

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[govindhtech]

Clarity AI and AWS Boost Trading for Effective Profits!

Clarity AI, a leading technology firm that provides environmental and social analytics to help companies and consumers invest and buy responsibly, joined AWS today. Clarity AI helps investors manage, quantify, and optimize their investment portfolios’ social and sustainability effect and comply with regulatory reporting by analyzing millions of data points. These data enable 150 million buyers to buy sustainable items and help investors allocate $30 trillion to environmentally friendly enterprises.

According to Ángel Agudo, board director and senior vice president of Product at Clarity AI, AWS offers the necessary cloud services, flexibility, and scale for data-driven companies to leverage AI and provide sustainability insights to decision-makers. “At Clarity AI, we want investors and businesses to value social and environmental impact beyond financial values.”

AWS’s generative AI, machine learning (ML), and analytics capabilities enable Clarity AI’s mission-critical platforms to provide sustainability analysis tools for investing, corporate research, benchmarking, consumer ecommerce purchasing, and regulatory reporting. Clarity AI trains up to 7 billion parameter large language models (LLMs) and natural language processing (NLP) models using Amazon SageMaker, a fully managed service to build, train, and deploy ML models, Amazon SageMaker Studio, and Amazon EC2 GPU instances.

These methods identify, organize, and classify millions of unstructured data points from sustainability, financial, profitability, and research reports. Clarity AI then utilizes this data to identify enterprises affected by a news occurrence and assess environmental severity. This impartial information helps investors, academics, and consumers make sustainable buying and investing decisions.

Clarity AI’s sustainability technology platform provides objective data on over 70,000 firms, 420,000 funds, and 400 local governments using AWS cloud capabilities. Ecommerce organizations visually highlight sustainable brands to buyers using the company’s environmental study of technology, fashion and apparel, grocery and household brands, and health and beauty. Clarity AI’s platform interfaces with ecommerce platforms to display badges that acknowledge a brand’s sustainability performance, allowing eco-conscious buyers to reward sustainable practices.

Environmental, social, and governance (ESG) reports that disclose an organization’s environmental impact influence investors and customers today. Investors utilize these reports to track a company’s environmental and social effect, but the lack of clear reporting standards, increasing ESG rules, and fragmented data make ESG performance data validation difficult.

Clarity AI used Amazon SageMaker to create generative AI models on AWS to find new data points to help clients avoid greenwashing or hazardous environmental activities. Clarity AI extracts data, identifies concerns, and assesses environmental impact using NLP models. Amazon QuickSight, AWS’s business intelligence solution, lets Clarity AI show insights in modern, interactive dashboards, reports, integrated analytics, and natural language queries. Clarity AI’s product specialists work more efficiently with a generative AI–powered chatbot on AWS to manage client requests.

AWS manages security, governance, and regulatory compliance for the organization. Clarity AI uses Amazon GuardDuty, a threat monitoring service, and AWS WAF, a web firewall, to protect against typical web attacks and bots that can damage availability and security. AWS security and compliance services helped Clarity AI receive ISO 27001 and SOC 2 Type II certifications.

Clarity AI plans to use more AWS generative AI and machine learning services like Amazon Bedrock, which provides multiple foundation models via API, to harness innovative technologies, streamline model development, and improve inference.

“Generative AI has the potential to transform every application, business, and industry, and Clarity AI is a great example of how this technology can help business, society, and the planet make more sustainable investments and purchase decisions,” said Matt Garman, AWS senior vice president of Sales, Marketing, and Global Services. With customers like Clarity AI, AWS believes that choice and security are the winning combination to let enterprises use generative AI to rethink consumer experiences.

About Amazon

Customer obsession over competitive focus, passion for creativity, operational excellence, and long-term thinking govern Amazon. Amazon wants to be Earth’s Most Customer-Centric, Best Employer, and Safest Workplace. Amazon pioneered customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology, Amazon Studios, and The Climate Pledge.

About Clarity AI

Sustainability technology platform Clarity AI provides investors, businesses, and consumers with environmental and social insights using machine learning and big data. Clarity AI is vital for end-to-end sustainability analysis in investing, corporate research, benchmarking, consumer ecommerce, and regulatory reporting. Clarity AI examines more than 70,000 enterprises, 420,000 funds, and 400 countries as of September 2023, more than any other market competitor.

