AWS Big Data Blog

Category: Advanced (300)

Working with percolators in Amazon OpenSearch Service

Amazon OpenSearch Service is a managed service that makes it easy to secure, deploy, and operate OpenSearch and legacy Elasticsearch clusters at scale in the AWS Cloud. Amazon OpenSearch Service provisions all the resources for your cluster, launches it, and automatically detects and replaces failed nodes, reducing the overhead of self-managed infrastructures. The service makes it […]

Build a transactional data lake using Apache Iceberg, AWS Glue, and cross-account data shares using AWS Lake Formation and Amazon Athena

Building a data lake on Amazon Simple Storage Service (Amazon S3) provides numerous benefits for an organization. It allows you to access diverse data sources, build business intelligence dashboards, build AI and machine learning (ML) models to provide customized customer experiences, and accelerate the curation of new datasets for consumption by adopting a modern data […]

Simplify and speed up Apache Spark applications on Amazon Redshift data with Amazon Redshift integration for Apache Spark

Customers use Amazon Redshift to run their business-critical analytics on petabytes of structured and semi-structured data. Apache Spark is a popular framework that you can use to build applications for use cases such as ETL (extract, transform, and load), interactive analytics, and machine learning (ML). Apache Spark enables you to build applications in a variety […]

Automate discovery of data relationships using ML and Amazon Neptune graph technology

Data mesh is a new approach to data management. Companies across industries are using a data mesh to decentralize data management to improve data agility and get value from data. However, when a data producer shares data products on a data mesh self-serve web portal, it’s neither intuitive nor easy for a data consumer to […]

Accelerate HiveQL with Oozie to Spark SQL migration on Amazon EMR

Many customers run big data workloads such as extract, transform, and load (ETL) on Apache Hive to create a data warehouse on Hadoop. Apache Hive has performed pretty well for a long time. But with advancements in infrastructure such as cloud computing and multicore machines with large RAM, Apache Spark started to gain visibility by […]

How CyberSolutions built a scalable data pipeline using Amazon EMR Serverless and the AWS Data Lab

This post is co-written by Constantin Scoarță and Horațiu Măiereanu from CyberSolutions Tech. CyberSolutions is one of the leading ecommerce enablers in Germany. We design, implement, maintain, and optimize award-winning ecommerce platforms end to end. Our solutions are based on best-in-class software like SAP Hybris and Adobe Experience Manager, and complemented by unique services that […]

Amazon EMR on EKS widens the performance gap: Run Apache Spark workloads 5.37 times faster and at 4.3 times lower cost

Amazon EMR on EKS provides a deployment option for Amazon EMR that allows organizations to run open-source big data frameworks on Amazon Elastic Kubernetes Service (Amazon EKS). With EMR on EKS, Spark applications run on the Amazon EMR runtime for Apache Spark. This performance-optimized runtime offered by Amazon EMR makes your Spark jobs run fast […]

Connect to Amazon MSK Serverless from your on-premises network

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed, highly available, and secure Apache Kafka service. Amazon MSK reduces the work needed to set up, scale, and manage Apache Kafka in production. With Amazon MSK, you can create a cluster in minutes and start sending data. With Amazon MSK Serverless, you can […]

How Morningstar used tag-based access controls in AWS Lake Formation to manage permissions for an Amazon Redshift data warehouse

This post was co-written by Ashish Prabhu, Stephen Johnston, and Colin Ingarfield at Morningstar and Don Drake, at AWS. With “Empowering Investor Success” as the core motto, Morningstar aims at providing our investors and advisors with the tools and information they need to make informed investment decisions. In this post, Morningstar’s Data Lake Team Leads […]

Patterns for updating Amazon OpenSearch Service index settings and mappings

Amazon OpenSearch Service is used for a broad set of use cases like real-time application monitoring, log analytics, and website search at scale. As your domain ages and you add additional consumers, you need to reevaluate and change the domain’s configuration to handle additional storage and compute needs. You want to minimize downtime and performance […]