Category: Customer Solutions

In this post, we show you how EUROGATE uses AWS services, including Amazon DataZone, to make data discoverable by data consumers across different business units so that they can innovate faster. Two use cases illustrate how this can be applied for business intelligence (BI) and data science applications, using AWS services such as Amazon Redshift and Amazon SageMaker.

Juicebox is an AI-powered talent sourcing search engine, using advanced natural language models to help recruiters identify the best candidates from a vast dataset of over 800 million profiles. At the core of this functionality is Amazon OpenSearch Service, which provides the backbone for Juicebox’s powerful search infrastructure, enabling a seamless combination of traditional full-text search methods with modern, cutting-edge semantic search capabilities. In this post, we share how Juicebox uses OpenSearch Service for improved search.

In this post, we explore how Fitch Group, one of the top credit rating companies, used Amazon MSK and Amazon MSK Replicator to achieve multi-Region resiliency for their mission-critical Kafka infrastructure.

Jumia is a technology company born in 2012, present in 14 African countries, with its main headquarters in Lagos, Nigeria. In this post, we share part of the journey that Jumia took with AWS Professional Services to modernize its data platform that ran under a Hadoop distribution to AWS serverless based solutions.

HEMA is a household Dutch retail brand name since 1926, providing daily convenience products using unique design. This post describes how HEMA used Amazon DataZone to build their data mesh and enable streamlined data access across multiple business areas. It explains HEMA’s unique journey of deploying Amazon DataZone, the key challenges they overcame, and the transformative benefits they have realized since deployment in May 2024. From establishing an enterprise-wide data inventory and improving data discoverability, to enabling decentralized data sharing and governance, Amazon DataZone has been a game changer for HEMA.

DeNA Co., Ltd. (DeNA) engages in a variety of businesses, from games and live communities to sports & the community and healthcare & medical, under our mission to delight people beyond their wildest dreams. This post introduces a case study where DeNA combined Amazon Redshift Serverless and dbt (dbt Core) to accelerate data quality tests in their business.

In this post, we continue from Accelerate Amazon Redshift secure data use with Satori – Part 1, and explain how Satori, an Amazon Redshift Ready partner, simplifies both the user experience of gaining access to data and the admin practice of granting and revoking access to data in Amazon Redshift. Satori enables both just-in-time and self-service access to data.

REA Group, a digital real estate business, uses Amazon Managed Streaming for Apache Kafka (Amazon MSK) and a data streaming platform called Hydro to efficiently share and access large amounts of data across multiple domains and services. This approach allows REA Group to maintain optimal performance and cost-efficiency while scaling to meet growing user demands. In this post, they share their approach to MSK cluster capacity planning.

ANZ Institutional Division has transformed its data management approach by implementing a federated data platform based on data mesh principles. This shift aims to unlock untapped data potential, improve operational efficiency, and increase agility. The new strategy empowers domain teams to create and manage their own data products, treating data as a valuable asset rather than a byproduct. This post explores how the shift to a data product mindset is being implemented, the challenges faced, and the early wins that are shaping the future of data management in the Institutional Division.

FINRA performs big data processing with large volumes of data and workloads with varying instance sizes and types on Amazon EMR. Amazon EMR is a cloud-based big data environment designed to process large amounts of data using open source tools such as Hadoop, Spark, HBase, Flink, Hudi, and Presto. In this post, we talk about our challenges and show how we built an observability framework to provide operational metrics insights for big data processing workloads on Amazon EMR on Amazon Elastic Compute Cloud (Amazon EC2) clusters.