Programming Tutorials

  Few things are more frustrating in data engineering than waiting for a complex query to finish, only to be hit with a vague error message. If you are reading this, you likely just encountered the following error in the BigQuery UI or API: Error: 403 Response too large to return. Consider setting allowLargeResults to true in your job configuration. Despite the HTTP 403 status code—which typically implies a permissions issue—this is actually a data serialization limit. It stops your workflow cold, preventing data extraction or visualization. This guide provides the technical root cause analysis and three proven architectural patterns to bypass this limit permanently using SQL and the Python Client Library. The Root Cause: The 10MB JSON Limit To fix the error, you must understand how BigQuery delivers results. BigQuery is a distributed compute engine capable of scanning petabytes of data in seconds. However, the mechanism for delivering that data back to the client (your browser, Ju...

  In data engineering, few things are as frustrating as a pipeline failure caused by a single malformed character in a 5GB XML feed. If you rely on Python’s built-in  xml.etree.ElementTree , you likely encounter the dreaded  ParseError: not well-formed (invalid token) . Standard XML parsers are designed to fail fast. According to the W3C specification, if XML is not strictly "well-formed," it is fatal. However, the real world is messy. Legacy systems produce unescaped ampersands, web scrapers retrieve truncated responses, and third-party APIs often deliver "XML-ish" data that breaks strict validators. Halting execution is rarely an option. This guide details how to implement robust, fault-tolerant XML parsing using Python and  lxml . The Root Cause: Why Standard Parsers Fail To fix the problem, we must understand the mechanics of the failure. Python's standard library  xml.etree.ElementTree  is often backed by the Expat parser. Expat is a stream-oriented pa...