Migrating a data warehouse from a legacy environment requires a massive upfront investment in resources and time. There is a lot to consider before and during migration. You may need to replan your data model, use a separate platform for tasks scheduling, and handle changes in the application’s database driver. Therefore, organizations must take a strategic approach to streamline the process. This article presents a step-by-step approach for migrating a data warehouse to the cloud.

Implementing a modern, cloud-based analytics stack doesn’t have to be hard — you can do it in three steps, actually. Implementing a modern data stack (MDS) — data integration tool, cloud data warehouse and business intelligence platform — is the best way to establish a successful analytics program as data sources and data volumes multiply.

Many in the community have been asking us to develop a new Kafka to S3 connector for some time. So we’re pleased to announce it's now available. It’s been designed to deliver a number of benefits over existing S3 connectors. Like our other Stream Reactors, the connector extends the standard connect config adding a parameter for a SQL command (Lenses Kafka Connect Query Language or “KCQL”). This defines how to map data from the source (in this case Kafka) to the target (S3).

In June, Snowflake announced the public preview of the external functions feature with support for calling external APIs via AWS API Gateway. With external functions, you can easily extend your data pipelines by calling out to external services, third-party libraries, or even your own custom logic, enabling exciting new use cases. For example, you can use external functions for external tokenization, geocoding, scoring data using pre-trained machine learning models, and much more.