Category: Kinesis Data Streams

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, […]

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 […]

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 […]

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. Kinesis Agent is a standalone Java software application that offers a straightforward way to collect and send data to Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose. The agent continuously monitors […]

Data is a key strategic asset for every organization, and every company is a data business at its core. However, in many organizations, data is typically spread across a number of different systems such as software as a service (SaaS) applications, operational databases, and data warehouses. Such data silos make it difficult to get unified […]

In this post, we show you some different options for handling large records within Kinesis Data Streams and the benefits and disadvantages of each approach. We provide some sample code for each option to help you get started with any of these approaches with your own workloads.

Organizations are grappling with the ever-expanding spectrum of data formats in today’s data-driven landscape. From Avro’s binary serialization to the efficient and compact structure of Protobuf, the landscape of data formats has expanded far beyond the traditional realms of CSV and JSON. As organizations strive to derive insights from these diverse data streams, the challenge […]

This is a guest post by Khandu Shinde, Staff Software Engineer and Edward Paget, Senior Software Engineering at Chime Financial. Chime is a financial technology company founded on the premise that basic banking services should be helpful, easy, and free. Chime partners with national banks to design member first financial products. This creates a more […]

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. Building a streaming data solution requires thorough testing at the scale it will operate in a production environment. Streaming applications operating at scale often handle large volumes of up to GBs per […]

We recently announced support for streaming extract, transform, and load (ETL) jobs in AWS Glue version 4.0, a new version of AWS Glue that accelerates data integration workloads in AWS. AWS Glue streaming ETL jobs continuously consume data from streaming sources, clean and transform the data in-flight, and make it available for analysis in seconds. AWS also offers a broad selection of services to support your needs. A database replication service such as AWS Database Migration Service (AWS DMS) can replicate the data from your source systems to Amazon Simple Storage Service (Amazon S3), which commonly hosts the storage layer of the data lake. This post demonstrates how to apply CDC changes from Amazon Relational Database Service (Amazon RDS) or other relational databases to an S3 data lake, with flexibility to denormalize, transform, and enrich the data in near-real time.