Imagine you’re going through immigration at the airport. The immigration officer says, “I don’t need your passport because I trust that you are who you claim to be.” Wait, what? That would never happen, right? That’s because trust is exploitable. Sooner or later, somebody will try to lie about who they are, and thus a criminal could enter the country. That’s why countries must enforce some form of identity, like a passport, to certify travelers are who they claim.

We are living in a time where a difference of a mere couple of seconds can make you lose your business to another company with a faster, more easily accessible web application. In such a highly competitive space, it is important to squeeze out the maximum amount of performance from your application’s software stack and hardware infrastructure.

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.

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.

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.

In order to gain an advantage over their competitors and improve customer experience, organizations of all sizes and industries are using new technologies to undergo digital transformation. According to IT research and advisory firm Gartner, 87 percent of senior business leaders say that digital modernization of their company is a top priority. An emerging theme of many digital transformations relates to ‘application integration’ – but why do these have such a close association?

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.

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.

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.