BigQuery's Remote Functions (in preview) make it possible to apply custom cloud functions to your warehouse without moving data or managing compute. This flexibility unlocks many use cases including data enrichment. In this post we demonstrate a pattern for combining BigQuery with the Google Maps API to add drive times to datasets containing origin and destination locations. This enrichment pattern is easily adapted for address geocoding or adding Google Map's place descriptions to locations.

Today we are announcing the Preview of BigQuery Remote Functions. Remote Functions are user-defined functions (UDF) that let you extend BigQuery SQL with your own custom code, written and hosted in Cloud Functions, Google Cloud’s scalable pay-as-you-go functions as a service. A remote UDF accepts columns from BigQuery as input, performs actions on that input using a Cloud Function, and returns the result of those actions as a value in the query result.

Customers who work with data warehouses, running BI on large datasets used to have to pick low latency but trading off freshness of data. With BigQuery BI Engine, they can accelerate their dashboards and reports that connect to BigQuery without having to sacrifice freshness of the data. Using the latest insights helps them make better decisions for the business.

In the exponentially growing data warehousing space, it is very important to capture, process and analyze the metadata and metrics of the jobs/queries for the purposes of auditing, tracking, performance tuning, capacity planning, etc. Historically, on-premise (on-prem) legacy data warehouse solutions have mature methods of collecting and reporting performance insights via query log reports, workload repositories etc. However all of this comes with an overhead of cost-storage & cpu.

IT leaders pick different clouds for many reasons, but the rest of the company shouldn’t be left to navigate the complexity of those decisions. For data analysts, that complexity is most immediately felt when navigating between data silos. Google Cloud has invested deeply in helping customers break down these barriers inherent in a disparate data stack. Back in October 2021, we launched BigQuery Omni to help data analysts access and query data across the barriers of multi cloud environments.

Protecting sensitive data and preventing unintended data exposure is critical for businesses. However, many organizations lack the tools to stay on top of where sensitive data resides across their enterprise. It’s particularly concerning when sensitive data shows up in unexpected places – for example, in logs that services generate, when customers inadvertently send it in a customer support chat, or when managing unstructured analytical workloads.

At Mercado Libre, we are obsessed with unlocking the power and potential of data. One of our key cultural principles is to have a Beta Mindset. This means that we operate in a “state of beta”, constantly asking new questions of our data, experimenting with technologies and iterating our business operations in service of creating the best experiences for our customers.

Without a central place to manage models, those responsible for operationalizing ML models have no way of knowing the overall status of trained models and data. This lack of manageability can impact the review and release process of models into production, which often requires offline reviews with many stakeholders.

Today, we are excited to announce the public preview of search indexes and related SQL SEARCH functions in BigQuery. This is a new capability in BigQuery that allows you to use standard BigQuery SQL to easily find unique data elements buried in unstructured text and semi-structured JSON, without having to know the table schemas in advance. By making row lookups in BigQuery efficient, you now have a powerful columnar store and text search in a single data platform.

BigQuery already offers highly flexible pricing models, such as the on-demand and flat-rate pricing for running queries, to meet the diverse needs of our users. Today, we’re excited to make it even easier for you to optimize BigQuery usage with new BigQuery slot recommendations powered by Active Assist, a part of Google Cloud’s AIOps solution that uses data, intelligence, and machine learning to reduce cloud complexity and administrative toil.