Google Cloud Platform Podcast BigLake with Gaurav Saxena and Justin Levandoski
Data Warehouses vs. Data Lakes
- Data warehouses are suitable for structured data and business intelligence.
- Data lakes accommodate diverse data types and offer flexibility for analytics.
Data Lake Shortcomings
- Data lakes offer flexibility for AI/ML and unstructured data but lack enterprise features.
- Enterprises desire uniform features across data lakes and warehouses.
BigLake's Foundation
- BigLake builds upon BigQuery's foundation, extending its capabilities.
- BigQuery's modular architecture facilitates expansion to data lakes.
Stephanie Wong and Debi Cabrera are learning all about BigLake from guests Gaurav Saxena and Justin Levandoski of the BigQuery team.
BigLake offers unified data management from both data warehouses and data lakes. What exactly is the difference between a data warehouse and a data lake? Justin explains what a data lake is, how they came to be, and the benefits. Each data option has its cons too, like the limitations of data lakes for enterprise use. Enter BigLake built on BigQuery, which helps enterprise clients manage and analyze their data from both data warehouses and data lakes. The best features of BigQuery are now available for Google Cloud Storage and across multi-cloud solutions.
Guarav describes BigLake behind the scenes and how the principles of BigQuery’s data management can now be used for open file formats in BigLake. It’s BigQuery for more data formats, Justin explains. BigLake solves many data problems quickly with a special emphasis on improving security. Our guests talk specifically about clients who gain the most from using BigLake, especially those looking to analyze distributed data and those who need easy and fast security and compliance solutions. With tightened security, BigLake offers access delegation and secure APIs that work over object storage. We hear about the user experience and how easy it is to get started, especially for customers already familiar with and using other GCP products.
Google’s advocacy of open source projects means many clients are coming in with workloads built with open source software. BigLake supports multi-cloud projects so that tables can be built on top of any data system. No matter the format of your data, you can run analytics with BigLake. We talk more about the security features of BigLake and how easy it is to unify data warehouses and data lakes with optimal data security.
The customers have helped shape BigLake, and Gaurav describes how these clients are using this data software. We hear about integration with BigQuery Omni and Dataplex and how BigLake is different. In the future, Google will continue to make simple, effective solutions for data management and analytics, building further off of BigQuery.
Gaurav SaxenaGaurav Saxena is a product management lead at Google BigQuery. He has 12+ years of experience building products at the intersection of cloud, data and AI. Before Google, Gaurav led product management at Microsoft Azure and Amazon Web Services for some of the most widely used cloud offerings in storage and data.
Justin LevandoskiJustin is a tech lead/manager in BigQuery leading BigLake and other projects pushing the frontier of BigQuery. Prior to Google, just worked on Amazon Aurora and was part of the Database research group at Microsoft Research.
Cool things of the week- Your ultimate guide to Speech on Google Cloud blog
- Announcing the Climate Innovation Challenge—grants to support cutting-edge earth research blog
- BigLake site
- BigQuery site
- Cloud Storage site
- Spark site
- Apache Ranger site
- BigQuery Omni docs
- Apache Iceberg site
- Delta Lake site
- Presto site
- TensorFlow site
- Dataplex site
Debi is working on a series about automatic DLP. Cloud Data Loss Prevention is now automatic and allows you to scan data across your whole org with the click of one button!
HostsStephanie Wong and Debi Cabrera