Mick Semb Wever
Committer and on the PMC for Apache Cassandra
Asker, Norway
Principal Architect at DataStax, engineering and product on Cassandra and Open Source.
Beyond technology, crazy about snowboarding, rock climbing, trail running, skiing, surfing, and just anything awesome in nature.
Area of Expertise
Topics
Using Kaskada to do Real Time Machine Learning
Real-time and Soft-AI is about bringing ML models *to* the data, instead of bringing data to machine learning. With modern microservices data platforms it’s very accessible, and now cost effective, to have data low-latency at scale and to bring models to the data.
The age of the microservices database architecture for real-time data platforms is here, evident by big tech's leaps forward in the soft AI space. How is separating compute and storage in the analytics platform: avoiding copying data between platforms; a requirement to real-time ML, to enabling AI platforms, and well as providing better data governance and saving infrastructure costs.
The Accord Consensus Protocol, how to do ACID transactions globally at any scale
The Accord Consensus Protocol, providing global leaderless single-network-round-trip consensus using commodity clocks.
Research from University of Michigan & Apple Inc. introduces ACID-compliant, strict serialisable transactions possible globally at scale, at high throughput, with low latency.
Move over Paxos, ePaxos, Raft, Janus, Calvin, all now outdated, slow & faulty.
We'll explain why your db must be leaderless, scalable & fast. The notion of copying data to analytics is dead weight in the age of Data Mesh & Soft-AI. Your db can no longer be a single-process monolith from ages decades old if you are to be David against the Goliath of Big Tech.
Mick will explain trade-offs from sharding data, shadow paging, sagas, to choosing eventual consistency & isolation consistency in the db.
Why K8s is the Best Technology for Running a Cloud-Native Database
What does Kubernetes provide that allows us to reduce the complexity of Apache Cassandra while making it better suited for cloud native deployments?
That was the question we started with as we began a mission to bring Cassandra closer to Kubernetes and eliminate the redundancy. Many great open source databases have been adapted to run on Kubernetes, without relying on the deep ecosystem of projects that it takes to run in Kubernetes (there is a difference).
The design and implementation of the Astra Serverless Database re-architected Apache Cassandra to run only on Kubernetes infrastructure, and is a silicon valley success story on top of Kubernetes taking advantage of some of its best values. Built to be optimised for multi-tenancy and auto-scaling, we set out with a design goal to completely separate compute and storage. Decoupling different aspects of Cassandra into scaleable services and relying on the benefits of Kubernetes and it's ecosystem created a simpler more powerful database service than a stand alone, bare-metal Cassandra cluster. The entire system is now built on Apache Cassandra, Stargate, Etcd, Prometheus, and object-storage like Minio or Ceph.
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