Category: Amazon Redshift

2023 was a rollercoaster year in tech, and we at the AWS Architecture Blog feel so fortunate to have shared in the excitement. As we move into 2024 and all of the new technologies we could see, we want to take a moment to highlight the brightest stars from 2023. As always, thanks to our […]

In the first part of this blog series, Optimize your modern data architecture for sustainability: Part 1 – data ingestion and data lake, we focused on the 1) data ingestion, and 2) data lake pillars of the modern data architecture. In this blog post, we will provide guidance and best practices to optimize the components […]

The modern data architecture on AWS focuses on integrating a data lake and purpose-built data services to efficiently build analytics workloads, which provide speed and agility at scale. Using the right service for the right purpose not only provides performance gains, but facilitates the right utilization of resources. Review Modern Data Analytics Reference Architecture on […]

Data is at the center of stateful applications. Data consistency models will vary when choosing in-Region vs. multi-Region. In this post, part 2 of 3, we continue to filter through AWS services to focus on data-centric services with native features to help get your data where it needs to be in support of a multi-Region […]

When selecting managed database services in AWS, it’s important to understand how data transfer charges are calculated – whether it’s relational, key-value, document, in-memory, graph, time series, wide column, or ledger. This blog will outline the data transfer charges for several AWS managed database offerings to help you choose the most cost-effective setup for your […]

The AWS Cloud is an ever-growing network of Regions and points of presence (PoP), with a global network infrastructure that connects them together. With such a vast selection of Regions, costs, and services available, it can be challenging for startups to select the optimal Region for a workload. This decision must be made carefully, as […]

Customers are building databases, data warehouses, and data lake solutions in isolation from each other, each having its own separate data ingestion, storage, management, and governance layers. Often these disjointed efforts to build separate data stores end up creating data silos, data integration complexities, excessive data movement, and data consistency issues. These issues are preventing […]

Organizational analytics systems have shifted from running in the background of IT systems to being critical to an organization’s health. Analytics systems help businesses make better decisions, but they tend to be complex and are often not agile enough to scale quickly. To help with this, customers upgrade their traditional on-premises online analytic processing (OLAP) […]

As the volume of customers’ data grows, companies are realizing the benefits that data has for their business. Amazon Web Services (AWS) offers many database and analytics services, which give companies the ability to build complex data management workloads. At the same time, these services can reduce the operational overhead compared to traditional operations. Using […]

One of the keys to identifying timely and impactful actions is having enough raw material to work with. However, this up-to-date information typically lives in the databases that sit behind several different applications. One of the first steps to finding data-driven insights is gathering that information into a single store that an analyst can use […]