Analytics

There is an urgent need for banks to be nimble and adaptable in the thick of a multitude of industry challenges, ranging from the maze of regulatory compliance, sophisticated criminal activities, rising customer expectations and competition from traditional banks and new digital entrants. As banks find their bearings in this landscape, what appear to be insurmountable odds are in fact opportunities for growth and competitive differentiation.

In the first part of this series , I outlined the prerequisites for a modern Enterprise Data Platform to enable complex data product strategies that address the needs of multiple target segments and deliver strong profit margins as the data product portfolio expands in scope and complexity: With this article, I will dive into the specific capabilities of the Cloudera Data Platform (CDP) that has helped organizations to meet the aforementioned prerequisite capabilities and fulfill a successful data prod

Over the last year, perhaps unsurprisingly, increasing numbers of companies have made the jump to the cloud. It’s become a necessary move for so many businesses. But, as I discussed with Joe DosSantos on the latest episode of Data Brilliant – the rewards are abundant, but the journey is not always straight forward.

Most companies have had to adjust to the big data push. Some have learned to fully leverage data to get a comprehensive view of their business and make long-term plans for their processes. However, it can be a long way from there to fueling minute-by-minute processes with quality data. Operational analytics allows your company to be at its most effective on a real-time basis. How does operational analytics (also called continuous analytics) offer an advantage to your company and how do you implement it?

Distributed systems have unlocked high performance at a large scale and low latency. You can run your applications worldwide from the comfort of your Amazon Web Services (AWS) platform in California, but the user adding an item to their shopping cart in Japan will not notice any delay or system faults. However, distributed systems - and specifically distributed database systems - also malfunction.

In the modern age, everything runs on the cloud. The majority of modern applications are written with cloud technologies - they use public cloud providers for DNS, distributed caching, and distributed data stores. Cloud solutions are so popular among engineers because of their many advantages: But distributed systems are not impervious to breaking. Foursquare’s example is testimony that even the great and mighty experience failure within distributed systems.

In the most recent season of BigQuery Spotlight, we discussed key concepts like the BigQuery Resource hierarchy, query processing, and the reservation model. This blog focuses on extending those concepts to operationalize workload management for various scenarios.

Interested in transformations? Avoid these pitfalls and embrace these best practices to get the most out of data transformation.

So far in this series, we’ve been focused on generic concepts and console-based workflows. However, when you’re working with huge amounts of data or surfacing information to lots of different stakeholders, leveraging BigQuery programmatically becomes essential. In today’s post, we’re going to take a tour of BigQuery’s API landscape - so you can better understand what each API does and what types of workflows you can automate with it.

If the COVID-19 pandemic has taught us anything, it is that speed and intelligence are of the essence when it comes to making business decisions. Organizations must find ways of keeping ahead of competitors and disruptions by continually leveraging data to make smart decisions. The problem? Data may be everywhere, but it’s not always available in a form that businesses can use to generate analytics in real time.