After growing rapidly, the Raddish team knew they needed a solution to help simplify and visualize all the data they were generating.

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!

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.

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.

Sync data incrementally, or watch your pipeline grind to a halt.

Today, we are excited to announce the general availability (GA) of BigQuery materialized views. Materialized views (MV’s) are precomputed views that periodically cache results of a query to provide customers increased performance and efficiency.

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.

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.

There is nothing permanent except change. In a world of turbulent, unpredictable change, we humans are always learning to cope with the unexpected. Hopefully, your machine learning business applications do this every moment, by adapting to fresh data. In a previous post, we discussed the impact of COVID-19 on the data science industry.

Today’s enterprise data science teams have one of the most challenging, yet most important roles to play in your business’s ML strategy. In our current landscape, businesses that have adopted a successful ML strategy are outperforming their competitors by over 9%. The implications of ML on the future of business are clear. However, only 4% of enterprise executives today report seeing success from their ML investment.