Systems | Development | Analytics | API | Testing

COVID-19, the Data Deluge and Optimizing Splunk for Time and Cost

The new normal has changed the way we work and the way we conduct business. More and more employees are working from home, customers are shopping online, and everyone’s phone is still attached to their ears. Bottom line: everything we’re doing in business and in our personal lives is leaving a digital trail. In fact, now devices are getting in the game and creating more data than people, 277 times more, according to Cisco.

What Grocers and CPG Companies Need to Know About Post-Pandemic Shopping

The COVID-19 pandemic has changed nearly everything. It’s affected nearly all Americans, and as such, it’s impacted every organization they interact with, both B2C and B2B. One industry that has had its operations turned upside down is the grocery industry. Grocery stores and their consumer packaged goods (CPG) suppliers and partners had to improvise and adapt nearly overnight to accommodate the changing demands of shoppers.

Qlik vs. Power BI - Free Doesn't Mean Freedom for Your Data or Users

Every professional has used Microsoft products in their work life. The ubiquity of Microsoft Office in the enterprise is one of the main reasons many analytics users are familiar with Power BI, given how the company bundles its business intelligence software with other licenses in the same way Excel and PowerPoint comes with Office.

Validating Jet Engine Predictive Models Using Cloudera Machine Learning

In this video, we’ll go over how to use Cloudera Machine Learning (CML) to validate a complex predictive model. Using a publicly available NASA dataset that simulates how jet engines degrade over time, we’ll use machine learning concepts in a cloud environment to go from simulation data to a cost benefit analysis in just a few steps. We’ll also show how we can run experiments to track specific metrics from many different scenarios that our predictive model could possibly be implemented in.

Redivis makes research data accessible, experiences collaborative with BigQuery

Understanding the data we collect is essential—it allows us to identify trends and uncover answers about our world. However, stories in our data frequently go untold. Large datasets are hard to share between research communities due to their size, security restraints, and complexity. Even if these datasets are accessible to users, the tools needed to query them often require deep technical knowledge.

Smile with new user-friendly SQL capabilities in BigQuery

October happens to be the month to celebrate World Smile Day when Harvey Ball, the inventor of the smiley face declared this day as such to give people a reason to smile. This month, BigQuery users have a lot of new reasons to smile about with the release of new user-friendly SQL capabilities now generally available.

Using Cloudera Machine Learning to Build a Predictive Maintenance Model for Jet Engines

Running a large commercial airline requires the complex management of critical components, including fuel futures contracts, aircraft maintenance and customer expectations. Airlines, in just the U.S. alone, average about 45,000 daily flights and transporting over 10 million passengers a year (source: FAA). Airlines typically operate on very thin margins, and any schedule delay immediately angers or frustrates customers.

Apache Spark on Kubernetes: How Apache YuniKorn (Incubating) helps

Apache Spark unifies batch processing, real-time processing, stream analytics, machine learning, and interactive query in one-platform. While Apache Spark provides a lot of capabilities to support diversified use cases, it comes with additional complexity and high maintenance costs for cluster administrators. Let’s look at some of the high-level requirements for the underlying resource orchestrator to empower Spark as a one-platform.

How Software Companies Can Build Scalable Embedded Analytics Apps with Snowflake

Customers of B2B companies rely on insights from applications to grow their business, secure their infrastructure, make business decisions, and more. Unless your B2B company offers a rich set of analytics within its product, your customers likely demand nightly data dumps from your application so they can analyze application data with their own BI stack.