You are here:  Home  >  Data Education  >  Big Data News, Articles, & Education  >  Big Data News  >  Current Article

Talend Speeds Apache Spark and Machine Learning Implementations without Coding

By   /  September 12, 2018  /  No Comments

A new press release reports, “Talend, a global leader in cloud integration solutions, today announced it will debut at the Strata Data Conference in New York City a new sandbox that brings sophisticated machine learning technologies to the hands of developers and data engineers so they can easily create smarter data pipelines. With the Talend Big Data and Machine Learning Sandbox, data engineers can start using a step-by-step ‘cookbook’ that includes pre-built machine learning proofs of concept and leverages Apache Spark, Spark Machine Learning Library (MLlib) and Spark Streaming in minutes without coding. Interested parties can download the free sandbox here.”

Ashley Stirrup, CMO at Talend, commented, “There is a massive skills gap where developers and data engineers are struggling to implement big data and machine learning to drive greater business insight… Hand-coding big data integrations often results in inefficiencies when moving to production, such as high maintenance costs, manual integration tasks and re-implementation of machine learning algorithms. With Talend Big Data and Machine Learning Sandbox, teams can get up and running with machine learning in minutes and make the transition from pilot to production more quickly.”

The release adds, “Four pre-built machine learning proofs of concept are included in a step-by-step ‘cookbook‘ with the Talend Big Data and Machine Learning Sandbox. Developers and data engineers can quickly get started with a fully configured, drag-and-drop Spark processing environment and uncover business insights using Talend’s out-of-the-box scenarios, including: (1) Recommendation Engine – Automate a next-best-movie offer using machine learning. (2) Real-Time Risk Assessment Engine – Mitigate risk with real-time loan prediction. (3) IoT Predictive Maintenance – Optimize performance and lifecycle of vending machines using sensor data. (4) Data Warehouse Optimization – Offload data processing to Spark for faster and deeper insight, at a lower cost.”

Read more at Nasdaq.

Photo credit: Talend

You might also like...

Data Literacy and the Colin Powell Rule: From Frontline Field Support to Back Office Operations

Read More →