Analytics

Heroku's Postgres open-source database is the most effective service for developers who want to build engaging apps. With all of its features and Heroku's experience managing databases, it guarantees security and compliance with GDPR.

Data processors handle an abundance of data — including personal information about individuals. As the collection and use of data become more widespread, governments continue to enact laws that protect personally identifiable information (PII). Failing to comply with such laws means risking serious fines and penalties, and damaging public trust. Masking PII through pseudonymization is one way to protect it.

Last week in our BigQuery Reference Guide series, we spoke about the BigQuery resource hierarchy - specifically digging into project and dataset structures. This week, we’re going one level deeper and talking through some of the resources within datasets. In this post, we’ll talk through the different types of tables available inside of BigQuery, and how to leverage routines for data transformation.

9.6 is focused on Yellowfin features that enhance the way our customers build, design and embed stunning analytical content, which include data storytelling, augmented analytics, actionable dashboards — and provide a high ease-of-use experience. As always, you can read the full list of updates in our release notes page, and view our release highlights video below to see the new enhancements demonstrated.

Marketing is more complex if you're engaged in the healthcare field. Whether you work with patients or market to consumers interested in healthcare products, it's important to understand HIPAA guidelines. This article explains the basics of HIPAA Privacy and Security Rules, and how this legislation affects your marketing strategy.

Companies need to store and retrieve data for daily processes and other needs. Ideally, the data storage atmosphere using ELT is a data lake or an organized storage system that makes logging, storing, and retrieving data efficient and easy.

When it comes to anomaly detection, one of the key challenges that many organizations face is that it can be difficult to know how to define what an anomaly is. How do you define and anticipate unusual network intrusions, manufacturing defects, or insurance fraud? If you have labeled data with known anomalies, then you can choose from a variety of supervised machine learning model types that are already supported in BigQuery ML.