Category: Intermediate (200)

AWS helps SaaS vendors by providing the building blocks needed to implement a streaming application with Amazon Kinesis Data Streams and Amazon Managed Streaming for Apache Kafka (Amazon MSK), and real-time processing applications with Amazon Managed Service for Apache Flink. In this post, we look at implementation patterns a SaaS vendor can adopt when using a streaming platform as a means of integration between internal components, where streaming data is not directly exposed to third parties. In particular, we focus on Amazon MSK.

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing ETL (extract, transform, and load), business intelligence (BI), and reporting tools. Tens of thousands of customers use Amazon Redshift to process exabytes of data per […]

In today’s data-driven world, organizations are continually confronted with the task of managing extensive volumes of data securely and efficiently. Whether it’s customer information, sales records, or sensor data from Internet of Things (IoT) devices, the importance of handling and storing data at scale with ease of use is paramount. A common use case that […]

As part of an indexing operation, the ingested documents are stored as immutable segments. Each segment is a collection of various data structures, such as inverted index, block K dimensional tree (BKD), term dictionary, or stored fields, and these data structures are responsible for retrieving the document faster during the search operation. Out of these data structures, stored fields, which are largest fields in the segment, are compressed when stored on the disk and based on the compression strategy used, the compression speed and the index storage size will vary. In this post, we discuss the performance of the Zstandard algorithm, which was introduced in OpenSearch v2.9, amongst other available compression algorithms in OpenSearch.

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.