As businesses scale to meet the demands of their customers, so do their need for efficient products to collect, manage and analyze data to meet their business goals. Whether you are building a multi-player game or a global e-commerce platform, it's critical to ensure that data can be stored and queried at scale with strong consistency and then processed for analysis to deliver real-time insights.
Geographical data is one of the critical datasets for data-driven organizations to make informed business decisions. As the data is growing more than ever before, it’s becoming more challenging to manage and analyze mammoth datasets using traditional databases, this is true for geographical data as well as it requires significant computational power to process. Esri has been one of the leading companies in Geospatial software development since 1969.
The Google BigQuery Write API offers high-performance batching and streaming in one unified API. The previous post in this series introduced the BigQuery Write API. In this post, we'll show how to stream JSON data to BigQuery by using the Java client library.
BigQuery is a serverless, highly scalable, and cost-effective data warehouse that customers love. Similarly, Dataflow is a serverless, horizontally and vertically scaling platform for large scale data processing. Many users use both these products in conjunction to get timely analytics from the immense volume of data a modern enterprise generates.
Digital technology promises transformative results. Yet, it’s not uncommon to encounter potholes and speed bumps along the way. One area that frequently trips up businesses is putting data into action. It can be extraordinarily difficult to take advantage of the right data at exactly the right time — in real time — to drive decision-making. For SAP customers wanting to maximize the value of their data, Google Cloud offers a number of capabilities.
During a Teradata migration to BigQuery, one complex and time consuming process is migrating Teradata users and their permissions to the respective ones in GCP. This mapping process requires admin and security teams to manually analyze, compare, and match hundreds to thousands of Teradata user permissions to BigQuery IAM permissions. We already described this manual process for some common data access patterns in our earlier blog post.
Thirty five years ago, SQL-86, the first SQL standard, came into our world, published as an ANSI standard in 1986 and adopted by the International Standards Organization (ISO) in 1987. On this Valentine’s Day, we, in BigQuery, reaffirm our love and commitment to user-friendly SQL through a whole slew of new SQL features that we’re pleased to share with you, our beloved BigQuery users.
Are you storing your data in BigQuery and interested in using that data to train and deploy models? Or maybe you’re already building ML workflows in Vertex AI, but looking to do more complex analysis of your model’s predictions? In this post, we’ll show you five integrations between Vertex AI and BigQuery, so you can store and ingest your data; build, train and deploy your ML models; and manage models at scale with built-in MLOps, all within one platform. Let’s get started!
