Analytics

Generative AI has recently emerged as a groundbreaking technology and businesses have been quick to respond. Recognizing its potential to drive innovation, deliver significant ROI and add economic value, business adoption is rapid and widespread. They are not wrong. A research report by Quantum Black, AI by McKinsey, titled "The Economic Potential of Generative AI”, estimates that generative AI could unlock up to $4.4 trillion in annual global productivity.

Unlocking the full potential of your SAP solution requires the complete trust of your data. Without trust, users will second guess any insights, stalling business progress. Research has pinpointed three key pain points that companies encounter with their SAP data: a prevailing sense of data distrust, a lack of maintenance and data cleansing, and a shortage of skilled users. These pain points not only impede progress but also pose significant roadblocks to migrating to S/4HANA, the future of SAP.

Multi-tenancy is a concept that refers to the ability of a software application or system to serve multiple tenants, or customers, on a shared infrastructure. In simpler terms, it is the capability of a single instance of a software application to accommodate multiple users or organizations, each with their own datasets and customization options.

Salesforce is among the leading CRM software platforms for collecting and leveraging user data to make smart sales, marketing, and customer support decisions. However, other software in your tech stack can benefit from such data. With the right Salesforce middleware, you can exchange data easily with your other critical tools.

Discover where your company stands with its SEO performance in comparison to the most relevant industry benchmarks.

Organizations increasingly rely on streaming data sources not only to bring data into the enterprise but also to perform streaming analytics that accelerate the process of being able to get value from the data early in its lifecycle. As lakehouse architectures (including offerings from Cloudera and IBM) become the norm for data processing and building AI applications, a robust streaming service becomes a critical building block for modern data architectures.

In the first three parts of our Inside Flink blog series, we discussed the benefits of stream processing, explored why developers are choosing Apache Flink® for a variety of stream processing use cases, and took a deep dive into Flink's SQL API. In this post, we'll focus on how we’ve re-architected Flink as a cloud-native service on Confluent Cloud. However, before we get into the specifics, there is exciting news to share.