Clarity AI integrates its capabilities into clients’ operations with partners like BlackRock Aladdin, Refinitiv an LSEG subsidiary, BNP Manaos, CACEIS, and SimCorp to bring societal impact to markets. Klarna’s 150 million consumers and 400,000 merchants receive Clarity AI’s sustainability insights. Clarity AI has locations across North America, Europe, and the Middle East. Its clients include Invesco, Nordea, BlackRock, Santander, Wellington, and BNP Paribas, which handle tens of billions.

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[paruabeautystudio]

Subjects covered during the AI AWS - Specialty test

Per the AWS test guide, you will be tested on 4 key regions:

Information Designing (20%): S3 (and VPC Endpoint Entryway), Kinesis (Transfers, FireHose, Information Investigation, Video), Paste (Information List and Crawler), Athena, AWS Information Stores (Redshift, RDS/Aurora, DynamoDB, ElasticSearch, ElastiCache), Aws Course Training Information Pipelines, AWS Group, AWS DMS, and AWS Step Capabilities.

Exploratory Information Examination (24%): Information Types and Dissemination, Time Series, Amazon Athena, Quicksight, Ground Truth, EMR, Flash, Information binning, Changing, Encoding, Scaling and Rearranging, Managing Missing information, Imbalanced information, and Anomalies.

Demonstrating (36%): CNN, RNN, Tuning brain organizations, Regularization, Inclination plummet technique, L1 and L2 regularization, Disarray lattice (Accuracy, Review, F1, AUC), Troupe strategies (Sacking and Helping), Amazon Sagemaker, Amazon Calculations (Straight Student, XGBoost, Seq2Seq, BlazingText, DeepAR, Object2Vec, ObjectDetection, Picture Characterization, Semantic Division, RCF, LDA, KNN, K-Means, PCA, Factorization Machine), and Amazon artificial intelligence Administrations (Appreciate, Decipher, Interpret, Polly, Rekognition, Conjecture, Lex and so on).

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AWS and machine learning

AWS (Amazon Web Services) is a collection of remote computing services (also called web services) that make up a cloud computing platform, offered by Amazon.com. These services operate from 12 geographical regions across the world.

AWS provides a variety of services for machine learning, including:

Amazon SageMaker is a fully-managed platform for building, training, and deploying machine learning models.

Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning.

AWS Deep Learning AMIs, pre-built Amazon Machine Images (AMIs) that make it easy to get started with deep learning on Amazon EC2.

AWS Deep Learning Containers, Docker images pre-installed with deep learning frameworks to make it easy to run distributed training on Amazon ECS.

Additionally, AWS also provides services for data storage, data processing, and data analysis which are essential for machine learning workloads. These services include Amazon S3, Amazon Kinesis, Amazon Redshift, and Amazon QuickSight.

In summary, AWS provides a comprehensive set of services that allow developers and data scientists to build, train, and deploy machine learning models easily and at scale.

AWS also provides several other services that can be used in conjunction with machine learning. These include:

Amazon Comprehend is a natural language processing service that uses machine learning to extract insights from text.

Amazon Transcribe is a service that uses machine learning to transcribe speech to text.

Amazon Translate is a service that uses machine learning to translate text from one language to another.

Amazon Rekognition is a service that uses machine learning to analyze images and videos, detect objects, scenes, and activities, and recognise faces, text, and other content.

AWS also provides a number of tools and frameworks that can be used to build and deploy machine learning models, such as:

TensorFlow is an open-source machine learning framework that is widely used for building and deploying neural networks.

Apache MXNet, a deep learning framework that is fully supported on AWS.

PyTorch is an open-source machine-learning

 library for Python that is also fully supported on AWS.

AWS SDKs for several programming languages, including Python, Java, and .NET, which make it easy to interact with AWS services from your application.

AWS also offers a number of programs and resources to help developers and data scientists learn about machine learning, including the Machine Learning University, which provides a variety of courses, labs, and tutorials on machine learning topics, and the AWS Machine Learning Blog, which features articles and case studies on the latest developments in machine learning and how to use AWS services for machine learning workloads.

In summary, AWS provides a wide range of services, tools, and resources for building and deploying machine learning models, making it a powerful platform for machine learning workloads at any scale.

[amazonquicksighttraining]

Amazon QuickSight Training Course | AWS QuickSight Online Training

AWS QuickSight vs. Tableau: Which Data Visualization Tool is Right for You?

