Category: Kinesis Data Streams

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: HAQM Kinesis Data Firehose has been renamed to HAQM 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 HAQM Simple Storage Service (HAQM S3), which commonly hosts the storage layer of the data lake. This post demonstrates how to apply CDC changes from HAQM Relational Database Service (HAQM RDS) or other relational databases to an S3 data lake, with flexibility to denormalize, transform, and enrich the data in near-real time.

April 2025: This post was reviewed and updated for accuracy. HAQM Kinesis Data Streams is a serverless data streaming service that makes it easy to capture, process, and store streaming data at any scale. As customers collect and stream more types of data, they have asked for simpler, elastic data streams that can handle variable and […]

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. Flink shines in its ability to handle processing of data streams in real-time and low-latency stateful […]

HAQM Redshift is a fully managed, scalable cloud data warehouse that accelerates your time to insights with fast, easy, and secure analytics at scale. Tens of thousands of customers rely on HAQM Redshift to analyze exabytes of data and run complex analytical queries, making it the widely used cloud data warehouse. You can run and […]

February 9, 2024: HAQM Kinesis Data Firehose has been renamed to HAQM Data Firehose. Read the AWS What’s New post to learn more. August 30, 2023: HAQM Kinesis Data Analytics has been renamed to HAQM Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. In this post, we […]

Customers have been using data warehousing solutions to perform their traditional analytics tasks. Recently, data lakes have gained lot of traction to become the foundation for analytical solutions, because they come with benefits such as scalability, fault tolerance, and support for structured, semi-structured, and unstructured datasets. Data lakes are not transactional by default; however, there […]

August 30, 2023: HAQM Kinesis Data Analytics has been renamed to HAQM Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. Real-time anomaly detection describes a use case to detect and flag unexpected behavior in streaming data as it occurs. Online machine learning (ML) algorithms are popular for […]