Analytics

The importance of effective data analytics within an organization is widely accepted by business leaders at this point. With use cases for data analysis spanning every department—from IT management, financial planning, marketing analytics, and so on—the right data analytics tools can have a significant impact on a company’s profitability and growth.

To understand the value of logs—those many digital records of hardware and software events—picture a big puzzle. You put all the pieces together to make sense of them. Every day the modern enterprise generates billions of logs, each capturing a user log-in, application record change, network service interruption—as well as the messages these entities send to one another.

Productboard is a customer-driven product management system, which enables companies to leverage customer feedback and data insights to fuel innovation, and ultimately, deliver products that customers will love. For a few years, the company worked with data consulting agencies, but things weren't working out. Productboard was using Keboola, but they weren't sure how to get the most out of it.

Democratization of data within an organization is essential to help users derive innovative insights for growth. In a big data environment, traceability of where the data in the data warehouse originated and how it flows through a business is critical. This traceability information is called data lineage. Being able to track, manage, and view data lineage helps you to simplify tracking data errors, forensics, and data dependency identification.

Cloudera Operational Database is now available in three different form-factors in Cloudera Data Platform (CDP). If you are new to Cloudera Operational Database, see this blog post. And, check out the documentation here. In this blog post, we’ll look at both Apache HBase and Apache Phoenix concepts relevant to developing applications for Cloudera Operational Database.

Five differences between Stitch, Talend, and Xplenty: Organizations store data in many destinations, making that data difficult to analyze. Legacy systems, SaaS locations, in-house databases, apps, you name it — by storing data in all kinds of places, companies can complicate data analytics considerably. Storing data in a warehouse or a lake makes more sense.

Today’s customers have a growing need for a faster end to end data ingestion to meet the expected speed of insights and overall business demand. This ‘need for speed’ drives a rethink on building a more modern data warehouse solution, one that balances speed with platform cost management, performance, and reliability.