Setting The Stage For The Next Chapter Of The Cassandra Database
Data Engineering Podcast
00:00
Cassandra
Cassandra is primarily used as a system of record because of the durability guarantees. It's able to be fault tolerant at both the cluster scale and the geographic scale. Debesium essentially allows you to grab that sedy seed data from a stream, jump it into a catcuundor. And i think that's superimportant to get your transactional darty kind of usage in place.
Transcript
Play full episode
Transcript
Episode notes
Summary
The Cassandra database is one of the first open source options for globally scalable storage systems. Since its introduction in 2008 it has been powering systems at every scale. The community recently released a new major version that marks a milestone in its maturity and stability as a project and database. In this episode Ben Bromhead, CTO of Instaclustr, shares the challenges that the community has worked through, the work that went into the release, and how the stability and testing improvements are setting the stage for the future of the project.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- Schema changes, missing data, and volume anomalies caused by your data sources can happen without any advanced notice if you lack visibility into your data-in-motion. That leaves DataOps reactive to data quality issues and can make your consumers lose confidence in your data. By connecting to your pipeline orchestrator like Apache Airflow and centralizing your end-to-end metadata, Databand.ai lets you identify data quality issues and their root causes from a single dashboard. With Databand.ai, you’ll know whether the data moving from your sources to your warehouse will be available, accurate, and usable when it arrives. Go to dataengineeringpodcast.com/databand to sign up for a free 30-day trial of Databand.ai and take control of your data quality today.
- Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription
- Your host is Tobias Macey and today I’m interviewing Ben Bromhead about the recent release of Cassandra version 4 and how it fits in the current landscape of data tools
Interview
- Introduction
- How did you get involved in the area of data management?
- For anyone who isn’t familiar with Cassandra, can you briefly describe what it is and some of the story behind it?
- How did you get involved in the Cassandra project and how would you characterize your role?
- What are the main use cases and industries where someone is likely to use Cassandra?
- What is notable about the version 4 release?
- What were some of the factors that contributed to the long delay between versions 3 and 4? (2015 – 2021)
- What are your thoughts on the ongoing utility/benefits of projects such as ScyllaDB, particularly in light of the most recent release?
- Cassandra is primarily used as a system of record. What are some of the tools and system architectures that users turn to when building analytical workloads for data stored in Cassandra?
- The architecture of Cassandra has lent itself well to the cloud native ecosystem that has been growing in recent years. What do you see as the opportunities for Cassandra over the near to medium term as the cloud continues to grow in prominence?
- What are some of the challenges that you and the Cassandra community have faced with the flurry of new data storage and processing systems that have popped up over the past few years?
- What are the most interesting, innovative, or unexpected ways that you have seen Cassandra used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Cassandra?
- When is Cassandra the wrong choice?
- What is in store for the future of Cassandra?
Contact Info
- @benbromhead on Twitter
- benbromhead on GitHub
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Cassandra
- Instaclustr
- HBase
- DynamoDB Whitepaper
- Property Based Testing
- QuickTheories
- Riak
- FoundationDB
- ScyllaDB
- YugabyteDB
- Azure CosmoDB
- Amazon Keyspaces
- Netty
- Kafka
- CQRS == Command Query Responsibility Segregation
- Elasticsearch
- Redis
- Memcached
- Debezium
- CDC == Change Data Capture
- Bigtable White Paper
- CockroachDB
- Vitess
- CAP Theorem
- Paxos
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
