Posts in tag

BigQuery;


Most data analysts are familiar with the concept of organizing data into clusters so that it can be queried faster and at a lower cost. The user behavior dictates how the dataset should be clustered: for example, when a user seeks to analyze or visualize geospatial data (a.k.a location data), it is most efficient to …

Are you worried about controlling your BigQuery costs across multiple projects? In this blog, you will learn about the different guardrails BigQuery provides to limit costs, and monitor BigQuery consumption. Also, learn how to design the warehouse that scales seamlessly while keeping costs under your control. 1. Set up user-level quota and project-level quota can …

If you’re a college student like me and are gearing up to enter the “big kids” job market, as I like to call it, then you’ve probably been wondering (or worrying) about how to get ahead of the curve and stand out amongst your peers. When I think about which high-value fields to target for …

Iteration and innovation fuel the data-driven culture at Mercado Libre. In our first post, we presented our continuous intelligence approach, which leverages BigQuery and Looker to create a data ecosystem on which people can build their own models and processes. Using this framework, the Shipping Operations team was able to build a new solution that …

  In the past decade, we have experienced an unprecedented growth in the volume of data that can be captured, recorded and stored.  In addition, the data comes in all shapes and forms, speeds and sources. This makes data accessibility, data accuracy, data compatibility, and data quality more complex than ever more. Which is why …

BigQuery is amazing. It is one of my favorite tools within Google Cloud. Luckily, it looks like Google feels the same and, to the joy of BigQuery fans, keeps adding new features there. No matter how long I have been working with BigQuery, there is always something new I discover once in a while. Today …

Modern businesses are increasingly relying on real-time insights to stay ahead of their competition. Whether it’s to expedite human decision-making or fully automate decisions, such insights require the ability to run hybrid transactional analytical workloads that often involve multiple data sources. BigQuery is Google Cloud’s serverless, multi-cloud data warehouse that simplifies analytics by bringing together …

Today we are excited to announce the release of over twenty new BigQuery and BigQuery ML (BQML) operators for Vertex AI Pipelines, that help make it easier to operationalize BigQuery and BQML jobs in a Vertex AI Pipeline. The first five BigQuery and BQML pipeline components were released earlier this year. These twenty-one new, first-party, …

IBM mainframes have been around since the 1950s and are still vital for many organizations. In recent years many companies that rely on mainframes have been working towards migrating to the cloud. This is motivated by the need to stay relevant, the increasing shortage of mainframe experts and the cost savings offered by cloud solutions. …

We’re excited to launch a new feature for Workflows, a serverless orchestrator for developers that connects multiple Google Cloud and external services. Parallel Steps—now in Preview—enables developers to run multiple concurrent steps, which can help reduce the time it takes to execute a workflow, particularly one that includes long-running operations like HTTP requests and callbacks. …