I'm a big fan of Mark Litwintschik's latest series of blog posts. He's been measuring every big data platform he can find using the billion taxi trips made available as open data by the NYC TLC.Spoiler alert: I love the stellar results for Google BigQuery and Google Cloud Dataproc — but before we get there, here's my visualization of Mark's findings in a nutshell:Design of a Cost Efficient Time Series Store for Big Data. ... and it's also what serves as the storage layer for BigQuery, according to Google. ... Hive or Presto on top of the same HDFS ...The 2018 benchmark compares price, performance, and differentiated features for the most popular cloud data warehouses—Azure, BigQuery, Presto, Redshift, and Snowflake.Machine Learning, Data Science, Big Data, Analytics, AI How does that change for different user roles (e.g. data engineer vs sales management) What are the scaling factors for Looker, both in terms of volume of data for reporting from, and for user concurrency? What are the most challenging aspects of building a business intelligence tool and company in the modern data ecosystem? solution benchmark. Introduction In the last few years, Big Data Analytics have gained a very fair amount of success. The trend is expected to grow rapidly with further advancement in the coming years. Today, there is a plethora of ... Google BigQuery, Impala, Presto, Hive, Spark and Azure SQL Data Warehouse.Google BigQuery vs Presto: What are the differences? Developers describe Google BigQuery as "Analyze terabytes of data in seconds".Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure Load data with ease.