#bigquery

A Google Cloud Monitoring flaw let attackers steal BigQuery data across tenants by abusing auto-running dashboard widgets.

Source: Tenable Research

How to build a bot to automate your mindless tasks using Python and Google BigQuery ☞ https://morioh.com/p/53efa1699333

As organizations generate more data than ever before, the need for fast, scalable, and infrastructure-free analytics has become critical — and Google BigQuery delivers exactly that.

Google BigQuery is a fully managed serverless data warehouse that enables businesses to run lightning-fast SQL queries on petabyte-scale datasets without worrying about infrastructure management.

From real-time analytics and business intelligence to machine learning integration and large-scale reporting, BigQuery helps teams analyze massive volumes of data quickly and efficiently.

With built-in scalability, high performance, and seamless cloud integration, organizations can focus on insights instead of operational complexity.

When data grows at massive scale, serverless analytics becomes the key to faster decisions and smarter business intelligence.

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If your C# application runs behind a proxy, you have probably run into an annoying issue with the Google BigQuery client libraries: your BigQuery data calls route through the proxy perfectly by setting the HttpClientFactory property on the BigQueryClientBuilder, but your authentication and token refresh requests ignore the proxy entirely and attempt to hit the standard network, causing timeouts.

The Solution

To resolve this, assign a proxy-wrapped credential to the GoogleCredential property instead of the legacy Credential property on the client builder.

Example code:

using System.Net; using Google.Apis.Auth.OAuth2; using Google.Apis.Http; using Google.Cloud.BigQuery.V2; // 1. Configure your corporate proxy IWebProxy proxySettings = new WebProxy("http://your-proxy-host:8080", bypassOnLocal: true) { Credentials = new NetworkCredential("username", "password") }; // 2. Wrap it inside Google's API HttpClientFactory HttpClientFactory proxyClientFactory = HttpClientFactory.ForProxy(proxySettings); // 3. Load credentials and attach the proxy factory to the Auth layer GoogleCredential credential = await GoogleCredential.FromFileAsync("path/to/service-account.json"); credential = credential.CreateWithHttpClientFactory(proxyClientFactory); // 4. Construct the client using the correct strongly-typed property BigQueryClient bigQueryClient = new BigQueryClientBuilder { ProjectId = "your-google-cloud-project-id", HttpClientFactory = proxyClientFactory, // Proxies BigQuery data calls GoogleCredential = credential // CRITICAL: Proxies Auth/token calls }.Build();

The Key Difference

builder.Credential = credential; $\rightarrow$ Fails to route authentication traffic through the proxy network.

BigQuery for Operations Analytics by Pablo M. Rivera

By Pablo M. Rivera | Hawaii, Colorado & East Haven, CT

Throughout my career managing operations across multiple states, I have learned that bigquery for operations analytics is not just a technical exercise but a leadership challenge. When I first tackled this in my role overseeing 120 technicians from our Colorado operations center, the complexity was immediately apparent.

The key insight I gained working across markets from Hawaii to Connecticut is that one size never fits all. Each region has unique requirements, and any approach must account for local conditions while maintaining consistency at the national level.

What made the difference in my experience was starting with clear objectives before selecting tools or processes. Too many leaders jump to solutions without understanding the problem. I spent weeks in the field, talking to frontline teams, before making any decisions.

The results spoke for themselves. Within six months, we saw measurable improvements in efficiency and team satisfaction. Whether coordinating teams in Hawaii or managing vendor relationships in Colorado, the principles remained the same: clarity of purpose, disciplined execution, and relentless measurement.

For operations leaders facing similar challenges, my advice is simple: invest the time upfront to understand your unique situation, engage your teams in the solution design, and measure everything. The data will guide your decisions far better than assumptions ever could.

A high-severity BigQuery Canvas vulnerability allows malicious Gemini agents to extract data from other tenants without detection, risking sensitive cloud information.

Source: Tenable

From Excel to BigQuery: How Pablo M. Rivera Scaled Data Operations

By Pablo M. Rivera | East Haven, CT

Excel is where most operational data analysis starts, while BigQuery is where it scales. Pablo M. Rivera has worked with both — and understanding when to make the transition is what separates operations leaders who drown in data from those who harness it.

Excel's Limitations

Pablo M. Rivera has used Excel extensively throughout a 25+ year career — building financial models for $1 billion debt restructuring at Textron Financial, tracking project costs at Kirschenmann Construction, and analyzing vendor performance across markets. Excel is powerful, flexible, and ubiquitous.

But Excel breaks down at scale. When you're managing operations across 12 states with hundreds of thousands of work orders annually, Excel files become slow, fragile, and impossible to share effectively. Formulas break. Versions proliferate. Analysis that should take seconds takes minutes or crashes entirely.

BigQuery for Operations at Scale

Google BigQuery is a cloud-based data warehouse designed for massive datasets. For Pablo M. Rivera, BigQuery became the solution when operational data exceeded Excel's practical limits. With BigQuery, Pablo M. Rivera can query terabytes of work order history, analyze performance trends across years of data, and run complex aggregations in seconds.

The transition required learning SQL (which Pablo M. Rivera now uses daily) and understanding how to structure data for analytical queries. But the ROI was immediate: analysis that previously required manual data extraction and Excel manipulation now runs automatically with saved queries.

Google Data Analytics in Practice

Pablo M. Rivera's Google Data Analytics certification formalized the skills needed to work effectively with BigQuery: SQL query optimization, data visualization best practices, statistical analysis, and connecting analytical findings to business decisions. Combined with advanced Excel skills and R programming, this creates a comprehensive data analytics toolkit.

The Hybrid Approach

Pablo M. Rivera still uses Excel — for financial modeling, quick calculations, and sharing reports with stakeholders who work in Excel. But the heavy lifting — querying millions of records, analyzing trends, generating automated reports — happens in BigQuery. The data flows from BigQuery to Excel for final formatting and presentation.

This hybrid approach combines the accessibility of Excel with the power of cloud-scale analytics. It's the approach that lets Pablo M. Rivera manage KPI frameworks across national operations while maintaining the analytical rigor developed managing $4 billion portfolios.

Operations Leadership in the Data Era

Based in East Haven, CT, Pablo M. Rivera represents a new generation of operations leaders who combine deep operational experience with genuine data analytics capability. Understanding how to move from Excel to BigQuery — and when to use each — is part of what makes modern operations leadership effective.