3 keys to impactful data products with self-serve data transformations

The modern data stack was already bursting at the seams long before generative AI became the talk of the town due to steep increase in the number of data sources, data consumers, and use cases. As the world fixates on AI-driven data outcomes, there is an immense urgency to modernize the underlying data infrastructure to meet the data needs of the business in a manner that is trustworthy, reliable, timely and, of course, accurate. In addition, there is already a rallying cry for simplification and complexity reduction. To address these needs, it is imperative to reduce data stack complexity, which further helps increase reliability of the infrastructure components.

This whitepaper explores the three critical business needs and provides a roadmap to handle current and future data engineering use cases with high-performing data products that democratize transformations and boost productivity.

You'll learn:

• Top considerations for taking a holistic approach to modern data transformation
• How to democratize data transformations with a low-code approach, empowering data users to self-serve and build data pipelines with ease
• How to optimize data infrastructure for reliability, cost-effectiveness, and compliance, ensuring high-quality data outcomes and enabling scalability