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How 3rd Party Tools Nearly Killed Performance (and Culture) at Adidas

by Manuel Pais on  Jan 20, 2017

How the shoe and clothes giant manufacturer's IT tamed an out-of-control proliferation of third party tools in their global websites which was killing performance. Furthermore, this led to a blame culture setting in between business and IT. A new third party governance process focusing on performance data and user experience validation was key to stop the bleeding.

Mathieu Ripert on Instacart's Machine Learning Optimizations

by Alexandre Rodrigues on  Jan 05, 2017

Instacart is an online delivery service for groceries under one hour. Customers order the items on the website or using the mobile app, and a group of Instacart’s shoppers go to local stores, purchase the items and deliver them to the customer. InfoQ interviewed Mathieu Ripert, data scientist at Instacart, to find out how machine learning is leveraged to guarantee a better customer experience.

AFK-MC² Algorithm Speeds up k-Means Clustering Algorithm Seeding

by Alexandre Rodrigues on  Dec 23, 2016

“Fast and Probably Good Seedings for k-Means” by Olivier Bachem et al. was presented on 2016’s Neural Information Processing Systems (NIPS) conference and describes AFK-MC2, an alternative method to generate initial seedings for k-Means clustering algorithm that is several orders of magnitude faster than the state of art method k-Means++.

Julien Le Dem on the Future of Column-Oriented Data Processing with Apache Arrow

by Alexandre Rodrigues on  Dec 08, 2016 1

Julien Le Dem, the PMC chair of the Apache Arrow project, presented on Data Eng Conf NY on the future of column-oriented data processing. Apache Arrow is an open-source standard for columnar in-memory execution. InfoQ interviewed Le Dem to find out the differences between Arrow and Parquet.

Microsoft Releases Data Science Tools for Interactive Data Exploration and Modeling

by Srini Penchikala on  Nov 07, 2016

Microsoft recently released two new data science tools for interactive data exploration: modeling and reporting. These tools can be reused by data science teams with data specific tasks in their projects. The goal is to ensure consistency and completeness of data science tasks across different projects in the organization.

Microservices and Stream Processing Architecture at Zalando Using Apache Flink

by Srini Penchikala on  Oct 31, 2016 1

Javier Lopez and Mihail Vieru spoke at Reactive Summit 2016 Conference about cloud-based data integration and distribution platform used for stream processing in business intelligence use cases. Their solution is based on technologies such as Flink, Kafka and Elasticsearch.

Stream Processing and Lambda Architecture Challenges

by Alexandre Rodrigues on  Oct 19, 2016 4

Lambda architecture has been a popular solution that combines batch and stream processing. Kartik Paramasivam at LinkedIn wrote about how his team addressed stream processing and Lambda architecture challenges using Apache Samza for data processing. The challenges described are the late arrival of events and the processing of duplicated messages.

Reactive Summit 2016 Conference: Reactive Microservices and Staging Data Pipelines

by Srini Penchikala on  Oct 08, 2016

Reactive microservices, data center scale operating system (DCOS), and staging reactive data pipelines were the highlighted topics at Reactive Summit 2016 Conference held this week. InfoQ team attended the conference and this post is a summary of the first day's events at the conference.

Data Streaming Architecture with Apache Flink

by Srini Penchikala on  Jun 09, 2016

Jamie Grier recently spoke at OSCON 2016 Conference about data streaming architecture using Apache Flink. He talked about the building blocks of data streaming applications and stateful stream processing with code examples of Flink applications and monitoring.

Precision Medicine Modeling Demonstration with Spark on EMR, ADAM, and the 1000 Genomes Project

by Dylan Raithel on  May 19, 2016

AWS engineers Christopher Crosbie and Ujjwal Ratan detail using Spark on EMR for precision medicine data analysis on the ADAM platform with data from the 1000 genomes project.

Elephant in the Cloud - Hadoop as a Service

by Srini Penchikala on  May 02, 2016 2

Hadoop and other big data technologies revolutionized the way organizations run data analytics but the organizations are still facing challenges with operating costs of using these technologies for on-premise data processing. Ashish Thusoo recently spoke at Enterprise Data World Conference about Hadoop as a service offering that helps organizations bridge the gaps with these capabilities.

Google Cloud Machine Learning and Tensor Flow Alpha Release

by Dylan Raithel on  Apr 18, 2016

Late last month Google released an alpha version of their TensorFlow (TF) integrated cloud machine learning service as a response to a growing need to make their Tensor Flow library to run at scale on the Google Cloud Platform (GCP). Google describes several new feature sets around making TF usage scale by integrating several pieces of the GCP like Dataproc, a managed Hadoop and Spark service.

Microsoft Releases Power BI Embedded Preview

by Kent Weare on  Apr 17, 2016

Recently at the 2016 Build Event in San Francisco, Microsoft announced a change to their Power BI offering. The update comes in the form of giving customers and ISVs with the ability to embed Power BI reports within their own applications. Microsoft is calling this service Power BI Embedded and it is currently in preview.

Funnel Analysis at Twitter for Improving User Engagement

by Srini Penchikala on  Feb 25, 2016

Funnel analysis is used to analyze a sequence of events to help with user engagement on a website or a mobile application. Data Science team at Twitter uses this concept to learn how users interact with user interfaces during sign up or tweeting for improving user engagement with Twitter.

IBM Extends its Cloud Data Analytics Services

by Abel Avram on  Feb 06, 2016

IBM has announced four new data services: Analytics Exchange, Compose Enterprise, Graph, and Predictive Analytics. IBM’s new data services are meant to enable users to analyze their own data or get access to datasets provided by IBM. While some of the services run on Bluemix, for others the data can be deployed on other clouds, including private ones.

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