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09 November 2021: Amazon Web Services (AWS) announced the availability of Babelfish for Amazon Aurora. Babelfish enables its hyperscale Aurora relational database service to understand Microsoft SQL Server and PostgreSQL commands. This allows customers to run applications written for Microsoft SQL Server directly on Amazon Aurora with minimal modifications in the code. 

Why it’s Important.

This new feature in Amazon Aurora, means enterprises with legacy applications can migrate to the Cloud without the time, effort and huge costs involved in rewriting application codes. In addition, using Babelfish benefits organisations through:

  • Reduced migration costs and no expensive lock-in licensing terms, unlike in commercial-grade databases
  • No interruption in existing Microsoft SQL Server database use since Babelfish can handle the TDS network protocol
  • Availability of the open-source version of Babelfish for PostgreSQL on GitHub under the permissive Apache 2.0 and PostgreSQL licenses 

Who’s impacted

  • CIO
  • Development team leads
  • Business analysts

What’s Next?

More general availability of hyperscale Cloud computing to support scalability and high-performance needs is expected in the coming months from major vendors. The most successful ones will require minimal changes in enterprises' existing SQL Server application code, speed of migration, and ease of switching to other tools post-migration.

Related IBRS Advisory

  1. VENDORiQ: Google Next: Data - PostgreSQL Spanning the Globe
  2. VENDORiQ: Google introduces Database Migration Service

Conclusion: Organisations are using chatbots as information assistants, advisors, and digital services channels. Most businesses start with generic chatbots (as virtual agents), but as the demand for customer communication grows, chatbots require integration with an increasing number of backend systems and improved scalability.

The reason why most chatbot ventures fail is the inability to recognise that the chatbot principle is simple, yet complexity of deployment rises sharply over time. In addition, chatbot design must align the business and target audiences, and both will evolve. This subtle shift over time is important as organisations need to learn the role, tone, specific purpose, and personalities of their chatbots based on actual usage and feedback.

Thus, starting small with continuous, incremental development is the best strategy for chatbot development. However, this iterative approach must balance the development of chatbots with business implementation, and must consider the attributes of the existing and future deployments.