Planet NoSQL

A drawing should have no unnecessary lines and a machine no unnecessary parts.                     William Strunk Jr. , Elements of Style In the book Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems , Martin Kleppmann has written about traits and trade-offs for data infrastructure while designing modern applications. He has given an example architecture for a data system that combines several components. I used this example for the article Example Architectures for Data-Intensive Applications . That article explored just the Couchbase features and functions. A recent twitter thread talked about how AWS has many databases , but most “half baked” services (not my characterization) compete for the same business as DynamoDB!  Could this be a paradox of choice ? Theory is, more choices do not usually lead to better outcomes. However, customers do vote with their dollars a...

  "If you're not using deep learning, you should be."      Jeff Dean, Google Fellow. Google has started out with a mission to empower everything and everyone with artificial intelligence (AI). It has open-sourced Tensorflow and supporting libraries to enable developers and enterprises to build and train models, and infer (predict) using those. Building useful enterprise services with this may take time. Google has also exposed many of the AI services (via Cloud Machine Learning APIs) that can be quite useful in your applications. Extracting features and text from images, translating text from one language to another, and doing sentiment analysis on text can help improve user experience dramatically. There are new businesses and business models enabled by these services alone. Google has done the hard work of building and training the model. You simply call a REST API to exploit their machine learning APIs. These APIs simply act as a declarative service invocation met...