The Low-Code Platform for Data Lakehouses

4 considerations for building a next-gen data architecture 

As organizations seek to design, build, and implement data and analytics capabilities, they are pressed to reinvent and reorient their data architectures—as well as justify these activities with ROI measures. From the cloud-native data warehouse and data lakehouse to data mesh and data fabric, a range of architecture patterns and enabling technologies have come to the forefront of modernization discussions.

However, there are significant challenges in preparing data for analytics on cloud data platforms. The cloud is missing data tools that provide functionality similar to the on-premises ETL products that make data users productive.

Download this Big Data Quarterly Best Practices Report produced by Unisphere Research to learn:

• The top considerations for building a next-gen data architecture  
• The challenges of using legacy ETL methods and code to build data pipelines in the cloud
• How to boost productivity by enabling your data and business teams to self-serve and visually build data products, natively on lakehouses and other cloud data platforms