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.
As enterprises seek to accelerate the process of getting insights from their data, they face numerous sources of friction. Data sprawl across silos, diverse formats, the explosion of data volumes, and the fact that data is spread across many data centers and clouds and processed by many disparate tools, all act to slow the progress.
In digital transformation projects, it’s easy to imagine the benefits of cloud, hybrid, artificial intelligence (AI), and machine learning (ML) models. The hard part is to turn aspiration into reality by creating an organization that is truly data-driven.
The manufacturing industry, like any other industry, is not immune to data challenges. Sourcing data, wrangling it and ensuring it’s being used in a governed, standardized way are not uncommon problems. Particularly in manufacturing, issues surface with inventory management, within the supply chain and with logistics.
When we announced the GA of Cloudera Data Engineering back in September of last year, a key vision we had was to simplify the automation of data transformation pipelines at scale. By leveraging Spark on Kubernetes as the foundation along with a first class job management API many of our customers have been able to quickly deploy, monitor and manage the life cycle of their spark jobs with ease. In addition, we allowed users to automate their jobs based on a time-based schedule.
At Airflow Summit 2021, Unravel’s co-founder and CTO, Shivnath Babu, led a talk titled Data Pipeline HealthCheck for Correctness, Performance & Cost Efficiency. This story, along with the slides and videos included in it, come from the presentation.
For more than 20 years, OpenTable has connected foodies and novice diners with the restaurants they love. But how does its technology work on the back end? To make a long story short: data. Beyond the app and website, OpenTable provides restaurants with software that manages their floor plans, phone reservations, walk-ins, shift scheduling, turn times, and more.
Are you ready to turbo-charge your data flows on the cloud for maximum speed and efficiency? We are excited to announce the general availability of Cloudera DataFlow for the Public Cloud (CDF-PC) – a brand new experience on the Cloudera Data Platform (CDP) to address some of the key operational and monitoring challenges of standard Apache NiFi clusters that are overloaded with high-performant flows.
We just announced Cloudera DataFlow for the Public Cloud (CDF-PC), the first cloud-native runtime for Apache NiFi data flows. CDF-PC enables Apache NiFi users to run their existing data flows on a managed, auto-scaling platform with a streamlined way to deploy NiFi data flows and a central monitoring dashboard making it easier than ever before to operate NiFi data flows at scale in the public cloud.
