What can you learn from DBMS success stories in Internet Services?

Netflix recently published how it built Marken, a scalable annotation service using Cassandra, ElasticSearch and Iceberg. Marken allows storing and querying annotations, or tags, on arbitrary entities. Users define versioned schemas for their annotations, which include out-of-the-box support for temporal and spatial objects. An annotation is a piece of metadata that can be attached to an object from any domain. An example of a simple annotation is "Movie Entity with id 1234 has violence". A more complex instance includes an ML algorithm that can identify characters in a frame and wants to store that information in a queryable format.

Airbnb is a rapidly growing marketplace and our infrastructure needs are evolving. Like many other websites, we were using MySQL as our main datastore, but we recently migrated it to RDS. RDS (Relational Database Service) is a web service by Amazon that lets you run a scalable MySQL setup in the cloud while hiding the administrative overhead that usually comes with it. This blog post is about the challenges we faced with MySQL, how RDS solves them, and how we migrated to RDS from our old setup

Spotify has the largest Hadoop cluster in Europe Technologies: Scalding, Hive (a SQL language, for ad-hoc analysis), Apache Crunch, Python MapReduce (for stuff that's scheduled) This presented two main problems Speed of iteration was VERY slow Spotify required QUICK analysis (think bigger stars like Taylor Swift) but this became impossible--the queries themselves were taking an hour. Complex analysis would span several days. I feel this…even with smaller databases, good analysis takes time. I can only imagine how frustrating it can be when you begin to explore data structures like these. When Taylor Swift's music was removed from Spotify…what happened? "I want to understand

Facebook's decision to move from Apache Cassandra to MySQL and then again to Apache Cassandra for their backend was likely driven by a combination of factors, including performance, scalability, and ease of use. When Facebook first started using Apache Cassandra, it was a relatively new and untested technology, but it offered the scalability and performance that Facebook needed to handle the large amounts of data generated by its users. However, as the company grew and its data needs became more complex, it may have found that Cassandra wasn't able to meet its needs as well as other solutions, such as MySQL.

AWS delivers multiple managed DBMS options as a service. Veing a global cloud provider, the opportunities to bring up a DB solution whereever the clientbase is, migrating the data between geographies and the availability that comes with different availability zones or regions, if the latency requirements permit, is unbeaten.

DynamoDB is cloud-native in that it does not run on-premises or even in a hybrid cloud; it only runs on Amazon Web Services (AWS). This enables it to scale as needed without requiring a customer’s capital investment in hardware. It also has attributes common to other cloud-native applications, such as elastic infrastructure deployment (meaning that AWS will provision more servers in the background as you request additional capacity).