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| Name | Using Starburst Enterprise and Tableau | DevCenter |
| Video Uploader | Video From Starburst |
| Upload Date | This Video Uploaded At 07-02-2024 19:35:43 |
| Video Discription | Starburst Enterprise offers a single point of access to all of your data sources, whether they be on-prem or in the cloud. This includes all major cloud providers, like AWS, Azure, and GCP. You can use Starburst Enterprise's large selection of catalog connectors to query data from multiple sources as if they were residing in one source. So now you can pull in data from an AWS data lake, and join it with data from an on-prem PostgreSQL, MySQL or MongoDB database. You can join data from an Oracle, or Snowflake data warehouse. And while you're doing that, you can pivot new data to an Apache Iceberg open data lakehouse format, where you can have all the flexibility of a data lake and all the functionality of a data warehouse. Starburst Enterprise also integrates with Tableau, providing a clear, easy way to view and consume data for analysis. With all of these data sources queried from a single place, the opportunities are huge. Suddenly, Tableau can access data from multiple sources via Starburst Enterprise. What's more, this connection is built to scale. So whether it's gigabytes, terabytes, or petabytes of data, you can use a Starburst Enterprise workflow to deliver better insights from more data. This video sets out this exact use case. In it, Lester Martin and Chris Ghyzel walk through a typical workflow for Starburst Enterprise and Tableau, and demonstrate some of the use cases and scenarios you're likely to encounter when implementing this. Both presenters are career technologists hailing from the data engineering and data science communities. They know exactly what problems data professionals face and exactly how Starburst Enterprise and Tableau can help solve these problems together. Contents of this video: 00:00 - Introduction 03:05 - Connecting Starburst Enterprise to Tableau 07:36 - Query Overview 09:12 - Completed queries 15:12 - Results 16:42 - Parallelism 17:49 - Memory usage 20:00 - Visualizing relationships 22:25 - Failure Info 25:44 - Building relationships between tables 31:45 - Conclusion #starburstdata #starburst #data #dataanalytics #dataengineering #sql #tableau #bitools #businessintelligence #dataanalyst #sql #datapipline #etl #etlpipeline #aws #azure #gcp #onpremise #powerbi #datalakehouse #datawarehouse #datawarehousing #datalake #trino #cloudcomputing [msWt78UBH98] |
| Category | Science & Technology |
| Tags | presto | prestosql | prestodb | starburst presto | starburst data | analytics | big data | sql |
