SBS is Australia’s most diverse broadcaster, offering a multi-platform portfolio of TV, radio, and digital services. The company’s data is also diverse and multi-platform, and SBS understands its data is a key element in contributing to improving viewership, customer satisfaction, and loyalty. But what’s the key to using data more effectively? How do you bring together dozens of data sources into a single, central data platform?

CDP Operational Database (COD) is an autonomous transactional database powered by Apache HBase and Apache Phoenix. It is one of the main Data Services that runs on Cloudera Data Platform (CDP) Public Cloud. You can access COD right from your CDP console. With COD, application developers can now leverage the power of HBase and Phoenix without the overheads that are often related to deployment and management.

For members of the military, financial planning is a difficult and ever-changing process. Few businesses understand this better than First Command Financial Services, which focuses on serving the nation’s military families with flexible solutions to home mortgages, car loans, and wealth management.

Dive into these SaaS growth hacks that SaaS experts have used themselves to attract more users and grow their product.

In our previous article, The Future of the Modern Data Stack, we examined the motivations of the modern data stack, its current state, and looked optimistically into the future to see where it is headed. If you’re new to the modern data stack, we highly recommend giving the aforementioned article a read. A question we often get from new adopters of the modern data stack is “What tech should we be looking into?”.

As enterprises develop complex hybrid and multi-cloud environments to support futuristic use cases, a new report published by Big Data Quarterly (BDQ) aims to educate IT decision-makers and practitioners on the most up-to-date solutions and strategies for modern data management in hybrid and multi-cloud environments.

You got through all the hurdles getting the data you need; you worked hard training that model, and you are confident it will work. You just need to run it with a more extensive data set, more memory and maybe GPUs. And then...well. Running your code at scale and in an environment other than yours can be a nightmare. You have probably experienced this or read about it in the ML community. How frustrating is that? All your hard work and nothing to show for it.

I’ve had the pleasure to participate in a few Commercial Lines insurance industry events recently and as a prior Commercial Lines insurer myself, I am thrilled with the progress the industry is making using data and analytics. However, I do not think Commercial Lines insurance gets the credit it deserves for the industry-leading role it has played in analytics. Commercial Lines truly is an “uber industry” with respect to data.

Natural language query (NLQ) is fundamentally a feature designed to democratize self-service analytics, and help make it more pervasive throughout the organization.

Interested in learning about different technologies and methodologies, such as Databricks, Amazon EMR, cloud computing and DataOps? A good place to start is reading articles that give tips, tricks, and best practices for working with these technologies. Here are some of our favorite articles from experts on cloud migration, cloud management, Spark, Databricks, Amazon EMR, and DataOps!