Systems | Development | Analytics | API | Testing

The Key Metrics That Fintech Product Managers Can't Live Without

If each product is a world in its own, each industry in which that product -or service, for that matter- is deployed, is a universe. A seemingly chaotic universe full of data coming from every direction and angle that you, the product manager, need to catch, analyze, and funnel into your every day. If this does not sound easy, it is because it is not!

Cloud Data Retention & Analysis: Unlocking the Power of Your Data

Enterprise data growth is accelerating rapidly in 2021, challenging organizations to adopt cloud data retention strategies that maximize the value of data and fulfill compliance needs while minimizing costs. To meet this challenge, organizations are adopting or refining their cloud data retention strategies. In this blog post, we’ll take a closer look at the state of data retention and analytics in the cloud.

Sample applications for Cloudera Operational Database

Cloudera Operational Database is an operational database-as-a-service that brings ease of use and flexibility to Apache HBase. Cloudera Operational Database enables developers to quickly build future-proof applications that are architected to handle data evolution. In the previous blog posts, we looked at application development concepts and how Cloudera Operational Database (COD) interacts with other CDP services.

New in BigQuery BI Engine: faster insights across popular BI tools

Business analysts working with larger and larger data sets are finding traditional BI methods can't keep up with their need for speed. BigQuery BI Engine is designed to meet this need by accelerating the most popular dashboards and reports that connect to BigQuery. With the freshest data available, your analysts can identify trends faster, reduce risk, match the pace of customer demand, even improve operational efficiency in an ever-changing business climate.

What Is a Data Stack?

These days, there are two kinds of businesses: data-driven organizations; and companies that are about to go bust. And often, the only difference is the data stack. Data quality is an existential issue—to survive, you need a fast, reliable flow of information. The data stack is the entire collection of technologies that make this possible. Let's take a look at how any company can assemble a data stack that's ready for the future.