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.
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- 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