December 2016 Summaries

In-memory technology adoption is increasing rapidly due to growing demands for real-time data access and analytics, as well as rising transaction volumes globally. Companies are seeking faster ingest and analytics engines to stay competitive in the on-demand economy, where performance inconsistencies can be a major disadvantage. As users and businesses need access to more data, companies must evaluate their business needs and select an appropriate in-memory solution, such as a pure in-memory database or a disk-based/persistent database with an in-memory column store. The current state of in-memory computing is characterized by diverse systems that offer varying benefits and use cases, including classification, business advantages, leading use cases from corporations, and predictions for future developments.

In some industries, a hesitance remains in recognizing the commodification forces of real-time solutions as orthodox tenets such as regulatory compliance and traditional value propositions act as barriers to marketplace entry. The Property and Casualty Insurance industry is an illustrative example where many traditional insurers capture little to no real-time data about driver behavior and vehicle performance. However, companies like Metromile are transforming the personal auto insurance market with a modern model that uses IoT technology to provide data-driven insights and services to customers. Despite technological challenges, competitors are taking notice of usage-based insurance and several US insurers now underwrite policies requiring telematic devices. Real-time technology is available on commodity hardware, and enterprises can benefit by using systems like Apache Kafka and SingleStore to improve network operations and reduce integration complexity.

The history of SQL dates back to 1970 when E.F. Codd published a seminal paper on a relational model of data for large shared data banks, leading to the development of the relational database market into a $36 billion industry. In recent years, NoSQL databases gained popularity, but many companies soon realized that they lacked the ability to analyze their data easily, highlighting the limitations of NoSQL without SQL. However, new products and cloud services have emerged that enable scalable SQL systems, allowing for easy analytics and data analysis. The convergence between old and new approaches in database technology is evident, with SQL becoming increasingly relevant as NoSQL databases offer SQL interoperability and distributed architectures. Amazon Web Services' statistics show that SQL is still the dominant force in the cloud, with services like Redshift, Aurora, and Athena delivering SQL on top of S3, demonstrating that SQL never left and remains essential for data analysis.