Tabular

Series: Tabular Solutions Guest: Jason Reid, Tabular co-founder Subject: Using AWS EMR to read data from Tabular managed Iceberg tables Jason shows Shawn what is involved in setting up AWS EMR to query Tabular-managed Apache Iceberg tables. www.tabular.io aws.amazon.com #iceberg #datalake #apacheiceberg #datalakehouse #emr #tabular

Series: Tabular Solutions Guest: Fokko Driesprong, Senior Software Engineer at Tabular Subject: Using PyIceberg in Outerbounds to build machine learning applications on data hosted in Tabular-managed Apache Iceberg tables. www.tabular.io www.outerbounds.com #iceberg #datalake #apacheiceberg #datalakehouse #outerbounds #tabular #machinelearning #metaflow #pyiceberg

Series: Tabular Bits Subject: Cascading Privileges With cascading privileges, changing user permissions against your databases is fast and easy. It might be because you need to change a role group or just to ensure that you've got everything properly applied in all cases. Whatever the reason, Tabular makes it simple. www.tabular.io iceberg.apache.org #datalake #datalakehouse #dataengineering #tabular #iceberg #apacheiceberg

Series: Tabular Solutions Guest: Jason Reid, Tabular co-founder Subject: Using Spark in Google Colab to read/write data from Tabular managed Iceberg tables Jason shows Shawn how to configure Google Colab to use Apache Spark to read/write data in Tabular-managed Apache Iceberg tables. www.tabular.io https://colab.google/ #iceberg #datalake #apacheiceberg #datalakehouse #redshift #tabular, #apachespark, #googlecolab

Series: Tabular Bits Subject: File Loader Use the Tabular UI to quickly set up an AWS S3 location as a data load source for your Tabular-managed Iceberg tables. Files can be dropped there ad-hoc or delivered by your application, even Kafka streams. Supported file types are JSON, CSV, TSV, and Parquet. Once loaded, Tabular will start automatically optimizing your Iceberg tables to improve performance and lower storage costs. www.tabular.io iceberg.apache.org #datalake #datalakehouse #dataengineering #tabular #iceberg #apacheiceberg

Series: Tabular Solutions Guest: Jason Reid, Tabular co-founder Subject: Using AWS Redshift to read data from Tabular managed Iceberg tables Jason shows Shawn what is involved in setting up AWS Redshift to query Tabular-managed Apache Iceberg tables. www.tabular.io aws.amazon.com #iceberg #datalake #apacheiceberg #datalakehouse #redshift #tabular

Series: Tabular Solutions Guest: Eduard Tudenhöfner, Senior Software Engineer at Tabular Subject: Using Airbyte to write data to Tabular managed Iceberg tables Eduard walks Shawn through what is involved in setting up Airbyte to use Tabular-managed Apache Iceberg tables as a data destination. www.tabular.io www.airbyte.com #iceberg #datalake #apacheiceberg #datalakehouse #airbyte #tabular

Series: Tabular Bits Subject: Creating Tables Use the Tabular UI to quickly create an Iceberg table in a non-programmatic way. Once it is created, you can immediately start writing data to it. www.tabular.io iceberg.apache.org #datalake #datalakehouse #dataengineering #tabular #iceberg #apacheiceberg

Series: Tabular Solutions Guest: Albert Wong, Developer Advocate, CelerData Subject: Accessing Tabular managed Iceberg tables from CelerData Albert shows Shawn how to use CelerData to query and create data in Tabular managed Iceberg tables. CelerData is a managed solution for StarRocks, an open-source MPP OLAP database for full analytics scenarios, including multi-dimensional analytics, real-time analytics, and ad-hoc query. www.celerdata.com www.tabular.io #iceberg #datalake #apacheiceberg #datalakehouse #starrocks #celerdata #tabular

Series: Tabular Bits Subject: Connecting with and using Athena with Tabular Learn how to quickly configure Athena and query your Tabular managed Apache Iceberg tables. www.tabular.io #datalake #datalakehouse #aws #athena #tabular #iceberg #apacheiceberg

Series: Tabular Bits Subject: Connecting with and using Athena/PySpark with Tabular Learn how to quickly configure Athena/PySpark to access your Tabular managed Apache Iceberg tables. www.tabular.io #datalake #datalakehouse #pyspark #aws #athena #tabular #iceberg #apacheiceberg

Series: Tabular Bits Subject: Make Spark secure with Tabular Learn how to secure your data, not your compute, with Tabular. www.tabular.io #datalake #datalakehouse #spark #datasecurity #tabular #iceberg #apacheiceberg

