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Deep Learning at Gilt

by Alex Giamas on  Feb 19, 2017

Deep Learning is a rapidly evolving subfield of Machine Learning originating from Neural Networks. Recent algorithmic advances and utilization of GPU parallelization have resulted in Deep Learning based algorithms mastering the game of Go as well as several practical applications. The fashion industry is one of the target sectors for Deep Learning. Gilt is using Deep Learning for real world apps

Microsoft AirSim, a Simulator for Drones and Robots

by Abel Avram on  Feb 16, 2017

Microsoft has developed and open sourced AirSim, a tool that can be used to simulate the flight of drones around the world. The simulator is built on the Unreal Engine and Microsoft will soon add support for robots and other types of vehicles.

Apache Flink 1.2 Released with Dynamic Rescaling, Security and Queryable State

by Alexandre Rodrigues on  Feb 15, 2017

Apache Flink 1.2 was announced and features dynamic rescaling, security, queryable state, and more. The release resolved 650 issues, maintains compatibility with all public APIs and ships with Apache Kafka 0.10 and Apache Mesos support. Flink’s dynamic rescaling allows one to change the parallelism of a streaming job or of an operator within the job.

MindMeld’s Guide to Building Conversational Apps

by Abel Avram on  Feb 03, 2017

MindMeld, a conversational AI company, has published The Conversational AI Playbook, a guide outlining the challenges and the steps to be made to create conversational applications.

Apache HBase 1.3 Ships with Multiple Performance Improvements

by Alexandre Rodrigues on  Jan 30, 2017

Apache HBase 1.3.0 was released mid-January 2017 and ships with support for date-based tiered compaction and improvements in multiple areas, like write-ahead log (WAL), and a new RPC scheduler, among others. The release includes almost 1,700 resolved issues in total.

Apache Eagle, Originally from eBay, Graduates to top-level project

by Alexandre Rodrigues on  Jan 24, 2017

Apache Eagle, an open-source solution for identifying security and performance issues on big data platforms, graduates to Apache top level project on January 10, 2017. Firstly open-sourced by eBay on October 2015, Eagle was created to instantly detect access to sensitive data or malicious activities and, to take actions in a timely fashion.

Improving Azure SQL Database Performance Using In-Memory Technologies

by Kent Weare on  Jan 21, 2017 4

In late 2016, Microsoft announced the general availability of Azure SQL Database In-Memory technologies. In-Memory processing is only available in Azure Premium database tiers and provides performance improvements for On-line Analytical Processing (OLTP), Clustered Columnstore Indexes and Non-clustered Columnstore Indexes for Hybrid Transactional and Analytical Processing (HTAP) scenarios.

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.

Google BigQuery Adds New Public Datasets

by Alex Giamas on  Jan 05, 2017

Stack Overflow recently announced making its dataset available through Google’s BigQuery. Using regular SQL statements, developers can query the full set of Stack Overflow data including posts, votes, tags, and badges. In this article we explore datasets that are available through Google's BigQuery platform.

Julien Nioche on StormCrawler, Open-Source Crawler Pipelines Backed by Apache Storm

by Alexandre Rodrigues on  Dec 15, 2016

Julien Nioche, director of DigitalPebble, PMC member and committer of the Apache Nutch web crawler project, talks about StormCrawler, a collection of reusable components to build distributed web crawlers based on the streaming framework Apache Storm. InfoQ interviewed Nioche, main contributor of the project, to find out more about StormCrawler and how it compares to other similar technologies.

Facebook's Comparison of Apache Giraph and Spark GraphX for Graph Data Processing

by Srini Penchikala on  Dec 09, 2016

A Facebook team has recently published a comparison of the performance of their existing Giraph-based graph processing system with the newer GraphX which is part of the popular Spark framework. Their conclusion is that GraphX is neither sufficiently scalable or performant to support their graph processing workloads.

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.

Couchbase 4.6 Developer Preview Released, Adds Real-Time Connectors for Apache Spark 2.0 and Kafka

by Alexandre Rodrigues on  Nov 28, 2016

Couchbase 4.6 Developer Preview features full text search improvements, cross data center replication with globally-ordered conflict resolution and connectors for real-time analytics technologies: one for Spark 2.0 and the other for Kafka.

Spark Summit EU Highlights: TensorFlow, Structured Streaming and GPU Hardware Acceleration

by Alexandre Rodrigues on  Nov 13, 2016

Apache Spark integration with deep learning library TensorFlow, online learning using Structured Streaming and GPU hardware acceleration were the highlights of Spark Summit EU 2016 held last week in Brussels.

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.

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