Data warehouses have gone through many transformations, from standard relational databases on powerful hardware, to column oriented storage engines, to the current generation of cloud-native analytical engines. SnowflakeDB has been leading the charge to take advantage of cloud services that simplify the separation of compute and storage. In this episode Kent Graziano, chief technical evangelist for SnowflakeDB, explains how it is differentiated from other managed platforms and traditional data warehouse engines, the features that allow you to scale your usage dynamically, and how it allows for a shift in your workflow from ETL to ELT. If you are evaluating your options for building or migrating a data platform, then this is definitely worth a listen.
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 $60 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!
- 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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
- You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media and the Python Software Foundation. Upcoming events include the Software Architecture Conference in NYC and PyCOn US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
- Your host is Tobias Macey and today I’m interviewing Kent Graziano about SnowflakeDB, the cloud-native data warehouse
- How did you get involved in the area of data management?
- Can you start by explaining what SnowflakeDB is for anyone who isn’t familiar with it?
- How does it compare to the other available platforms for data warehousing?
- How does it differ from traditional data warehouses?
- How does the performance and flexibility affect the data modeling requirements?
- Snowflake is one of the data stores that is enabling the shift from an ETL to an ELT workflow. What are the features that allow for that approach and what are some of the challenges that it introduces?
- Can you describe how the platform is architected and some of the ways that it has evolved as it has grown in popularity?
- What are some of the current limitations that you are struggling with?
- For someone getting started with Snowflake what is involved with loading data into the platform?
- What is their workflow for allocating and scaling compute capacity and running anlyses?
- One of the interesting features enabled by your architecture is data sharing. What are some of the most interesting or unexpected uses of that capability that you have seen?
- What are some other features or use cases for Snowflake that are not as well known or publicized which you think users should know about?
- When is SnowflakeDB the wrong choice?
- What are some of the plans for the future of SnowflakeDB?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Data Warehouse
- Oracle DB
- MPP == Massively Parallel Processing
- Shared Nothing Architecture
- Multi-Cluster Shared Data Architecture
- Google BigQuery
- AWS Redshift
- AWS Redshift Spectrum
- SnowflakeDB Semi-Structured Data Types
- ACID == Atomicity, Consistency, Isolation, Durability
- 3rd Normal Form
- Data Vault Modeling
- Dimensional Modeling
- SnowflakeDB Virtual Warehouses
- CRM == Customer Relationship Management
- Master Data Management
- Apache Spark
- SSIS == SQL Server Integration Services
- Apache Kafka
- Snowflake Data Exchange
- OLTP == Online Transaction Processing
- Snowflake Documentation
- Data Catalog