LinkedIn recently open sourced Cubert, its High Performance Computation Engine for Complex Big Data Analytics. Cubert is a framework written for analysts and data scientists in mind.Developed completely in Java and expressed as a scripting language, Cubert is designed for complex joins and aggregations that frequently arise in the reporting world.
Microsoft recently announced new machine learning capabilities for Microsoft Azure platform. Developers can also create their own web services and publish them to Azure Marketplace. Microsoft also announced availability of Apache Storm for Azure. Azure Stream Analytics, Data Factory and Event Hubs for Azure were all announced in the past few weeks by Microsoft. In this article we explore moreabout
Usage of data generated by drones is going to add a new horizon in data storage and processing. Kirk Borne, professor at George Mason University recently talked about the challenge of processing, storing and transferring this data.
With Raising the game - The IBM Business Tech Trends Study (PDF) IBM has evaluated the current adoption landscape of 4 key technologies in the enterprise: Big Data & Analytics, Cloud, Mobile and Social, comparing today’s adoption with 2012’s and Pacesetters against Dabblers.
Ayasdi announced last month a partnership with Cloudera, the biggest distributor of Apache Hadoop. The partnership will ensure the compatibility of their solution with Cloudera Enterprise 5, the latest version of Cloudera’s big data platform based on Apache Hadoop.
Splunk, a company specializing in searching, monitoring, and analyzing machine-generated data, has announced the release of Hunk 6.1. Hunk provides an analytics platform for big data. The new release also provides streaming resource libraries to connect Hunk to any NoSQL data store, such as Apache Cassandra, MongoDB, and Neo4j.
Recently, Spark graduated from the Apache incubator. Spark claims up to 100x speed improvements over Apache Hadoop over in-memory datasets and gracefully falling back to 10x speed improvement for on-disk performance. Based on Scala, it can run SQL queries and be used directly in R. It provides Machine Learning, Graph database capabilities and other further discussed in the article.
Hadoop is definitely the platform of choice for Big Data analysis and computation. While data Volume, Variety and Velocity increases, Hadoop as a batch processing framework cannot cope with the requirement for real time analytics. Spark, Storm and the Lambda Architecture can help bridge the gap between batch and event based processing.
Arun Kejariwal, from Twitter, talked at Velocity Conf London last month about forecasting algorithms used at Twitter to proactively predict system resource needs as well as business metrics such as number of users or tweets. Given the dynamic nature of their data stream, they found that a refined ARIMA model works well once data is cleansed, including removal of outliers.
Jeff Magnusson from Netflix team gave a presentation at QCon SF 2013 Conference about their Data Platform as a Service. Following up to this presentation, we will look at the technology stack and how it helps Netflix to tackle important business decisions.
The lean startup is a “scientific approach to creating and managing startups” as Eric Ries describes in the lean startup principles. It uses “hard things” like validated learning with experiments and data. But what the “soft things” like intuition, guts, feelings, passion, inspiration and fun, do they also matter when you are developing new products?
Thoughtworks recently released a new installment of their technology radar highlighting techniques enabling infrastructure as code, perimeterless enterprises, applying proven practices to areas without, and lightweight analytics.
DNN Social enables customers to interact with the site interface via blogs, discussion forums, FAQ's and includes features such as gamification, analytics, ideation and activity stream, which enables site administrators to gauge the effectiveness of interaction.
ThoughtWorks's latest "Technology Radar" focuses on mobile, accessible analytics, simple architectures, reproducible environments, and data persistence done right.
Precog has recently announced a Big Data warehousing and analysis service which takes care of the data capture, storage, transformation, analysis and visualization process and the infrastructure on which it runs, but leaving open various access points throughout the service via RESTful APIs enabling developers and data scientists to control the entire process.