December 2018 Summaries

SingleStore's unique combination of scalability, high performance, and SQL support sets it apart from other databases, allowing for a "plug and play" integration with existing database systems and tools. Its distributed architecture enables massive scaling, handling transactions and analytics simultaneously, all on commodity hardware. SingleStore offers tiered storage, flexible data formats, and a management console with GUI and command-line tools, as well as enterprise security features such as RBAC, encryption, and auditing. The database also supports SQL API, queries, and continuous loading from various sources, making it suitable for organizations requiring relational database structure and NoSQL scalability.

A recent webinar brought together experts from SingleStore, Kollective, and Looker to discuss creating a fast, scalable, and affordable solution for media analytics. With the rise of video delivery, enterprises need to deliver a fast, flexible, and reliable experience while providing valuable insights through analytics. SingleStore can move video data internally, Kollective helps deliver it to users and generate statistics, and Looker makes the data easy to report on, analyze, and explore. The combination of these solutions enables a robust toolset for video data delivery and analytics, with each technology delivering novel and up-to-date solutions based on the latest advances in technology.

A real-time dashboard allows businesses to monitor their performance in real time, enabling them to identify problems before they start and seize opportunities that competitors are unaware of. This is made possible by SingleStore, a relational database with built-in SQL support, which can handle transactions and analytics in a single database, processing times for the latest data from days and hours to minutes and seconds. With SingleStore, businesses like Pandora can update their offerings many times a day, providing customers with better songlists and higher engagement, while also supporting development and operations staff with standard ANSI SQL and fast operation at any needed scale.

AWS re:Invent has seen an estimated 50,000-plus people attend the event, with a focus on emerging fields like AI and machine learning, as well as databases and infrastructure. Amazon announced new hardware, such as Inferentia, a processor designed for high-performance AI applications at low cost. The company also launched AWS Managed Kafka as a public preview, which has sparked concerns about the impact on open source projects and their communities. Additionally, AWS supported trends in blockchain and NewSQL databases like SingleStore, Amazon Aurora, and Google Spanner, with new features and offerings announced for its relational database, Amazon Aurora Global Database, and NoSQL database, Amazon DynamoDB. The event also highlighted concerns about the costs and lock-in associated with using AWS services, as well as the importance of hybrid infrastructure management.

Designing Data-Intensive Applications` is a comprehensive book by Martin Kleppman that emphasizes the importance of data at the center of system design challenges. The author, an expert in distributed systems and researcher at Cambridge University, has extensive experience as a software engineer tackling these topics at companies such as LinkedIn and Rapportive. As SingleStore will argue, however, most applications should be treated as data-intensive, requiring consideration of key data-related questions during design, including the value of core and additional data, operational and archival data stores, in-house data science resources, and potential partnerships to ensure full utilization of data. Machine learning and AI bring these questions to life, highlighting the need for high-quality data to achieve remarkable results. By applying these principles, developers can unlock new opportunities and accomplish things previously thought impossible with machine learning and AI.