Catalog of Data Modeling Tools

Deployment: Is the tool SaaS or open-source?

Classification: Is the tool exclusively used for transforming data (such as coalesce) or is the transformation part of a larger offering? For example, ETL tools transform data, but they also take care of the extract and loading steps.

Security: This criteria notes whether the solution is compliant with any specific regulatory law like GDPR, HIPAA, etc.

Language: What is the scripting language used for data transformations? Scala, Python, SQL? Is the solution no-code?

Community: Is there a community built around the solution? Communities tend to be especially important with open-source tools, as they provide a great amount of support.

Data sources supported: Where are the transformations operated with the solution? Does it support transformations in data warehouses? Databases?

Add data quality checks: test data quality with assertions checks for uniqueness or null values, or write a custom assertion in SQL to check any property of your data.

version control: You can easily track changes and restore version histories of datasets.

Real-time query validation: solution validates compiled queries against BigQuery in real-time, enabling users to identify issues before running queries.

Real-time data transformation: Run SQL search, aggregations and joins just as data is generated.