Category: Customer Solutions

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.

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

Tipico is the number one name in sports betting in Germany. Every day, we connect millions of fans to the thrill of sport, combining technology, passion, and trust to deliver fast, secure, and exciting betting, both online and in more than a thousand retail shops across Germany. We also bring this experience to Austria, where we proudly operate a strong sports betting business. In this post, we show how Tipico built a unified data transformation platform using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) and AWS Batch.

Using Amazon SageMaker Unified Studio serverless notebooks, AI-assisted development, and unified governance, you can speed up your data and AI workflows across data team functions while maintaining security and compliance. In this post, we walk you through how these new capabilities in SageMaker Unified Studio can help you consolidate your fragmented data tools, reduce time to insight, and collaborate across your data teams.

Bazaarvoice is an Austin-based company powering a world-leading reviews and ratings platform. Our system processes billions of consumer interactions through ratings, reviews, images, and videos, helping brands and retailers build shopper confidence and drive sales by using authentic user-generated content (UGC) across the customer journey. In this post, we show you the steps we took to migrate our workloads from self-hosted Kafka to Amazon Managed Streaming for Apache Kafka (Amazon MSK). We walk you through our migration process and highlight the improvements we achieved after this transition.

In this post, we show how Slack built a monitoring framework for Apache Spark on Amazon EMR that captures over 40 metrics, processes them through Kafka and Apache Iceberg, and uses Amazon Bedrock to deliver AI-powered tuning recommendations—achieving 30–50% cost reductions and 40–60% faster job completion times.

In this post, we discuss how Taxbit partnered with Amazon Web Services (AWS) to streamline their crypto tax analytics solution using Amazon S3 Tables, achieving 82% cost savings and five times faster processing times.

Socure is one of the leading providers of digital identity verification and fraud solutions. Socure’s data science environment includes a streaming pipeline called Transaction ETL (TETL), built on OSS Apache Spark running on Amazon EKS. TETL ingests and processes data volumes ranging from small to large datasets while maintaining high-throughput performance. In this post, we show how Socure was able to achieve 50% cost reduction by migrating the TETL streaming pipeline from self-managed spark to Amazon EMR serverless.

In this post, we discuss how Bayer AG used the next generation of Amazon SageMaker to build a cloud-based Pharma R&D Data Science Ecosystem (DSE) that unified data ingestion, storage, analytics, and AI/ML workflows.

In this post, we show you how Medidata created a unified, scalable, real-time data platform that serves thousands of clinical trials worldwide with AWS services, Apache Iceberg, and a modern lakehouse architecture.