Analytics

Snowpark is the set of libraries and runtimes that enables data engineers, data scientists and developers to build data engineering pipelines, ML workflows, and data applications in Python, Java, and Scala. Functions or procedures written by users in these languages are executed inside of Snowpark’s secure sandbox environment, which runs on the warehouse.

Is Windows your favorite development environment? Do you want to run Apache Kafka® on Windows? Thanks to the Windows Subsystem for Linux 2 (WSL 2), now you can, and with fewer tears than in the past. Windows still isn’t the recommended platform for running Kafka with production workloads, but for trying out Kafka, it works just fine. Let’s take a look at how it’s done.

Product analytics traditionally hinged on examining user interactions to extract actionable insights. The integration of machine learning (ML) has elevated this process, enriching our understanding and our ability to predict future trends. Let's unfold how ML integrates into product analytics and the transformative advantages it introduces. ‍

Learn the crucial role of data governance and security in database replication.

In a survey by the Harvard Business Review, 87% of respondents stated their organizations would be more successful if frontline workers were empowered to make important decisions in the moment. And 86% of respondents stated that they needed better technology to enable those in-the-moment decisions. Those coveted insights live at the end of a process lovingly known as the data pipeline.

Various factors can impede an organization's ability to leverage Confluent Cloud, ranging from data locality considerations to stringent internal prerequisites. For instance, specific mandates might dictate that data be confined within a customer's Virtual Private Cloud (VPC), or necessitate operation within an air-gapped VPC. However, a silver lining exists even in such circumstances, as viable alternatives remain available to address these specific scenarios.