Amazon QuickSight Training, you're likely exploring advanced business intelligence and data visualization tools to elevate your analytical capabilities. AWS QuickSight and Tableau are two leading solutions in this domain, each with unique features catering to diverse user needs. Whether you're a business looking for cost efficiency or a professional seeking robust features, choosing the right tool is crucial.

Overview of AWS QuickSight and Tableau

AWS QuickSight, Amazon's cloud-based BI solution, is designed to integrate seamlessly with other AWS services. It enables users to analyze data and share insights through interactive dashboards. On the other hand, Tableau, now part of Salesforce, is a veteran in the BI space, renowned for its user-friendly interface and extensive capabilities in data analysis.

AWS QuickSight shines with its cost-effectiveness and integration with Amazon Web Services, making it a favorite for businesses already using AWS. Tableau, however, excels in providing detailed, customizable dashboards and advanced analytics, catering to users needing more granular control.

Ease of Use

For beginners, AWS QuickSight offers a simpler, more intuitive interface, making it an excellent choice for users who prefer to avoid steep learning curves. Many users who undergo AWS QuickSight Online Training appreciate its guided learning paths and ease of implementation, especially when managing data from AWS sources. Its automated insights feature allows for faster decision-making, a key advantage for businesses with tight deadlines.

Tableau, while robust, has a steeper learning curve. Advanced users or those familiar with similar tools will find its extensive customization options invaluable. However, for new users, investing time in training is necessary to harness its full potential.

Integration Capabilities

AWS QuickSight integrates effortlessly with Amazon’s ecosystem, such as S3, Redshift, and RDS. This makes it a preferred choice for businesses already operating within the AWS framework. By enrolling in Amazon QuickSight Training, users can master these integrations, leveraging them to drive better decision-making.

Tableau, on the other hand, offers broad integration capabilities beyond cloud services, supporting various databases, third-party apps, and cloud platforms like Google Cloud and Azure. This flexibility makes it ideal for companies with heterogeneous IT infrastructures.

Scalability and Performance

AWS QuickSight boasts impressive scalability, making it a go-to option for businesses experiencing rapid growth. Its pay-per-session pricing model ensures affordability, even as user demand scales. This feature is highly valued by startups and SMBs, where cost management is crucial. QuickSight's serverless architecture means performance remains high, regardless of user volume, which is emphasized in AWS QuickSight Online Training modules.

Tableau provides excellent performance for static environments but may require additional resources for scaling, especially in enterprise setups. Tableau’s licensing can also be cost-prohibitive for smaller teams, making AWS QuickSight a more economical alternative in such scenarios.

Customization and Advanced Features

For users seeking deep customization and advanced analytics, Tableau has the edge. Its vast library of pre-built visualizations and tools like Tableau Prep for data cleaning are unmatched. However, AWS QuickSight has been catching up with features like SPICE (Super-fast, Parallel, In-memory Calculation Engine) and ML Insights. These innovations enable QuickSight to deliver insights faster and support advanced analytical needs, which are integral to any Amazon QuickSight Training curriculum.

Cost Considerations

AWS QuickSight is known for its cost-effective pricing, particularly its pay-per-session model, which eliminates the need for upfront investments. This makes it accessible to businesses of all sizes. Tableau, while offering rich features, follows a subscription-based pricing model that can be expensive, especially for large teams or enterprise setups. For organizations looking to maximize their ROI, AWS QuickSight Online Training can help users extract maximum value from this tool.

Key Use Cases

AWS QuickSight: Ideal for organizations deeply integrated with AWS, looking for scalable, cost-effective BI tools.

Tableau: Best suited for businesses requiring highly detailed analytics and those with diverse IT infrastructures.

Why Training is Essential?

For both tools, training plays a crucial role in maximizing their potential. Whether it's mastering AWS QuickSight’s seamless AWS integrations or Tableau’s intricate visualization capabilities, a structured learning path is essential. Enrolling in Amazon QuickSight Training or other specialized courses ensures users can confidently navigate features, optimize workflows, and derive actionable insights.

Conclusion      

Both AWS QuickSight and Tableau have unique strengths, making them suitable for different scenarios. AWS QuickSight’s simplicity, cost-effectiveness, and integration with the AWS ecosystem make it an excellent choice for small to medium-sized businesses and startups. Tableau, with its advanced customization and broader integration capabilities, is a better fit for enterprises needing sophisticated analytics.