00:00 Intro 05:34 Three main factors affecting cost 11:10 Tabular's optimization techniques 16:34 Illustrate cost savings 22:23 Summary Series: Tabular Office Hours Guest: Jason Reid, Tabular co-founder and head of product Subject: Cost Optimization Jason reviews how to optimize costs on AWS Object Store and how Tabular does it for you automatically. AI Generated Summary: - James Reed, co-founder of Tabular, discussed the benefits of automatic table optimization for cloud data warehousing, which can lower costs and improve query performance. He explained the three components of cost for data warehousing (network, storage, and compute) and provided examples of pricing models, such as Amazon Athena's pricing based on data volume. - - Jason discussed the three main factors that affect the cost of running a cloud data warehouse environment: network, storage, and compute. He also explained how Tabular's automated optimization techniques can reduce costs by organizing data more effectively and executing compute in the background. - Jason discussed how Tabular's platform handles optimization through sorting, compression, and compaction. The platform constantly experiments with different compression settings to find the best combination of size, write performance, and read performance for each table, resulting in a 50-80% reduction in overall data size and significant cost savings. - Jason discussed how Tabular's table optimization can significantly reduce the cost of data warehousing bills by compacting and organizing data into a smaller set of bytes. He demonstrated this through a demo where a table with 434,000 rows and 175 megabytes worth of data was optimized to just over 100 megabytes, resulting in a 40% overall savings on the cost of that workload. - Jason explained how sorting and organizing the data helped to significantly reduce the amount of data that needed to be loaded, resulting in faster response times and lower costs. The team was excited about the optimization features in Tabular.

Series: Tabular Bits Subject: Connecting with and using Snowflake with Tabular Learn how simple it is to connect Snowflake with Tabular to power your analytics with Iceberg tables. www.tabular.io www.snowflake.com #datalake #datalakehouse #snowflake #tabular #iceberg #apacheiceberg

Series: Tabular Solutions Guest: Brian Olsen, Head of Developer Relations, Tabular Subject: Brian, who recently joined Tabular from @StarburstData collaborates with Shawn to demonstrate the seamless integration between Tabular and Starburst Galaxy. They showcase the effortless process of querying Tabular's managed Iceberg tables directly from Starburst Galaxy. #iceberg #datalake #apacheiceberg #datalakehouse #starburst #tabular

Series: Tabular Bits Subject: Drop and Restore Tables Tabular makes it very simple to drop and restore Apache Iceberg tables. This video illustrates the necessary steps. www.tabular.io iceberg.apache.org #datalake #datalakehouse #dataengineering #tabular #iceberg #apacheiceberg

Series: Ask the Iceberg Experts Guest: Ryan Blue, co-creator of Apache Iceberg, and co-founder of Tabular Subject: What is the Puffin file format, and how does it relate to the Apache Iceberg ecosystem? A special thanks to the Trino Software Foundation and Piotr Findeisen for their work on this project. iceberg.apache.org www.tabular.io www.trino.io #iceberg #datalake #datalakehouse #ryanblue #apacheicerg #dataengineering

Series: Tabular Solutions Guest: Alex Merced, Developer Advocate, Dremio Subject: Accessing Tabular managed Iceberg tables from Dremio Shawn and Alex discover how simple it is to use Dremio and Tabular together. #iceberg #datalake #apacheiceberg #datalakehouse #dremio #tabular

Series: Ask the Iceberg Experts Guest: Dennis Huo, Principal Software Engineer, Snowflake Subject: Snowflake support of Iceberg Dennis talks about Snowflake support of Iceberg, what it was like developing it, what it was like working with the Iceberg community and the Snowflake Catalog. iceberg.apache.org #iceberg #datalake #snowflake #tabular

Series: Tabular Bits Subject: Connecting with and using Trino, with Tabular www.tabular.io www.trino.io #datalake #datalakehouse #trino #tabular #iceberg #apacheiceberg

Series: Ask the Iceberg Experts Guest: Thomas Cardenas, Senior Software Engineer, Ancestry Subject: Ancestry implementation of Iceberg Thomas talks about his recent blog post on implementing and optimizing a 100 billion row table in Apache Iceberg for the Hints database at Ancestry. https://medium.com/ancestry-product-and-technology/scaling-ancestry-com-how-to-optimize-updates-for-iceberg-tables-with-100-billion-rows-860285922316 www.ancestry.com iceberg.apache.org #iceberg #datalake #ancestry #apacheicerg #dataengineering