Remove Common streaming data enrichment patterns in Amazon Kinesis Data Analytics for Apache Flink
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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

We’re living in the age of real-time data and insights, driven by low-latency data streaming applications. The volume of time-sensitive data produced is increasing rapidly, with different formats of data being introduced across new businesses and customer use cases.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

FMs are multimodal; they work with different data types such as text, video, audio, and images. Large language models (LLMs) are a type of FM and are pre-trained on vast amounts of text data and typically have application uses such as text generation, intelligent chatbots, or summarization.

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Migrate from Amazon Kinesis Data Analytics for SQL Applications to Amazon Kinesis Data Analytics Studio

AWS Big Data

Amazon Kinesis Data Analytics makes it easy to transform and analyze streaming data in real time. We also show how to use Kinesis Data Analytics Studio to test and tune your analysis before deploying your migrated applications.

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A side-by-side comparison of Apache Spark and Apache Flink for common streaming use cases

AWS Big Data

Apache Flink and Apache Spark are both open-source, distributed data processing frameworks used widely for big data processing and analytics. Spark is known for its ease of use, high-level APIs, and the ability to process large amounts of data.

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Implement Apache Flink near-online data enrichment patterns

AWS Big Data

Stream data processing allows you to act on data in real time. Real-time data analytics can help you have on-time and optimized responses while improving the overall customer experience. Pre-loading of reference data provides low latency and high throughput.

Testing 85
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Accelerating revenue growth with real-time analytics: Poshmark’s journey

AWS Big Data

In this post, we share how Poshmark improved CX and accelerated revenue growth by using a real-time analytics solution. High-level challenge: The need for real-time analytics Previous efforts at Poshmark for improving CX through analytics were based on batch processing of analytics data and using it on a daily basis to improve CX.

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Implement Apache Flink real-time data enrichment patterns

AWS Big Data

Stream data processing allows you to act on data in real time. Real-time data analytics can help you have on-time and optimized responses while improving the overall customer experience. Pre-loading of reference data provides low latency and high throughput.

Testing 52