The telecommunications industry continues to develop hybrid data architectures to support data workload virtualization and cloud migration. However, while the promise of the cloud remains essential—not just for data workloads but also for network virtualisation and B2B offerings—the sheer volume and scale of data in the industry require careful management of the “journey to the cloud.”
In this post I will demonstrate how Kafka Connect is integrated in the Cloudera Data Platform (CDP), allowing users to manage and monitor their connectors in Streams Messaging Manager while also touching on security features such as role-based access control and sensitive information handling. If you are a developer moving data in or out of Kafka, an administrator, or a security expert this post is for you. But before I introduce the nitty-gritty first let’s start with the basics.
Like all of our customers, Cloudera depends on the Cloudera Data Platform (CDP) to manage our day-to-day analytics and operational insights. Many aspects of our business live within this modern data architecture, providing all Clouderans the ability to ask, and answer, important questions for the business. Clouderans continuously push for improvements in the system, with the goal of driving up confidence in the data.
A recent VentureBeat article , “4 AI trends: It’s all about scale in 2022 (so far),” highlighted the importance of scalability. I recommend you read the entire piece, but to me the key takeaway – AI at scale isn’t magic, it’s data – is reminiscent of the 1992 presidential election, when political consultant James Carville succinctly summarized the key to winning – “it’s the economy”.
dbt allows data teams to produce trusted data sets for reporting, ML modeling, and operational workflows using SQL, with a simple workflow that follows software engineering best practices like modularity, portability, and continuous integration/continuous development (CI/CD).
Digital transformation. Everyone has their own ideas about what digital transformation means, so I decided to look up a few definitions.
This blog post will provide guidance to administrators currently using or interested in using Kafka nodes to maintain cluster changes as they scale up or down to balance performance and cloud costs in production deployments. Kafka brokers contained within host groups enable the administrators to more easily add and remove nodes. This creates flexibility to handle real-time data feed volumes as they fluctuate.
Tell us if this sounds familiar. You’ve found an awesome data set that you think will allow you to train a machine learning (ML) model that will accomplish the project goals; the only problem is the data is too big to fit in the compute environment that you’re using. In the day and age of “big data,” most might think this issue is trivial, but like anything in the world of data science things are hardly ever as straightforward as they seem.
