The report highlights platforms like Fivetran that are crucial for modernizing marketing data infrastructure in today’s fast-paced, budget-conscious market.
Organizations are generating more data than ever before, but accessing it and gaining insights from it can be challenging. Fivetran and Snowflake simplify and speed up the process.
In recent years, machine learning operations (MLOps) have become the standard practice for developing, deploying, and managing machine learning models. MLOps standardizes processes and workflows for faster, scalable, and risk-free model deployment, centralizing model management, automating CI/CD for deployment, providing continuous monitoring, and ensuring governance and release best practices.
Adobe launched Adobe Experience Platform Federated Audience Composition, now generally available on Snowflake, allowing organizations to unlock seamless interoperability for marketers by integrating Snowflake's AI Data Cloud with Adobe Real-Time Customer Data Platform (CDP) and Adobe Journey Optimizer.
Companies are under pressure to deliver faster innovation, enabled by cloud-based data analytics and AI. In order to deliver faster business value, data teams are looking to achieve speed and scale through data and AI pipeline performance and efficiency. A recent MIT Technology Review Insights report finds that 72% of technology leaders agree that data challenges are the most likely factor to jeopardize AI/ML goals.
Modern data analytics platforms like Databricks have become indispensable for many companies. However Databricks costs can become unpredictable. This article gives four steps to help companies like yours predict Databricks costs and save your company time and money.
Unravel Data is the DataOps observability platform for the modern data stack. Unravel provides AI-powered recommendations for performance, cost governance, and quality so data teams spend less time firefighting and more time innovating. Let’s see how it works.
In today’s fast-paced financial services landscape, customers have a shorter attention span than ever. To meet clients’ growing demands for real-time access to information and keep innovating in areas like fraud detection and personalized financial advice, Thrivent needed to overhaul its data infrastructure. With data scattered across siloed legacy systems, diverse tech stacks, and multiple cloud environments, the challenge was a bit daunting. But by adopting Confluent Cloud, Thrivent was able to unify its disparate data systems into a single source of truth.
Gen AI has the potential to bring immense value for marketing use cases, from content creation to hyper-personalization to product insights, and many more. But if you’re struggling to scale and operationalize gen AI, you’re not alone. That’s where most enterprises struggle. To date, many companies are still in the excitement and exploitation phase of gen AI. Few have a number of initial pilots deployed and even fewer have simultaneous pilots and are building differentiating use cases.
Every enterprise is modernizing their business systems and applications to respond to real-time data. Within the next few years, we predict that most of an enterprise's data products will be built using a streaming fabric – a rich tapestry of real-time data, abstracted from the infrastructure it runs on. This streaming fabric spans not just one Apache Kafka cluster, but dozens, hundreds, maybe even thousands of them.