Category: Amazon Data Firehose

In this post, we explore how PackScan uses Amazon cloud-based services to drive real-time visibility, improve logistics efficiency, and support the seamless movement of packages across Amazon’s Middle Mile network.

Airties is a wireless networking company that provides AI-driven solutions for enhancing home connectivity. This post explores the strategies the Airties team employed during their migration from Apache Kafka to Amazon Kinesis Data Streams, the challenges they overcame, and how they achieved a more efficient, scalable, and maintenance-free streaming infrastructure.

In this post, we show you how to create Iceberg tables in Amazon SageMaker Unified Studio and stream data to these tables using Firehose. With this integration, data engineers, analysts, and data scientists can seamlessly collaborate and build end-to-end analytics and ML workflows using SageMaker Unified Studio, removing traditional silos and accelerating the journey from data ingestion to production ML models.

In this post, we demonstrate how to build a scalable AWS WAF log analysis solution using Firehose and Apache Iceberg. Firehose simplifies the entire process—from log ingestion to storage—by allowing you to configure a delivery stream that delivers AWS WAF logs directly to Apache Iceberg tables in Amazon S3. The solution requires no infrastructure setup and you pay only for the data you process.

Recently, AWS introduced over 50 new capabilities across its streaming services, significantly enhancing performance, scale, and cost-efficiency. Some of these innovations have tripled performance, provided 20 times faster scaling, and reduced failure recovery times by up to 90%. We have made it nearly effortless for customers to bring real-time context to AI applications and lakehouses. In this post, we discuss the top six game changers that will redefine AWS streaming data.

These organizations use third-party satellite-powered terminal devices for remote monitoring using telemetry and NMEA-0183 formatted messages generated in near real time. This post demonstrates how to implement a satellite-based remote alerting and response solution on the AWS Cloud to provide time-critical alerts and actionable insights, with a focus on telemetry message ingestion and alerts. Key services in the solution include Amazon API Gateway, Amazon Data Firehose, and Amazon Location Service.

In this post, we discuss how you can send real-time data streams into Iceberg tables on Amazon S3 by using Amazon Data Firehose. Amazon Data Firehose simplifies the process of streaming data by allowing users to configure a delivery stream, select a data source, and set Iceberg tables as the destination. Once set up, the Firehose stream is ready to deliver data.

Today’s fast-paced world demands timely insights and decisions, which is driving the importance of streaming data. Streaming data refers to data that is continuously generated from a variety of sources. The sources of this data, such as clickstream events, change data capture (CDC), application and service logs, and Internet of Things (IoT) data streams are […]

You can use Amazon Data Firehose to aggregate and deliver log events from your applications and services captured in Amazon CloudWatch Logs to your Amazon Simple Storage Service (Amazon S3) bucket and Splunk destinations, for use cases such as data analytics, security analysis, application troubleshooting etc. By default, CloudWatch Logs are delivered as gzip-compressed objects. […]

Foundation models (FMs) are large machine learning (ML) models trained on a broad spectrum of unlabeled and generalized datasets. FMs, as the name suggests, provide the foundation to build more specialized downstream applications, and are unique in their adaptability. They can perform a wide range of different tasks, such as natural language processing, classifying images, […]