Tabular

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.