Performance tuning and best practices for Google BigQuery V2 Connector

Performance tuning and best practices for Google BigQuery V2 Connector

Overview

Overview

Performance tuning is an iterative process in which you analyze the performance, use guidelines to estimate and define parameters that impact the performance, and monitor and adjust the results as required.
This document describes the key hardware, database, Google Compute Engine, Secure Agent, and Informatica mapping parameters that you can tune to optimize the performance of Google BigQuery V2 Connector. This document also includes case studies that involve the tuning specifications required when you configure a mapping to write data to Google BigQuery from a flat file using Google Compute Engine and the Secure Agent. The graphs constructed from the case studies illustrate the impact that the tuning has on Google BigQuery V2 Connector performance.
The performance testing results listed in this article are based on observations in an internal Informatica environment using data from real-world scenarios. The performance of Google BigQuery V2 Connector might vary based on individual environments and other parameters even when you use the same data.