In order to better serve their customers and users, digital applications and platforms continue to store and use sensitive data such as Personally Identifiable Information (PII), genetic and biometric information, and credit card information. Many organizations that provide data for analytics use cases face evolving regulatory and privacy mandates, ongoing risks from data breaches and data leakage, and a growing need to control data access.

Indonesia’s largest hyperlocal company, Gojek has evolved from a motorcycle ride-hailing service into an on-demand mobile platform, providing a range of services that include transportation, logistics, food delivery, and payments. A total of 2 million driver-partners collectively cover an average distance of 16.5 million kilometers each day, making Gojek Indonesia’s de-facto transportation partner.

Another year, another wave of positive user-led ratings and responses for Yellowfin. It's been a tough few years due to the global pandemic, with much uncertainty across many organizations and industries. But this period has also seen businesses seize more opportunities, and adopt solutions that help them respond faster to customer demands - some with better business intelligence (BI) and analytics solutions, as one example.

In the second blog of the Universal Data Distribution blog series, we explored how Cloudera DataFlow for the Public Cloud (CDF-PC) can help you implement use cases like data lakehouse and data warehouse ingest, cybersecurity, and log optimization, as well as IoT and streaming data collection. A key requirement for these use cases is the ability to not only actively pull data from source systems but to receive data that is being pushed from various sources to the central distribution service.

COVID-19 introduced an unprecedented level of volatility in world markets, and the shockwaves that arrived in its wake exposed a wide chasm between two main types of multinational organizations: Those with agile internal processes and those without. In a world built on complex and globalized supply chains, COVID-19 tested that internal agility, sometimes to breaking point.

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake.

Every large enterprise organization is attempting to accelerate their digital transformation strategies to engage with their customers in a more personalized, relevant, and dynamic way. The ability to perform analytics on data as it is created and collected (a.k.a. real-time data streams) and generate immediate insights for faster decision making provides a competitive edge for organizations.

The terms embedded analytics and business intelligence (BI) are often used interchangeably. Both help you find key insights from complex datasets. But are they really the same thing? Is there any major difference between embedded analytics software and a business intelligence platform? In this post, you will find all the details.