There are plenty of trends and hot topics in the enterprise technology market today. One common area we hear about from users is that there’s a lot of data to collect, manage, and analyze. And whatever industry you’re in, you probably want to do something more with your data. We built BigQuery, one of the important tools in the Google Cloud Platform (GCP) arsenal, to provide serverless cloud data warehousing and analytics with built-in machine learning to meet modern data needs.
As more and more businesses look to the cloud to store and manage their data, an increasing number are embracing BigQuery as their serverless, highly scalable, enterprise data warehouse.
This month we released several new features in beta, including query scheduling, new BigQuery ML models and functions, and geospatial types and queries. We also released the ORC ingest format into GA. Let’s take a closer look at these features and what they might mean for you.
BigQuery, Google Cloud’s serverless, highly scalable, low-cost, enterprise data warehouse, was designed to make data analysts productive. With no infrastructure to manage, customers can focus on analyzing data using familiar Standard SQL, while simplifying database administration and data operations. Large enterprises, mid-market growing organizations, and cloud native startups across the globe can use BigQuery to perform analytics at scale with equal ease.
To build and develop an advanced data ecosystem is the dream of any data team, yet that often means understanding how the business will need to store and process that data. As Traveloka’s data engineers, one of our most important obligations is to custom-tailor our data delivery tools for each individual team in our company, so that the business can benefit from the data it generates.
