Rudderstack blog

As a data engineer, you're undoubtedly familiar with the complexities of managing ETL (Extract, Transform, Load) processes and batch operations, especially with the rapid expansion of cloud data warehouses.

As a data scientist or data analyst, you'll spend the majority of your time managing and manipulating data. This often involves employing Python for data engineering tasks.

A technical walkthrough of how we implemented the NextJS tag revalidation caching strategy using Sanity, webhooks, and the Next App Router. The first post from our development team that builds and manages the marketing site stack for rudderstack.com.

We rebuilt our JavaScript SDK from the ground up to be faster, smaller, and more flexible. Version three delivers better performance, gives you enhanced control, enables cookieless tracking, and features a new plugin architecture.

In this post we clarify the differences between zero copy and warehouse native customer data platform architectures and detail the advantages of the warehouse native approach.

In this comprehensive guide, we will explore the different data types supported by Snowflake and understand how they can be effectively used in your data analysis projects.

With our Kafka Source integration, you can easily get your customer data out of Kafka, solve data quality issues, and distribute it throughout your stack to drive better business outcomes.

A breakdown of event streaming approaches comparing and contrasting low-level Apache Kafka with RudderStack’s Event Stream. We detail when to use each tool and when to use both together.

The CDP is still rapidly evolving, but one thing is clear: The data warehouse will play a central role in its future. Here, we unpack the prevailing approaches and introduce its next evolution – The Warehouse Native Customer Data Platform.