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I was going through the article here : https://aws.amazon.com/blogs/opensource/announcing-partiql-one-query-language-for-all-your-data/
It looks like "PartiQL" is similar like U-SQL provided by Microsoft. Here it is : https://docs.microsoft.com/en-us/azure/data-lake-analytics/data-lake-analytics-u-sql-get-started
Is there anyone using "PartiQL" today? What are the differences between these two. Or, is it like, if its AWS then "PartiQL" and if Azure then its U-SQL?
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You should use PartiQL when the SQL language is cumbersome to support nested or semi-structured data. You should also use PartiQL when you think you may change how your data is formatted or where it resides. Finally, you should use PartiQL when you have schema-less data or your schema evolves over time.
U-SQL is a language that combines declarative SQL with imperative C# to let you process data at any scale. Through the scalable, distributed-query capability of U-SQL, you can efficiently analyze data across relational stores such as Azure SQL Database.
What is PartiQL? PartiQL provides SQL-compatible query access across multiple data stores containing structured data, semistructured data, and nested data. It is widely used within Amazon and is now available as part of many AWS services, including DynamoDB.
U-SQL is a Microsoft query language that combines a declarative SQL-like syntax with C# programming, enabling it to be used to process both structured and unstructured data in big data environments.
Having had a quick read through the PartiQL article, it does indeed seem similar in principle to U-SQL on the face of it. However you should know that U-SQL has not meaningfully moved on since June 2018. Read into that what you will.
Therefore, in my humble opinion and reading between the lines a little, I personally would not be starting new projects in 2019 with U-SQL. A considerably better option would be Databricks, an Apache Spark-based platform available on both AWS and Azure, offering you SQL, Scala, Java, Python and R as language choices plus that multi-product connectivity.
So a more meaningful comparison might be PartiQL versus Databricks. One potential upside to Databricks is it is present in AWS and Azure where PartiQL is not (implying lock-in).
Generate for yourself the illusion of cloud-agnostic designs.
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