Taking A Tour Of The Google Cloud Platform For Data And Analytics
Data Engineering Podcast
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The Data Cloud Is Part of the Platform
Google's BigQuery is a way to carry out analytics on structured and semi-structured data. It basically scales from really small data sets to petabytes of data. Dataflow is our ETL data processing engine. Apache Beam, which is part of our data cloud, is a very important component for simplifying the productionization of ML models. Vertex AI allows you to develop ML models and to deploy them.
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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
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- 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
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Google Cloud
- Forrester Wave
- Dremel
- BigQuery
- MapReduce
- Cloud Spanner
- 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
- AI Platform Notebooks
- GitHub Data Repository
- Stack Overflow Questions Data Repository
- PyPI Download Statistics
- Recommendations AI
- Pub/Sub
- Bigtable
- Datastream
- Change Data Capture
- Document AI
- Google Meet
- Data Governance
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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