Category: Analytics

Verisk, a catastrophe modeling SaaS provider serving insurance and reinsurance companies worldwide, cut processing time from hours to minutes-level aggregations while reducing storage costs by implementing a lakehouse architecture with Amazon Redshift and Apache Iceberg. If you’re managing billions of catastrophe modeling records across hurricanes, earthquakes, and wildfires, this approach eliminates the traditional compute-versus-cost trade-off by separating storage from processing power. In this post, we examine Verisk’s lakehouse implementation, focusing on four architectural decisions that delivered measurable improvements.

While general sizing guidelines for OpenSearch Service domains are covered in detail in OpenSearch Service documentation, in this post we specifically focus on T-shirt-sizing OpenSearch Service domains for e-commerce search workloads. T-shirt sizing simplifies complex capacity planning by categorizing workloads into sizes like XS, S, M, L, XL based on key workload parameters such as data volume and query concurrency.

Stream data processing allows you to act on data in real time. Real-time data analytics can help you have on-time and optimized responses while improving overall customer experience. Apache Flink is a distributed computation framework that allows for stateful real-time data processing. It provides a single set of APIs for building batch and streaming jobs, making […]

In this post, we demonstrate how you can create a custom index analyzer in OpenSearch to implement autocomplete functionality efficiently by using the Edge n-gram tokenizer to match prefix queries without using wildcards.

Amazon Athena is a serverless interactive query service that makes it easy to analyze data using SQL. With Athena, there’s no infrastructure to manage, you simply submit queries and get results. Capacity Reservations is a feature of Athena that addresses the need to run critical workloads by providing dedicated serverless capacity for workloads you specify. In this post, we highlight three new capabilities that make Capacity Reservations more flexible and easier to manage: reduced minimums for fine-grained capacity adjustments, an autoscaling solution for dynamic workloads, and capacity cost and performance controls.

In this post, we show how Zalando migrated their fast-serving layer data warehouse to Amazon Redshift to achieve better price-performance and scalability.

In this post, we demonstrate how to access an Amazon SageMaker Unified Studio IDC-based domain with a new IAM-based domain using role reuse and attribute-based access control.

This post explores how to build and manage a comprehensive extract, transform, and load (ETL) pipeline using SageMaker Unified Studio workflows through a code-based approach. We demonstrate how to use a single, integrated interface to handle all aspects of data processing, from preparation to orchestration, by using AWS services including Amazon EMR, AWS Glue, Amazon Redshift, and Amazon MWAA. This solution streamlines the data pipeline through a single UI.

In this post, we present an observability agent using OpenSearch Service and Amazon Bedrock AgentCore that can help surface root cause and get insights faster, handle multiple query-correlation cycles, and ultimately reduce MTTR even further.

This post provides an in-depth look at setting up Amazon CloudWatch alarms for OpenSearch Ingestion pipelines. It goes beyond our recommended alarms to help identify bottlenecks in the pipeline, whether that’s in the sink, the OpenSearch clusters data is being sent to, the processors, or the pipeline not pulling or accepting enough from the source. This post will help you proactively monitor and troubleshoot your OpenSearch Ingestion pipelines.