By enrolling in Amazon QuickSight Training or AWS QuickSight Online Training, users can develop the skills necessary to unlock the full potential of these tools. Ultimately, the choice between AWS QuickSight and Tableau depends on your specific business needs, budget, and the level of complexity required in your data visualization efforts. Both are powerful tools that can transform how businesses interact with and interpret their data, driving smarter decisions and better outcomes.

Visualpath offers AWS QuickSight Online Training for the next generation of intelligent business applications. AWS QuickSight Training in Hyderabad from industry experts and gain hands-on experience with our interactive program. Accessible globally, including in the USA, UK, Canada, Dubai, and Australia. With daily recordings and presentations available for later review. To book a free demo session, for more info, call +91-9989971070.

Key Points: AWS, Amazon S3, Amazon Redshift, Amazon RDS, Amazon Athena, AWS Glue, Amazon DynamoDB, AWS IoT Analytics, ETL Tools.

Attend Free Demo

Call Now: +91-9989971070

Whatsapp:  https://www.whatsapp.com/catalog/919989971070

Visit our Blog: https://visualpathblogs.com/

Visit: https://www.visualpath.in/online-amazon-quicksight-training.html

[silstonegroup]

Population Health and Healthcare analysis made easy by Amazon Healthlake

With the advent of EMR and Medical IoT, Healthcare Data has exploded. This data is very complex, and various tools and techniques need to be tailor-made for every source so that it can be useful and eventually transform healthcare by being able to make predictions that will enable us to deliver tailored treatments and preventative measures to the population at large. The management of this data also becomes very critical for Population Health Management and Value-based Care that relies on accurate predictions of estimates on the costs involved in keeping a population healthy. Much of the data is collected in clinical settings, is unstructured, and needs to be worked on before it can be used.

This need for extraction and transformation of Population Health data is addressed by Amazon HealthLakes by using integrated medical natural language processing (NLP) using machine learning (ML) models that have been trained to understand and extract meaningful information. This transformed data is then added to the patient’s record, providing a complete view of all of the patient’s attributes (such as medications, tests, procedures, and diagnoses) that is optimized for search and applying advanced analytics. Advanced Healthcare Analysis, when applied, provides the clinician with a comprehensive dashboard of Amazon HealthLake data to create holistic patient assessment over time, and compare that data with the population at large. HealthLake creates exciting new possibilities for extracting medical entities from unstructured data and quickly iterating as sources and datasets grow. The dashboard is designed to be easy to use and aims to help clinicians and health administrators make informed decisions and improve patient care withought getting into unnecessary complexity. One can start building a dashboard on raw FHIR data by importing it into Amazon S3, creating AWS Glue crawlers and Data Catalog tables, and creating a QuickSight dashboard.

Check out the link to know more about Amazon HealthLake: https://aws.amazon.com/healthlake/

Read our blog for more information about the impacts of Technology on Healthcare: https://www.silstonegroup.com/insights

Some of the work we have been doing: https://www.silstonegroup.com/home/portfolio

[learning-code-ficusoft]

E-commerce on AWS: Tools and Strategies for Online Stores

Introduction

In today’s digital-first world, e-commerce businesses require scalable, secure, and high-performing infrastructure to thrive. Amazon Web Services (AWS) provides a comprehensive suite of cloud services tailored to online stores, helping businesses scale efficiently, enhance security, and optimize costs. In this blog, we’ll explore the key AWS tools and strategies that power successful e-commerce platforms.

Why Choose AWS for E-commerce?

Scalability: AWS can handle sudden traffic spikes, ensuring smooth shopping experiences.

Security: Built-in compliance with PCI DSS and advanced security measures safeguard customer data.

Global Reach: AWS’s extensive infrastructure enables fast content delivery worldwide.

Cost Optimization: Pay-as-you-go pricing reduces upfront investment.

Reliability: High availability and disaster recovery options ensure uninterrupted service.

Essential AWS Tools for E-commerce

1. Hosting and Compute Power

Amazon EC2: Provides scalable compute power for hosting websites and applications.

AWS Lambda: Enables serverless execution of functions for event-driven processes.

Elastic Load Balancing (ELB): Distributes incoming traffic to enhance performance and availability.

2. Storage and Database Management

Amazon S3: Secure and scalable storage for product images, videos, and backups.

