Recommendation systems have become a cornerstone of modern life, spanning sectors that include online retail, music and video streaming, and even content publishing. These systems help us navigate the sheer volume of content on the internet, allowing us to discover what’s interesting or important to us. The classic modeling approaches to recommendation systems can be broadly categorized as content-based, as collaborative filtering-based, or as hybrid approaches that combine aspects of the two.

Data and analytics have become second nature to most businesses, but merely having access to the vast volumes of data from these devices will no longer suffice. Leading enterprises realize that the speed of data presents a new frontier for competitive differentiation. It is imperative for organizations to reduce time-to-insights to gain a competitive advantage by responding decisively to competitors, fine-tuning operations, and serving fickle customers.

With billions of industrial IoT (IIOT) devices in place, generating massive volumes of data from “the edge,” the potential for proof of concept success for use cases in the factory can be paralyzing. While the value of this digital revolution, aka Industry 4.0, is clear, realizing the full promise has been slow. Research and real-life experience from Accenture shows that many manufacturers get stuck early on or can’t get beyond proof-of-concept pilots to scale.

The move into any new technology requires planning and coordinated effort to ensure a successful transition. This blog will describe the four paths to move from a legacy platform such as Cloudera CDH or HDP into CDP Public Cloud or CDP Private Cloud. The four paths are In-place Upgrade, Side-car Migration, Rolling Side-car Migration, and Migrate to Public Cloud.

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. This year, we expanded our partnership with NVIDIA, enabling your data teams to dramatically speed up compute processes for data engineering and data science workloads with no code changes using RAPIDS AI.

This article is the second in a multipart series to showcase the power and expressibility of FlinkSQL applied to market data. In case you missed it, part I starts with a simple case of calculating streaming VWAP. Code and data for this series are available on github. Speed matters in financial markets. Whether the goal is to maximize alpha or minimize exposure, financial technologists invest heavily in having the most up-to-date insights on the state of the market and where it is going.

Prior the introduction of CDP Public Cloud, many organizations that wanted to leverage CDH, HDP or any other on-prem Hadoop runtime in the public cloud had to deploy the platform in a lift-and-shift fashion, commonly known as “Hadoop-on-IaaS” or simply the IaaS model.

CDP (Cloudera Data Platform) Private Cloud 1.2 was recently released and builds on the success of CDP Private Cloud Base (see the 7.1.6 release blog). While Private Cloud Base is the ideal modernization of both CDH and HDP deployments for traditional workloads, Private Cloud adds cloud-native capabilities.