Analytics

Businesses rely on personal data to better tailor their approach to customer relations and streamline a marketing strategy suited to their target audience. In today's business climate, holding onto the personally identifiable information (PII) of specific individuals for various marketing and customer services purposes requires secure storage and extraction. PII pseudonymization is the latest and greatest method for protecting personal data.

June is the month which holds the summer solstice, and (at least in the northern hemisphere) we enjoy the longest days of sunshine out of the entire year. Just as the sun is making its longest trips across the sky, the BigQuery team is delighted to announce our next set of user-friendly SQL features.

Data pipeline performance hinges on several important techniques, including algorithmic optimization and parallelization.

In today’s digital age, keeping up with market trends is exactly what a business has to do to stay ahead. Creating a solid online brand image plays a key role in this task, and to do it, dedicated SEO efforts go a long way. Crafting targeted keywords that can direct traffic to your webpages can work wonders in capturing a widespread customer base. Now what if we were to tell you that instead of doing everything manually, you could rely on an automated tool to take care of things?

With news of a devastating data breach constantly in the headlines, you need to take proactive steps to safeguard the personally identifiable information (PII) that your organization stores and processes. Along with techniques such as PII masking, PII pseudonymization is one of the most popular and practical ways to protect sensitive data. But what is PII pseudonymization, exactly, and how can you pseudonymize PII? We’ll answer these questions and more in this article.

Today we’re announcing preview availability of a new public dataset for Google Trends. For the first time we’re bringing Google-owned Search data into Google Cloud Datasets for convenient analysis in BigQuery, or through your favorite business intelligence tools.

Google’s cloud data warehouse, BigQuery, has enabled organizations around the world to accelerate their digital transformation and empower their data analysts to unlock actionable insights from their data. Using BigQuery ML, data analysts are able to create sophisticated machine learning models with just SQL and uncover predictive insights from their data much faster.

Many Cloudera customers are making the transition from being completely on-prem to cloud by either backing up their data in the cloud, or running multi-functional analytics on CDP Public cloud in AWS or Azure. The Replication Manager service facilitates both disaster recovery and data migration across different environments.