Category: Intermediate (200)

We are excited to announce the general availability of Amazon OpenSearch Service zero-ETL integration with Amazon Simple Storage Service (Amazon S3) for domains running 2.13 and above. The integration is new way for customers to query operational logs in Amazon S3 and Amazon S3-based data lakes without needing to switch between tools to analyze operational data. By querying across OpenSearch Service and S3 datasets, you can evaluate multiple data sources to perform forensic analysis of operational and security events. The new integration with OpenSearch Service supports AWS’s zero-ETL vision to reduce the operational complexity of duplicating data or managing multiple analytics tools by enabling you to directly query your operational data, reducing costs and time to action.

Amazon Kinesis Data Streams is used by many customers to capture, process, and store data streams at any scale. This level of unparalleled scale is enabled by dividing each data stream into multiple shards. Each shard in a stream has a 1 Mbps or 1,000 records per second write throughput limit. Whether your data streaming […]

This blog post is co-written with Sid Wray and Jake Koskela from Salesforce, and Adiascar Cisneros from Tableau.  Amazon Redshift is a fast, scalable cloud data warehouse built to serve workloads at any scale. With Amazon Redshift as your data warehouse, you can run complex queries using sophisticated query optimization to quickly deliver results to […]

AWS recently announced that Apache Flink is generally available for Amazon EMR on Amazon Elastic Kubernetes Service (EKS). Apache Flink is a scalable, reliable, and efficient data processing framework that handles real-time streaming and batch workloads (but is most commonly used for real-time streaming). Amazon EMR on EKS is a deployment option for Amazon EMR […]

Welcome back to our exciting exploration of architectural patterns for real-time analytics with Amazon Kinesis Data Streams! In this fast-paced world, Kinesis Data Streams stands out as a versatile and robust solution to tackle a wide range of use cases with real-time data, from dashboarding to powering artificial intelligence (AI) applications. In this series, we […]

In this post, we show how to create an AWS Glue job that measures and monitors the data quality of a data pipeline using dynamic rules. We also show how to take action based on the data quality results.

In this post, we explore how to use Zingg’s entity resolution capabilities within an AWS Glue notebook, which you can later run as an extract, transform, and load (ETL) job. By integrating Zingg in your notebooks or ETL jobs, you can effectively address data governance challenges and provide consistent and accurate data across your organization.

In today’s data-driven landscape, the efficiency and accessibility of querying tools play a crucial role in driving businesses forward. Amazon Redshift recently announced integration with Visual Studio Code (), an action that transforms the way data practitioners engage with Amazon Redshift and reshapes your interactions and practices in data management. This innovation not only unlocks […]

In today’s data-driven landscape, managing and analyzing vast amounts of data, especially logs, is crucial for organizations to derive insights and make informed decisions. However, handling this data efficiently presents a significant challenge, prompting organizations to seek scalable solutions without the complexity of infrastructure management. Amazon OpenSearch Serverless lets you run OpenSearch in the AWS […]

In this post, we discuss how to implement bucketing on AWS data lakes, including using Athena CTAS statement and AWS Glue for Apache Spark. We also cover bucketing for Apache Iceberg tables.