Category: Intermediate (200)

We’re happy to announce a new feature in the Amazon Data Firehose integration with Amazon MSK. You can now specify the Firehose stream to either read from the earliest position on the Kafka topic or from a custom timestamp to begin reading from your MSK topic. In this post of this series, we focus on managed data delivery from Kafka to your data lake.

Amazon OpenSearch Service is a versatile search and analytics tool. In this post, we explore conversational search, its architecture, and various ways to implement it.

Amazon Redshift employs columnar storage for database tables, reducing overall disk I/O requirements. This storage method significantly improves analytic query performance by minimizing data read during queries. This post showcases the key improvements in Amazon Redshift concurrent data ingestion operations.

In this post, we walk you through a search application building process using Amazon OpenSearch Service. Whether you’re a developer new to search or looking to understand OpenSearch fundamentals, this hands-on post shows you how to build a search application from scratch—starting with the initial setup; diving into core components such as indexing, querying, result presentation; and culminating in the execution of your first search query.

In this post, we demonstrate how to configure Fluent Bit, a fast and flexible log processor and router supported by various operating systems, to securely send logs from any environment to OpenSearch Ingestion using IAM Roles Anywhere.

In this post, we demonstrate a use case where you might need to use an MSK cluster in one AWS account, but MSK Connect is located in a separate account. We demonstrate how to implement IAM authentication after establishing network connectivity. IAM provides enhanced security measures, making sure your systems are protected against unauthorized access.

This post explores how to effectively architect a solution that addresses this specific challenge: enabling comprehensive analytics capabilities for global teams while making sure that your data remains in the AWS Regions required by your compliance framework. We use a variety of AWS services, including Amazon Redshift, Amazon Simple Storage Service (Amazon S3), and Amazon QuickSight.

In this post, we demonstrate how you can bring transactional data from AWS OLTP data stores like Amazon Relational Database Service (Amazon RDS) and Amazon Aurora flowing into Redshift using zero-ETL integrations to SageMaker Lakehouse Federated Catalog (Bring your own Amazon Redshift into SageMaker Lakehouse). With this integration, you can now seamlessly onboard the changed data from OLTP systems to a unified lakehouse and expose the same to analytical applications for consumptions using Apache Iceberg APIs from new SageMaker Unified Studio.

This post provides guidance on how you can use event driven architecture to enhance the resiliency of data solutions built on the next generation of Amazon SageMaker, a unified platform for data, analytics, and AI. SageMaker is a managed service with high availability and durability.

In this blog post we shall cover how understanding real-time payout performance, identifying customer behavior patterns across regions, and optimizing internal operations required more than traditional business intelligence and analytics tools. And how since implementing Amazon Redshift and Zero-ETL, MassPay has seen 90% reduction in data availability latency, payments data available for analytics 1.5x faster, leading to 45% reduction in time-to-insight and 37% fewer support tickets related to transaction visibility and payment inquiries.