How to Transform Your Data for Analytics

Explore the various data modeling and data transformation options to see which approaches work best for your modern cloud data warehouse, such as Snowflake.

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About the How to Transform Your Data for Analytics White Paper

The processes and techniques for transforming data for analytics have offered different options and evolved with the advent of new technologies such as MPP data warehouses and Hadoop. Once again, technology is reshaping how we think about data transformation in our current cloud data warehouse and data lakehouse era.


The Evolution of Data Modeling

Data modeling really took off with the introduction of relational databases, offering a much more flexible way to organize your data.

Cloud Data Warehouses and ELT

The advent and popularity of cloud data warehouses have altered the way we think about data transformation and modeling.

DataOps Process: Data Platform Capabilities

Types of Data Transformation

Different ways you would transform the data for analytics.

Versatile Data Transformation Tools

Versatile tools for effective data transformation and modeling processes.


A final step in data transformation is to do one last round of data organization to make it completely analytics-ready. As the first step in this process, make sure you have properly “flattened” the data into a single table/view with only the necessary fields for the analysis. This and materialization will help ensure optimized performance when the model is queried.


Datameer is a powerful SaaS data transformation platform that runs in Snowflake — your modern, scalable cloud data warehouse — that combines to provide a highly scalable and flexible environment to transform your data into meaningful analytics.

Datameer provides a number of key benefits for your modern data stack and cloud analytics.

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