Systems | Development | Analytics | API | Testing

Analytics

Harnessing the Data Cloud to Empower Our Own Marketing Team: Building a Digital Ads Ecosystem on Snowflake

You need metrics to do your job well as a marketer but getting clear, meaningful metrics is a huge challenge. While digital advertisers and paid media professionals are on the hook to build ample sales pipeline and maximize return on ad spend (ROAS), they’re also expected to deliver personalized advertising content while navigating evolving privacy requirements and adhering to consumer expectations—all while extracting insights from siloed ad platforms.

15 Examples of Data Pipelines Built with Amazon Redshift

At Integrate.io, we work with companies that build data pipelines. Some start cloud-native on platforms like Amazon Redshift, while others migrate from on-premise or hybrid solutions. What they all have in common is the one question they ask us at the very beginning: And so that’s why we decided to compile and publish a list of publicly available blog posts about how companies build their data pipelines.

Using ClearML and MONAI for Deep Learning in Healthcare

This tutorial shows how to use ClearML to manage MONAI experiments. Originating from a project co-founded by NVIDIA, MONAI stands for Medical Open Network for AI. It is a domain-specific open-source PyTorch-based framework for deep learning in healthcare imaging. This blog shares how to use the ClearML handlers in conjunction with the MONAI Toolkit. To view our code example, visit our GitHub page.

What is Confluent?

Confluent is pioneering a fundamentally new category of data infrastructure focused on data in motion. Confluent’s cloud-native offering is the foundational platform for data in motion – designed to be the intelligent connective tissue enabling real-time data, from multiple sources, to constantly stream across the organization. With Confluent, organizations can meet the new business imperative of delivering rich, digital front-end customer experiences and transitioning to sophisticated, real-time, software-driven backend operations.

Leveraging Amazon S3 Cloud Object Storage for Analytics

With its simplicity, flexibility, and cost-efficient characteristics, Amazon Simple Storage Service (Amazon S3) cloud object storage has become the preferred platform for collecting, analyzing, and retaining today’s growing mountain of diverse and disjointed enterprise data. And as Amazon Web Services (AWS) continues to grab market share in the hyperscale IaaS/PaaS/SaaS marketplace, organizations of every size are leveraging Amazon S3 to underpin a variety of use cases, such as.

The Top 6 Big Data Events for 2024

Big data is more crucial to businesses than ever, especially with the rise of artificial intelligence (AI) and machine learning technologies across multiple industries. The expanding internet of things (IoT) plus the multiple digital engagement points between businesses and customers create huge swathes of data and new challenges in how to manage this glut of information.

2024 Predications for SAP Finance Teams

Next year, finance teams utilizing SAP will face unparalleled pressure due to a confluence of economic challenges. The demand for actionable insights will grow amidst a landscape marred by economic recession and escalating inflation rates. These adversities heighten the necessity for finance professionals to adeptly translate raw data into strategic guidance.

Fostering Collaboration and Innovation through Databox Engineering Guilds

Helping companies leverage their data to make better decisions and improve their performance is something we strive for at Databox. Until recently, this type of analysis has only been reserved for larger enterprises with dedicated teams and budgets for complex tools. Fortunately, recent innovations are reshaping this landscape.