The database is the core of any system because it holds the data that drives your entire experience. We spend countless hours designing the data model, updating engine versions, and tuning performance. But how confident are you that you have configured it to be as performant as possible, given the dozens of parameters and how they interact with each other? Andy Pavlo researches autonomous database systems, and out of that research he created OtterTune to find the optimal set of parameters to use for your specific workload. In this episode he explains how the system works, the challenge of scaling it to work across different database engines, and his hopes for the future of database systems.
Census is the operational analytics platform that syncs your cloud warehouse with all the SaaS applications used by your Sales, Marketing & Success teams. If you need to get your company data into Salesforce, Marketo, Hubspot, Intercom, Zendesk, and other tools, Census is the easiest way to do so. Just write SQL (or plug in your dbt models), set up the sync frequencies, and voila, your data is now available to be used by all of your teams. No need to worry about incremental sync, backfilling, API quota management, API versioning, monitoring, and maintaining custom scripts. Just SQL. Start your free 14-day trial now.
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RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines.
RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team.
RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again.
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Andy Pavlo about OtterTune, a system to continuously monitor and improve database performance via machine learning
- How did you get involved in the area of data management?
- Can you describe what OtterTune is and the story behind it?
- How does it relate to your work with NoisePage?
- What are the challenges that database administrators, operators, and users run into when working with, configuring, and tuning transactional systems?
- What are some of the contributing factors to the sprawling complexity of the configurable parameters for these databases?
- Can you describe how OtterTune is implemented?
- What are some of the aggregate benefits that OtterTune can gain by running as a centralized service and learning from all of the systems that it connects to?
- What are some of the assumptions that you made when starting the commercialization of this technology that have been challenged or invalidated as you began working with initial customers?
- How have the design and goals of the system changed or evolved since you first began working on it?
- What is involved in adding support for a new database engine?
- How applicable are the OtterTune capabilities to analytical database engines?
- How do you handle tuning for variable or evolving workloads?
- What are some of the most interesting or esoteric configuration options that you have come across while working on OtterTune?
- What are some that made you facepalm?
- What are the most interesting, innovative, or unexpected ways that you have seen OtterTune used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on OtterTune?
- When is OtterTune the wrong choice?
- What do you have planned for the future of OtterTune?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- CMU (Carnegie Mellon University)
- Brown University
- Michael Stonebraker
- Learned Indexes
- Oracle DB
- Gaussian Process Model
- Reinforcement Learning
- AWS Aurora
- MVCC (Multi-Version Concurrency Control)
- MySQL Tuner