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Automating data pipelines with BigQuery and Fivetran

Companies from every industry vertical, including finance, retail, logistics, and others, all share a common horizontal analytics challenge: How do they best understand the market for their products? Solving this problem requires companies to conduct a detailed marketing, sales, and finance analysis to understand their place within the larger market. These analyses are designed to unlock insights in a company's data that can help businesses run more efficiently.

BigQuery explained: An overview

Google BigQuery was released to general availability in 2011 and has since been positioned as a unique analytics data warehousing service. Its serverless architecture allows it to operate at scale and speed to provide incredibly fast SQL analytics over large datasets. Since its inception, numerous features and improvements have been made to improve performance, security, reliability, and making it easier for users to discover insights.

Accelerating Mayo Clinic's data platform with BigQuery and Variant Transforms

Genomic data is some of the most complex and vital data that our customers and strategic partners like Mayo Clinic work with. Many of them want to work with genomic variant data, which is the set of differences between a given sample and a reference genome, in order to diagnose patients and discover new treatments. Each sample’s variants are usually stored as a Variant Call Format file, or VCF, but files aren’t a great way to do analytics and machine learning on these data.

BigQuery now offers industry-leading uptime SLA of 99.99%

More than ever, businesses are making real-time, data-driven decisions based on information stored in their data warehouses. Today’s data warehouse requires continuous uptime as analytics demands grow and organizations require rapid access to mission-critical insights. Business disruptions from unplanned downtime can severely impact company sales, reputation, and customer relations.

Better BigQuery pricing flexibility with 100 slots

BigQuery is used by organizations of all sizes, and to meet the diverse needs of our users, BigQuery offers highly flexible pricing options. For enterprise customers, BigQuery’s flat-rate billing model is predictable and gives businesses direct control over cost and performance. We’re now making the flat-rate billing model even more accessible by lowering the minimum size to 100 slots, so you can get started faster and quicker.

Use IAM custom roles to manage access to your BigQuery data warehouse

When migrating a data warehouse to BigQuery, one of the most critical tasks is mapping existing user permissions to equivalent Google Cloud Identity and Access Management (Cloud IAM) permissions and roles. This is especially true for migrating from large enterprise data warehouses like Teradata to BigQuery. The existing Teradata databases commonly contain multiple user-defined roles that combine access permissions and capture common data access patterns.

Bringing multi-cloud analytics to your data with BigQuery Omni

Today, we are introducing BigQuery Omni, a flexible, multi-cloud analytics solution that lets you cost-effectively access and securely analyze data across Google Cloud, Amazon Web Services (AWS), and Azure (coming soon), without leaving the familiar BigQuery user interface (UI). Using standard SQL and the same BigQuery APIs our customers love, you will be able to break down data silos and gain critical business insights from a single pane of glass.

Ask questions to BigQuery and get instant answers through Data QnA

Today, we’re announcing Data QnA, a natural language interface for analytics on BigQuery data, now in private alpha. Data QnA helps enable your business users to get answers to their analytical queries through natural language questions, without burdening business intelligence (BI) teams. This means that a business user like a sales manager can simply ask a question on their company’s dataset, and get results back that same way.

Genomics analysis with Hail, BigQuery, and Dataproc

At Google Cloud, we work with organizations performing large-scale research projects. There are a few solutions we recommend to do this type of work, so that researchers can focus on what they do best—power novel treatments, personalized medicine, and advancements in pharmaceuticals.

Building a genomics analysis architecture with Hail, BigQuery, and Dataproc

We hear from our users in the scientific community that having the right technology foundation is essential. The ability to very quickly create entire clusters of genomics processing, where billing can be stopped once you have the results you need, is a powerful tool. It empowers the scientific community to spend more time doing their research and less time fighting for on-prem cluster time and configuring software.