Remove Analytics Remove Data Processing Remove Events Remove Internet of Things
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Architectural Patterns for real-time analytics using Amazon Kinesis Data Streams, Part 2: AI Applications

AWS Big Data

Welcome back to our exciting exploration of architectural patterns for real-time analytics with Amazon Kinesis Data Streams! Before we dive in, we recommend reviewing Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1 for the basic functionalities of Kinesis Data Streams.

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Gain insights from historical location data using Amazon Location Service and AWS analytics services

AWS Big Data

The solution consists of the following interfaces: IoT or mobile application – A mobile application or an Internet of Things (IoT) device allows the tracking of a company vehicle while it is in use and transmits its current location securely to the data ingestion layer in AWS. The ingestion approach is not in scope of this post.

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Ingest and analyze your data using Amazon OpenSearch Service with Amazon OpenSearch Ingestion

AWS Big Data

Whether it’s customer information, sales records, or sensor data from Internet of Things (IoT) devices, the importance of handling and storing data at scale with ease of use is paramount. Amazon SQS receives an Amazon S3 event notification as a JSON file with metadata such as the S3 bucket name, object key, and timestamp.

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Invoke AWS Lambda functions from cross-account Amazon Kinesis Data Streams

AWS Big Data

In a streaming architecture, you may have event producers, stream storage, and event consumers in a single account or spread across different accounts depending on your business and IT requirements. Download and launch CloudFormation template 2 where you want to host the Lambda consumer.

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Optimize write throughput for Amazon Kinesis Data Streams

AWS Big Data

Whether your data streaming application is collecting clickstream data from a web application or recording telemetry data from billions of Internet of Things (IoT) devices, streaming applications are highly susceptible to a varying amount of data ingestion. Why do we get write throughput exceeded errors?

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Break data silos and stream your CDC data with Amazon Redshift streaming and Amazon MSK

AWS Big Data

Traditionally, customers used batch-based approaches for data movement from operational systems to analytical systems. A batch-based approach can introduce latency in data movement and reduce the value of data for analytics. Data silos causes data to live in different sources, which makes it difficult to perform analytics.

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Top 10 IT & Technology Buzzwords You Won’t Be Able To Avoid In 2020

datapine

Visual analytics: Around three million images are uploaded to social media every single day. In business intelligence, we are evolving from static reports on what has already happened to proactive analytics with a live dashboard assisting businesses with more accurate reporting. Internet of Things. Connected Retail.