Systems | Development | Analytics | API | Testing

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A Dose Of Data Science Demystification

Join two data engineers and analysts in pulling back the curtain on real customer engagements, showing how to select and implement advanced data science and analytic techniques. In this session we will discuss our implementation of two data science models at a large agricultural products manufacturer: a propensity-to-buy model and a recommendation engine. We will discuss how each of these models works and how they were implemented for our client.

Make Your Data Fabrics Work Better

To gain the full benefits of the DataOps strategy, your data lakes must change. The traditional concept of bringing all data to one place, whether on-premises or in the cloud, raises questions of timing, scale, organization and budget. The answer? Data fabric. It replaces traditional data lake organization concepts with a more flexible and economical architecture. In this session, we'll define what a data fabric is, show you how you can begin organizing around the concept, and discuss how to align it to your business objectives.

How to add business logic with content-based router

To explain the usage of our content-based router that enables you to add some business logic to your integration flow, let’s imagine that I have received a Google Spreadsheet file with unsorted leads data, for example, from downloading a coupon on a website. To convert these leads to customers, I’d like to offer them something of interest with a specifically targeted campaign based on the country of residence. For that, I need to filter and split the list.

Demand for Data Grows in Agriculture

Agriculture (Ag) is the oldest and largest industrial vertical in the world, and its importance continues to grow as it becomes more challenging for people to access healthy and fresh food. A recent Agriculture Analytics Market report, released by Markets and Markets, estimates that by 2023, the global agriculture analytics market size will grow from 585 million to 1.2 billion dollars as demands for real-time data analysis and improved operations increase.

How to Create a Python Stack

All programming languages provide efficient data structures that allow you to logically or mathematically organize and model your data. Most of us are familiar with simpler data structures like lists (or arrays) and dictionaries (or associative arrays), but these basic array-based data structures act more as generic solutions to your programming needs and aren’t really optimized for performance on custom implementations. There’s much more than programming languages bring to the table.

A pivotal paradox: 6 lessons learned managing a fully remote team

A mere few months ago the majority of the world was forced to change drastically, including the move into a ‘fully remote’ mode of office work. As reality was bearing down upon us, tech managers and CEOs everywhere were huddled together trying to figure out how to not only make it work, but work well.

Overview of the Operational Database performance in CDP

This article gives you an overview of Cloudera’s Operational Database (OpDB) performance optimization techniques. Cloudera’s Operational Database can support high-speed transactions of up to 185K/second per table and a high of 440K/second per table. On average, the recorded transaction speed is about 100K-300K/second per node. This article provides you an overview of how you can optimize your OpDB deployment in either Cloudera Data Platform (CDP) Public Cloud or Data Center.

Tech Tip: Pointing Your Automated Tests to Sauce

So you’ve realized the benefits of test automation. Through your own research, or perhaps a small proof of concept, you’ve realized removing once-manual quality processes can accelerate release cycles and improve your user experience. You’ve built a small suite of tests, and the benefits are real. The next step in your journey, you realize, is to achieve the real value of automation, which means running it continuously and at scale.