Amazon RDS: Managed relational database service for transactional data.

Amazon DynamoDB: NoSQL database for handling high-speed transactions at scale.

Amazon ElastiCache: Enhances site performance by caching frequently accessed data.

3. Security and Compliance

AWS Shield & WAF: Protects against DDoS attacks and malicious traffic.

Amazon Cognito: Manages authentication for user sign-ups and logins.

AWS IAM: Controls access to AWS resources with fine-grained permissions.

4. Payment Processing and Fraud Prevention

AWS Marketplace for Payment Gateways: Supports integrations with Stripe, PayPal, and other processors.

Amazon Fraud Detector: Uses machine learning to identify fraudulent transactions.

5. Content Delivery and User Experience

Amazon CloudFront: Accelerates content delivery globally with a robust CDN.

AWS Amplify: Simplifies front-end and mobile development for e-commerce platforms.

6. Analytics and Business Intelligence

Amazon QuickSight: Provides visual dashboards for sales and user behavior insights.

AWS Glue: Automates ETL processes for data integration.

Amazon Personalize: AI-driven recommendations for personalized shopping experiences.

Best Practices for Running an E-commerce Store on AWS

Optimize Costs: Use AWS Auto Scaling and Spot Instances to reduce costs.

Enhance Performance: Leverage caching, CDNs, and database optimization.

Prioritize Security: Implement IAM roles, encrypt sensitive data, and enable monitoring.

Ensure High Availability: Use multi-region deployments and automatic backups.

Utilize AI/ML: Enhance customer engagement with personalized product recommendations.

Conclusion

AWS provides a powerful cloud ecosystem for e-commerce businesses, offering flexibility, security, and cost-effectiveness. By leveraging AWS tools and best practices, online stores can enhance user experience, manage traffic surges efficiently, and ensure seamless scalability.

WEBSITE: https://www.ficusoft.in/aws-training-in-chennai/

[netsmp]

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[singgasinghrajput-blog]

AWS Training in Ahmedabad

AWS Cloud Computing is one of those jobs in the IT sector that have gathered a lot of limelight in the recent years. AWS training has become one of the hottest certifications in the tech world, and as more and more recruiters are looking forward to employing individuals with this qualification, more and more people are getting certified in AWS Cloud Computing.

The demand for professionals who have completed AWS training in Ahmedabad is high. As an AWS certified professional, you will be able to run application and web servers on cloud in order to host client-facing websites and internal applications. Also, you will be able to conduct reliable data analytics with the aid of AWS Kinesis, AWS QuickSight, and AWS Glue. Management systems like MySQL, PostgreSQL, Oracle, and SQL can be worked with to create managed databases.

There are numerous benefits of getting yourself enrolled into AWS training course. These benefits will benefit you as an individual as well as help you as a professional. Whether you are a student freshly out of college or you are an IT professional who is looking to expand their career aspects, getting certified in AWS will give a major boost to all of those. Here is a list of some of the benefits of being certified in AWS Cloud Computing:

 ·       Opening Doors to New Opportunities: With the ever increasing competition in the job sector, everyone is looking out for new opportunities that will give them more contentment and pay better. With the IT sector spreading so widely in India, one can say that getting certified to be eligible to work in it will open doors to new opportunities.

·       Showcase the Level of your Commitment: Getting certified in AWS Cloud Computing is not just time consuming, but also takes a lot of efforts and can also be expensive. Thus, getting this certification will showcase the level of your commitment.

·       Increased Salary: As your certifications and qualifications go higher, so does your salary structure. With AWS training certification you can be sure to have increased salary.

·       Better Projects: Since your certifications are more and better than your colleagues, the projects you end up with are also likely to be better than them. Certifications show your knowledge, skills, and capabilities.

·       Better Future Prospects: More career related certifications mean better job opportunities, better salary structure, better pay hike, and a chance to land in better job role. Thus, making your future prospects look brighter.

·       Expanded Professional Network: With the certification, you are more likely to end up in social and professional circles where you will find individuals who have similar qualifications as you do. Thus, expanding your professional network to include individuals with similar knowledge, skill set, and interests.

Getting AWS training in Ahmedabad has become a sought-after prospect for individuals who wish to make an impact in their IT career right from the start. With all the perks that come along with this certification, there is no questioning the impact AWS certification can have on one’s career path.