In the BigQuery Spotlight series, we talked about Monitoring. This post focuses on using Audit Logs for deep dive monitoring. BigQuery Audit Logs are a collection of logs provided by Google Cloud that provide insight into operations related to your use of BigQuery. A wealth of information is available to you in the Audit Logs. Cloud Logging captures events which can show “who” performed “what” activity and “how” the system behaved.
Data teams across companies have continuous challenges of consolidating data, processing it and making it useful. They deal with challenges such as a mixture of multiple ETL jobs, long ETL windows capacity-bound on-premise data warehouses and ever-increasing demands from users. They also need to make sure that the downstream requirements of ML, reporting and analytics are met with the data processing.
As customers grow their analytical workloads and footprint on BigQuery, their monitoring and management requirements evolve - they want to be able to manage their environments at scale, take action in context. They also desire capacity management capabilities to optimize their BigQuery environments. With our BigQuery Administrator Hub capabilities, customers can now better manage BigQuery at scale.
Mistakes are part of human nature. Who hasn’t left their car unlocked or accidentally hit “reply all” on an email intended to be private? But making mistakes in your enterprise data warehouse, such as accidentally deleting or modifying data, can have a major impact on your business.
BigQuery is a fully-managed enterprise data warehouse that helps you manage and analyze your data with built-in features like machine learning, geospatial analysis, and intelligent caching for business intelligence. To help you make the most of BigQuery, we’re offering the following no cost, on-demand training opportunities.
After migrating a Data Warehouse to Google Cloud BigQuery, ETL and Business Intelligence developers are often tasked with upgrading and enhancing data pipelines, reports and dashboards. Data teams who are familiar with SQL Server Integration Services (SSIS) and SQL Server Reporting Services (SSRS) are able to continue to use these tools with BigQuery, allowing them to modernize ETL pipelines and BI platforms after an initial data migration is complete.
Advertising agencies are faced with the challenge of providing the precision data that marketers require to make better decisions at a time when customers’ digital footprints are rapidly changing. They need to transform customer information and real-time data into actionable insights to inform clients what to execute to ensure the highest campaign performance.
When you build a data warehouse, the important question is how to ingest data from the source system to the data warehouse. If the table is small you can fully reload a table on a regular basis, however, if the table is large a common technique is to perform incremental table updates. This post demonstrates how you can enhance incremental pipeline performance when you ingest data into BigQuery.
Organizations are increasingly investing in modern cloud warehouses and data lake solutions to augment analytics environments and improve business decisions. The business value of such repositories increases as customer relationship data is loaded and additional insights are generated.
Over the past few weeks, we have been publishing videos and blogs that walk through the fundamentals of architecting and administering your BigQuery data warehouse. Throughout this series, we have focused on teaching foundational concepts and applying best practices observed directly from customers. Below, you can find links to each week’s content: Query Processing : Ever wonder what happens when you click “run” on a new BigQuery query?
