Category: Amazon Kinesis

Apache Spark is a powerful big data engine used for large-scale data analytics. Its in-memory computing makes it great for iterative algorithms and interactive queries. You can use Apache Spark to process streaming data from a variety of streaming sources, including Amazon Kinesis Data Streams for use cases like clickstream analysis, fraud detection, and more. Kinesis Data Streams is a serverless streaming data service that makes it straightforward to capture, process, and store data streams at any scale.

With the new open source Amazon Kinesis Data Streams Connector for Spark Structured Streaming, you can use the newer Spark Data Sources API. It also supports enhanced fan-out for dedicated read throughput and faster stream processing. In this post, we deep dive into the internal details of the connector and show you how to use it to consume and produce records from and to Kinesis Data Streams using Amazon EMR.

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. […]

Krones provides breweries, beverage bottlers, and food producers all over the world with individual machines and complete production lines. This post shows how Krones built a streaming solution to monitor their lines, based on Amazon Kinesis and Amazon Managed Service for Apache Flink. These fully managed services reduce the complexity of building streaming applications with Apache Flink. Managed Service for Apache Flink manages the underlying Apache Flink components that provide durable application state, metrics, logs, and more, and Kinesis enables you to cost-effectively process streaming data at any scale.

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, […]

The volume of data generated globally continues to surge, from gaming, retail, and finance, to manufacturing, healthcare, and travel. Organizations are looking for more ways to quickly use the constant inflow of data to innovate for their businesses and customers. They have to reliably capture, process, analyze, and load the data into a myriad of […]

A multi-account architecture on AWS is essential for enhancing security, compliance, and resource management by isolating workloads, enabling granular cost allocation, and facilitating collaboration across distinct environments. It also mitigates risks, improves scalability, and allows for advanced networking configurations. In a streaming architecture, you may have event producers, stream storage, and event consumers in a […]

Many organizations around the world rely on the use of physical assets, such as vehicles, to deliver a service to their end-customers. By tracking these assets in real time and storing the results, asset owners can derive valuable insights on how their assets are being used to continuously deliver business improvements and plan for future […]

Business leaders and data analysts use near-real-time transaction data to understand buyer behavior to help evolve products. The primary challenge businesses face with near-real-time analytics is getting the data prepared for analytics in a timely manner, which can often take days. Companies commonly maintain entire teams to facilitate the flow of data from ingestion to […]

We’re living in the age of real-time data and insights, driven by low-latency data streaming applications. Today, everyone expects a personalized experience in any application, and organizations are constantly innovating to increase their speed of business operation and decision making. The volume of time-sensitive data produced is increasing rapidly, with different formats of data being […]