Taking A Tour Of The Google Cloud Platform For Data And Analytics

Summary Google pioneered an impressive number of the architectural underpinnings of the broader big data ecosystem. Now they offer the technologies that they run internally to external users of their cloud platform. In this episode Lak Lakshmanan enumerates the variety of services that are available for building your various data processing and analytical systems. He shares some of the common patterns for building pipelines to power business intelligence dashboards, machine learning applications, and data warehouses. If you’ve ever been overwhelmed or confused by the array of services available in the Google Cloud Platform then this episode is for you. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at dataengineeringpodcast.com/hightouch. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Lak Lakshmanan about the suite of services for data and analytics in Google Cloud Platform. Interview Introduction How did you get involved in the area of data management? Can you start by giving an overview of the tools and products that are offered as part of Google Cloud for data and analytics? How do the various systems relate to each other for building a full workflow? How do you balance the need for clean integration between services with the need to make them useful in isolation when used as a single component of a data platform? What have you found to be the primary motivators for customers who are adopting GCP for some or all of their data workloads? What are some of the challenges that new users of GCP encounter when working with the data and analytics products that it offers? What are the systems that you have found to be easiest to work with? Which are the most challenging to work with, whether due to the kinds of problems that they are solving for, or due to their user experience design? How has your work with customers fed back into the products that you are building on top of? What are some examples of architectural or software patterns that are unique to the GCP product suite? What are the most interesting, innovative, or unexpected ways that you have seen Google Cloud’s data and analytics services used? What are the most interesting, unexpected, or challenging lessons that you have learned while working at Google and helping customers succeed in their data and analytics efforts? What are some of the new capabilities, new services, or industry trends that you are most excited for? Contact Info LinkedIn @lak_gcp on Twitter Website Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Google Cloud Data and Analytics Services Forrester Wave Dremel BigQuery MapReduce Cloud Spanner Spanner Paper Hadoop Tensorflow Google Cloud SQL Apache Spark Dataproc Dataflow Apache Beam Databricks Mixpanel Avalanche data warehouse Kubernetes GKE (Google Kubernetes Engine) Google Cloud Run Android Youtube Google Translate Teradata Power BI Podcast Episode AI Platform Notebooks GitHub Data Repository Stack Overflow Questions Data Repository PyPI Download Statistics Recommendations AI Pub/Sub Bigtable Datastream Change Data Capture Podcast Episode About Debezium for CDC Podcast Episode About CDC with Datacoral Document AI Google Meet Data Governance Podcast Episodes The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast