AWS Big Data Blog

Category: Kinesis Data Streams

Build a data lake using Amazon Kinesis Data Streams for Amazon DynamoDB and Apache Hudi

Amazon DynamoDB helps you capture high-velocity data such as clickstream data to form customized user profiles and online order transaction data to develop customer order fulfillment applications, improve customer satisfaction, and get insights into sales revenue to create a promotional offer for the customer. It’s essential to store these data points in a centralized data […]

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The following diagram illustrates the architecture of this intermediate pipeline to generate training data.

Retaining data streams up to one year with Amazon Kinesis Data Streams

Streaming data is used extensively for use cases like sharing data between applications, streaming ETL (extract, transform, and load), real-time analytics, processing data from internet of things (IoT) devices, application monitoring, fraud detection, live leaderboards, and more. Typically, data streams are stored for short durations of time before being loaded into a permanent data store […]

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Validate, evolve, and control schemas in Amazon MSK and Amazon Kinesis Data Streams with AWS Glue Schema Registry

Data streaming technologies like Apache Kafka and Amazon Kinesis Data Streams capture and distribute data generated by thousands or millions of applications, websites, or machines. These technologies serve as a highly available transport layer that decouples the data-producing applications from data processors. However, the sheer number of applications producing, processing, routing, and consuming data can […]

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Building an ad-to-order conversion engine with Amazon Kinesis, AWS Glue, and Amazon QuickSight

Businesses in ecommerce have the challenge of measuring their ad-to-order conversion ratio for ads or promotional campaigns displayed on a webpage. Tracking the number of users that clicked on a particular promotional ad and the number of users who actually added items to their cart or placed an order helps measure the ad’s effectiveness. Utilizing […]

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Building a scalable streaming data processor with Amazon Kinesis Data Streams on AWS Fargate

Data is ubiquitous in businesses today, and the volume and speed of incoming data are constantly increasing. To derive insights from data, it’s essential to deliver it to a data lake or a data store and analyze it. Real-time or near-real-time data delivery can be cost prohibitive, therefore an efficient architecture is key for processing, […]

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Migrating from Vertica to Amazon Redshift

Amazon Redshift powers analytical workloads for Fortune 500 companies, startups, and everything in between. With Amazon Redshift, you can query petabytes of structured and semi-structured data across your data warehouse, operational database, and your data lake using standard SQL. When you use Vertica, you have to install and upgrade Vertica database software and manage the […]

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Unified serverless streaming ETL architecture with Amazon Kinesis Data Analytics

Businesses across the world are seeing a massive influx of data at an enormous pace through multiple channels. With the advent of cloud computing, many companies are realizing the benefits of getting their data into the cloud to gain meaningful insights and save costs on data processing and storage. As businesses embark on their journey […]

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Streaming data from Amazon S3 to Amazon Kinesis Data Streams using AWS DMS

Stream processing is very useful in use cases where we need to detect a problem quickly and improve the outcome based on data, for example production line monitoring or supply chain optimizations. This blog post walks you through process of streaming existing data files and ongoing changes from Amazon Simple Storage Service (Amazon S3) to […]

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Stream CDC into an Amazon S3 data lake in Parquet format with AWS DMS

Most organizations generate data in real time and ever-increasing volumes. Data is captured from a variety of sources, such as transactional and reporting databases, application logs, customer-facing websites, and external feeds. Companies want to capture, transform, and analyze this time-sensitive data to improve customer experiences, increase efficiency, and drive innovations. With increased data volume and […]

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Stream, transform, and analyze XML data in real time with Amazon Kinesis, AWS Lambda, and Amazon Redshift

When we look at enterprise data warehousing systems, we receive data in various formats, such as XML, JSON, or CSV. Most third-party system integrations happen through SOAP or REST web services, where the input and output data format is either XML or JSON. When applications deal with CSV or JSON, it becomes fairly simple to […]

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