From a business perspective, data mesh architecture has gained popularity in recent years as a way to enable more autonomous and agile data processing across domains or teams. 🏵️🏵️
By decentralizing data ownership and processing, data mesh architecture can provide better data quality, easier data discovery, and faster data delivery.

However, implementing a streaming SQL engine in a data mesh architecture may not be a silver bullet solution for all use cases.
For example, some real-time use cases may require more complex processing logic than what can be expressed using SQL.
In these cases, a more custom solution may be required.

Moreover, the choice of data source and data sink can also affect the performance and scalability of the streaming SQL pipeline.
For example, using Elasticsearch as a data sink may work well for some use cases but may not provide sufficient scalability for high-volume data ingestion.
In the end, streaming SQL in data mesh architecture can be a powerful solution for real-time data processing and analysis.
However, it is important to carefully consider the technical and business requirements of each use case before choosing a specific technology or architecture.
⏩⏭️