Cloud Database Report Podcast

The adoption of cloud databases is accelerating, driven by business transformation and the need for database modernization. 

In this episode of the Cloud Database Report Podcast, founding editor John Foley talks with Andi Gutmans, Google Cloud's GM and VP of Engineering for Databases, about the platforms and technologies that organizations are using to build and manage these new data environments. 

Gutmans is responsible for development of Google Cloud's databases and related technologies, including Bigtable, Cloud SQL, Spanner, and Firestore. In this conversation, he discusses the three steps of cloud database adoption: migration, modernization, and transformation. "We're definitely seeing a tremendous acceleration," he says. 

Gutmans talks about the different types of database migrations, from "homogenous" migrations that are relatively fast and simple to more complex ones that involve different database sources and target platforms. He reviews the tools and services available to help with the process, including Google Cloud's Database Migration Service and Datastream for change data capture. 

Gutmans provides an overview of the "data cloud" model as a comprehensive data environment that connects multiple databases and reduces the need for organizations to build their own plumbing. Data clouds can "democratize" data while providing security and governance. 

Looking ahead, Google Cloud will continue to focus on database migrations, developing new enterprise capabilities, and providing a better experience for developers. 

Alexa Weber Morales has years of experience writing about the developer community, cloud infrastructure, and database tools. She had a long career in tech journalism, including as Editor in Chief of Software Development magazine, prior to joining Oracle as a writer, editor, and content strategist. 

In this podcast, John Foley, Editor of the Cloud Database Report, talks to Alexa about cloud-native database development, digital transformation, online education, and more.  The conversation ranges from Kubernetes to Java development to building applications with Oracle's Apex low-code development platform. Alexa also talks about what motivates and inspires developers. 

An interesting note about Alexa — she is also a Grammy-award winning singer, songwriter, and musician known for her work in salsa jazz. In this podcast, Alexa talks about using online learning to write her first symphony. 

Pinecone Systems' new vector database provide similarity search as a  cloud service. Use cases include recommendations, personalization, image search, and deduplication of records. 

A vector, or vector embedding, is a string of numbers that represents documents, images, or other data. Vectors are used in the development of machine learning applications. A vector database stores, searches, and retrieves the representations by similarity or by relevance.

Pinecone’s vector database is accessed through an API. Early adopters range from startups to large companies with machine learning initiatives that need to scale. 

Pinecone Systems’ lead investor was also an early investor in Snowflake, and the similarities don’t stop there.