As more and more businesses turn to advanced data analytics to help them make smarter decisions, run real-time analytics, and improve business operations, an increasing number are modernizing their data warehouses to make it all possible. For many businesses, knowing how to modernize means understanding where their data warehouse sits on the spectrum between traditional and cutting edge. To help, we collaborated with TDWI to offer the data warehouse maturity assessment.

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.

BigQuery is a fast, highly scalable, cost-effective, and fully-managed enterprise data warehouse for analytics at any scale. As BigQuery has grown in popularity, one question that often arises is how to copy tables across locations in an efficient and scalable manner. BigQuery has some limitations for data management, one being that the destination dataset must reside in the same location as the source dataset that contains the table being copied.

When working with tables in data warehouse environments, it is fairly common to come across a situation in which you need to generate surrogate keys. A surrogate key is a system-generated identifier that uniquely identifies a record within a table. Why do we need to use surrogate keys? Quite simply: contrary to natural keys, they persist over time (i.e. they are not tied to any business meaning) and they allow for unlimited values.

In this blog post, we’ll share more on how we built the BigQuery Ethereum Public Dataset that contains the Ethereum blockchain data. This includes the primary data structures—blocks, transactions—as well as high-value data derivatives—token transfers, smart contract method descriptions.

Ethereum and other cryptocurrencies have captured the imagination of technologists, financiers, and economists. Digital currencies are only one application of the underlying blockchain technology. Earlier this year, we made the Bitcoin dataset publicly available for analysis in Google BigQuery. Today we’re making the Ethereum dataset available.