Mark is joined in this episode by Avi Zloof from Evaluex to talk about the new world of elastically-provisioned cloud-hosted analytic databases such as Google BigQuery and Amazon Athena, how their pricing model and vendor strategy differs from the traditional database vendors, and how machine learning can be used to automate performance tuning and optimize workloads in this new world of large-scale distributed query and storage.
Mark is joined in this episode by Google Cloud Platform Developer Advocate Felipe Hoffa, talking about getting started as a developer using Google BigQuery along with Google Cloud Dataflow, Google Cloud Dataprep and Google Cloud Platform's machine learning APIs.
Mark is joined by returning special guest Dan McClary to talk about data modeling and database design on distributed query engines such as Google BigQuery, the underlying Dremel technology and columnar storage format that enables this cloud distributed data warehouse-as-a-service platform to scale to petabyte-size tables spanning tens of thousands of servers, and techniques to optimize BigQuery table joins using nested fields, table partitioning and denormalization.
- Dremel: Interactive Analysis of Web-Scale Datasets
- BigQuery under the hood
- Inside Capacitor, BigQuery’s next-generation columnar storage format
- Drill To Detail Ep.2. 'Future Of SQL On Hadoop', With Special Guest Dan McClary
- Google BigQuery, Large Table Joins and How Nested, Repeated Values and the Capacitor Storage Format (and Looker) Saves the Day
Stewart Bryson returns to the show to join Mark Rittman to discuss new-world BI and data warehousing development using Google BigQuery and Amazon Athena, Apache Kafka and StreamSets, and talks about his experiences with Looker, the cloud-native BI tool that brings semantic modeling and modern development practices to the world of business intelligence.
Mark Rittman is joined by Daniel Mintz from Looker to talk about BI and analytics on Google BigQuery, data modelling on the new generation of cloud-based distributed-data warehousing platforms, and Looker's re-introduction of semantic models to big data analytics developers.
Mark Rittman is joined by Alex Olivier from Qubit to talk about their platform journey from on-premise Hadoop to petabytes of data running in Google Cloud Platform, using Google Cloud Dataflow (aka Apache Beam), Google PubSub and Google BigQuery along with machine learning and analytics to deliver personalisation at-scale for digital retailers around the world.