Category: Amazon Redshift

We’re excited to announce the launch of the Apache Iceberg on AWS technical guide. Whether you are new to Apache Iceberg on AWS or already running production workloads on AWS, this comprehensive technical guide offers detailed guidance on foundational concepts to advanced optimizations to build your transactional data lake with Apache Iceberg on AWS.

In this post, we discuss how Amazon Redshift spatial index functions such as Hexagonal hierarchical geospatial indexing system (or H3) can be used to represent spatial data using H3 indexing for fast spatial lookups at scale. Navigating the vast landscape of data-driven insights has always been an exciting endeavor. As technology continues to evolve, one specific facet of this journey is reaching unprecedented proportions: geospatial data.

As data analytics use cases grow, factors of scalability and concurrency become crucial for businesses. Your analytic solution architecture should be able to handle large data volumes at high concurrency and without compromising speed, thereby delivering a scalable high-performance analytics environment. Amazon Redshift Serverless provides a fully managed, petabyte-scale, auto scaling cloud data warehouse to […]

In today’s data-driven landscape, the efficiency and accessibility of querying tools play a crucial role in driving businesses forward. Amazon Redshift recently announced integration with Visual Studio Code (), an action that transforms the way data practitioners engage with Amazon Redshift and reshapes your interactions and practices in data management. This innovation not only unlocks […]

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is a managed orchestration service for Apache Airflow that you can use to set up and operate data pipelines in the cloud at scale. Apache Airflow is an open source tool used to programmatically author, schedule, and monitor sequences of processes and tasks, referred to as workflows. […]

Analytics as a service (AaaS) is a business model that uses the cloud to deliver analytic capabilities on a subscription basis. This model provides organizations with a cost-effective, scalable, and flexible solution for building analytics. The AaaS model accelerates data-driven decision-making through advanced analytics, enabling organizations to swiftly adapt to changing market trends and make […]

Our zero-ETL integration with Amazon Redshift facilitates point-to-point data movement to get it ready for analytics, artificial intelligence (AI) and machine learning (ML) using Amazon Redshift on petabytes of data. In this post, we provide step-by-step guidance on how to get started with near real time operational analytics using the Amazon Aurora PostgreSQL zero-ETL integration with Amazon Redshift.

Aura from Unity (formerly known as ironSource) is the market standard for creating rich device experiences that engage and retain customers. In this post, we describe Aura’s successful and swift adoption of Redshift Serverless, which allowed them to optimize their overall bidding advertisement campaigns’ time to market from 24 hours to 2 hours. We explore why Aura chose this solution and what technological challenges it helped solve.

Healthcare providers have an opportunity to improve the patient experience by collecting and analyzing broader and more diverse datasets. This includes patient medical history, allergies, immunizations, family disease history, and individuals’ lifestyle data such as workout habits. Having access to those datasets and forming a 360-degree view of patients allows healthcare providers such as claim […]

Amazon Redshift is a fast, scalable, and fully managed cloud data warehouse that allows you to process and run your complex SQL analytics workloads on structured and semi-structured data. In this post, we discuss how to successfully conduct a proof of concept in Amazon Redshift by going through the main stages of the process, available tools that accelerate implementation, and common use cases.