article thumbnail

How FanDuel adopted a modern Amazon Redshift architecture to serve critical business workloads

AWS Big Data

In this post, we share how FanDuel moved from a DC2 nodes architecture to a modern Amazon Redshift architecture, which includes Redshift provisioned clusters using RA3 instances , Amazon Redshift data sharing , and Amazon Redshift Serverless. Their individual, product-specific, and often on-premises data warehouses soon became obsolete.

article thumbnail

How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

It covers how to use a conceptual, logical architecture for some of the most popular gaming industry use cases like event analysis, in-game purchase recommendations, measuring player satisfaction, telemetry data analysis, and more. Data lakes are more focused around storing and maintaining all the data in an organization in one place.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Extreme data center pressure? Burst to the cloud with CDP!

Cloudera

Your sunk costs are minimal and if a workload or project you are supporting becomes irrelevant, you can quickly spin down your cloud data warehouses and not be “stuck” with unused infrastructure. Cloud deployments for suitable workloads gives you the agility to keep pace with rapidly changing business and data needs.

article thumbnail

Backcountry modernizes for the cloud era

CIO Business Intelligence

Despite nearly $1 billion in online revenue in 2020, the web-based outdoor recreational retailer was running its entire business on an outdated and unsupported e-commerce platform called ADT. It got the basic job done but the company’s executive leadership team (ELT) knew it was time for a transformation. But tech was in the total doldrums.”.

article thumbnail

Four Factors to Consider when Migrating to Microsoft Business Central Online

Jet Global

An evolving toolset, shifting data models, and the learning curves associated with change all create some kind of cost for customer organizations. In other cases, costs are more obvious and clearly measurable. At the same time, you may not want to lose the ability to report against historical data.

article thumbnail

Of Muffins and Machine Learning Models

Cloudera

blueberry spacing) is a measure of the model’s interpretability. This allows data scientists, engineers and data management teams to have the right level of access to effectively perform their role. Model reproducibility is the extent to which a model can be recreated. The complete list is shown below: Model Lineage .

article thumbnail

Azure Data Sources for Data Science and Machine Learning

Jen Stirrup

It has native integration with other data sources, such as SQL Data Warehouse, Azure Cosmos, database storage, and even Azure Blob Storage as well. When you’re using Big Data technologies, it’s often a concern about how well those are performing in terms of performance and robustness.