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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.

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.

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?

Two data experts share their journeys toward better customer engagement, fueled by faster access to more data.

Starting this week, we’re adding new content to the BigQuery Spotlight Youtube series. Throughout the summer we’ll be adding new videos and blog posts focused on helping new BigQuery architects and administrators master the fundamentals. You can find complimentary material for the topics discussed in the official BigQuery documentation.

Knowing best practices for Amazon Web Services (AWS) data pipelines is essential for modern companies handling large datasets and requiring secure ETL (Extract, Transform, Load) processes. In this article, we discuss AWS data pipeline best practices to ensure top performance and streamlined processes — without complications that can impede the execution of data transfer.

When it was revealed in September 2017, the massive Equifax data breach made international headlines. As one of the three major credit agencies in the United States, Equifax is responsible for processing personally identifiable information (PII) such as individuals’ names, addresses, and social security numbers. According to Equifax, 143 million people were affected by the data breach, making it one of the biggest cybersecurity disasters in history.

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.