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.
Your data platform needs to be scalable, fault tolerant, and performant, which means that you need the same from your cloud provider. Linode has been powering production systems for over 17 years, and now they’ve launched a fully managed Kubernetes platform. With the combined power of the Kubernetes engine for flexible and scalable deployments, and features like dedicated CPU instances, GPU instances, and object storage you’ve got everything you need to build a bulletproof data pipeline. If you go to dataengineeringpodcast.com/linode today you’ll even get a $100 credit to use on building your own cluster, or object storage, or reliable backups, or… And while you’re there don’t forget to thank them for being a long-time supporter of the Data Engineering Podcast!
Have you ever woken up to a crisis because a number on a dashboard is broken and no one knows why? Or sent out frustrating slack messages trying to find the right data set? Or tried to understand what a column name means?
Our friends at Atlan started out as a data team themselves and faced all this collaboration chaos themselves, and started building Atlan as an internal tool for themselves. 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 and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription.
Databand.ai is a unified Data Observability Platform that helps DataOps teams catch and solve data health issues fast. Databand.ai’s platform helps data engineers pinpoint pipeline issues and quickly identify their root cause so DataOps can begin working on a resolution before bad data is delivered. Whether you’re using Apache Spark, Apache Airflow, Databricks, Amazon S3, self-hosted python scripts, or combinations of these, Databand.ai allows you to monitor data health along every step of its journey. Powerful integrations to 20+ tools gives you full visibility of your stack. Our mission is to help businesses trust their data with the most powerful Data Observability Platform. Experience unified observability with a free trial today: www.databand.ai
- 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
- 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?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- DynamoDB Whitepaper
- Property Based Testing
- Azure CosmoDB
- Amazon Keyspaces
- CQRS == Command Query Responsibility Segregation
- CDC == Change Data Capture
- Bigtable White Paper
- CAP Theorem