Mark Rittman is joined in this episode of Drill to Detail by returning guest Kent Graziano, Chief Technical Evangelist for Snowflake, to talk about Snowflake Data Warehouse's cloud-first architecture and recent product announcements at Snowflake Summit 2019.
Mark Rittman is joined in this episode by Jordan Tigani, Director of Product Management at Google for Google BigQuery, to talk about the history of BigQuery and its technology origins in Dremel; how BigQuery has evolved since its original release to handle a wide range of data warehouse workloads; and new features announced for BigQuery at Google Next’19 including BI Engine, Storage API, Connected Sheets and a range of new data connectors from Supermetrics and Fivetran.
Mark Rittman is joined in this episode by Mike Ferguson, long-term analyst, consultant and Managing Director of Intelligent Business Strategies to talk about data warehouse modernization, analytics and big data project success within enterprise customers and the re-emergence of interest in data governance and master data management within the industry.
Mark Rittman is joined by returning Special Guest Mark Grover to talk about his move from Cloudera and product engineering to a product manager role at Lyft; analytics use-cases in the ride-sharing industry; and the move from conversations about ETL tools, technology and engines to templates, paradigms and developer productivity.
- Mark Grover LinkedIn Profile and Github Profile
- "Hadoop Application Architectures"
- "Drill to Detail Ep. 7 'Apache Spark and Hadoop Application Architectures'
- Lyft Engineering Blog
- "Software Engineer to Product Manager" blog by Gwen Shapira
- "Introduction to the Oracle Data Integrator Topology" from the Oracle Data Integrator docs site
- Apache Airflow and Amazon Kinesis homepages
- "Experimentation in a Ridesharing Marketplace" by Nicholas Chamandy, Head of Data Science at Lyft
- "How Uber Eats Works with Restaurants"
- "Deliveroo has built a bunch of tiny kitchens to feed more hungry Londoners" - Wired.co.uk
Mark is joined in this episode of Drill to Detail by Wes McKinney, to talk about the origins of the Python Pandas open-source package for data analysis and his subsequent work as a contributor to the Kudu (incubating) and Parquet projects within the Apache Software Foundation and Arrow, an in-memory data structure specification for use by engineers building data systems and the de-facto standard for columnar in-memory processing and interchange.Read More
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.
Drill to Detail returns for a new season with special guest Jean-Pierre Dijcks, to talk about Oracle's Big Data Strategy now and in the past, thoughts on distributed query and storage in the cloud, and previewing themes and announcements to look forward to at the upcoming Oracle Open World 2017 event running in San Francisco next month
Mark Rittman is joined by Industry Analyst Mark Madsen to talk about marketing analytics and the rise of the omni-channel consumer, the use of AI in analytics and personalization and what this all means for brands, for advertisers and for marketers.
Mark is joined by Qubit colleague Will Browne to talk about a recent academic paper co-authored with Mike Swarbrick Jones on conversion optimisation techniques in the eCommerce industry. Using analytics and statistical analysis On 20 billion "user journeys" recorded in Qubit's Google Cloud Platform-hosted Customer Data Store this paper compares techniques using data and machine learning to those based on traditional sales techniques to see whether data trumps emotion ... or both have their place.