Aujourd’hui il est difficile de se retrouver dans la jungle d’Hadoop pour les raisons suivantes : - Ce sont des technologies jeunes. - Beaucoup de buzz et de communication de sociétés qui veulent prendre le train Big Data en marche. - Des raccourcis sont souvent... #bigdata #cloudera #hadoop
Several analytic frameworks have been announced in the last year. Among them are inexpensive data-warehousing solutions based on traditional Massively Parallel Processor (MPP) architectures (Redshift), systems which impose MPP-like execution engines on top of Hadoop (Impala, HAWQ) and systems which optimize MapReduce to improve performance on analytical workloads (Shark, Stinger/Tez). This benchmark provides quantitativeand qualitative comparisons of five systems. It is entirely hosted on EC2 and can be reproduced directly from your computer.
Redshift - a hosted MPP database offered by Amazon.com based on the ParAccel data warehouse. We tested Redshift on HDDs.Hive - a Hadoop-based data warehousing system. (v0.12)Shark - a Hive-compatible SQL engine which runs on top of the Spark computing framework. (v0.8.1)Impala - a Hive-compatible* SQL engine with its own MPP-like execution engine. (v1.2.3)Stinger/Tez - Tez is a next generation Hadoop execution engine currently in development (v0.2.0)
This remains a work in progress and will evolve to include additional frameworks and new capabilities. We welcome contributions.
What this benchmark is not
This benchmark is not intended to provide a comprehensive overview of the tested platforms. We are aware that by choosing default configurations we have excluded many optimizations. The choice of a simple storage format, compressed SequenceFile, omits optimizations included in columnar formats such as ORCFile and Parquet. For now, we've targeted a simple comparison between these systems with the goal that the results areunderstandable and reproducible.
What is being evaluated?
This benchmark measures response time on a handful of relational queries: scans, aggregations, joins, and UDF's, across different data sizes. Keep in mind that these systems have very different sets of capabilities. MapReduce-like systems (Shark/Hive) target flexible and large-scale computation, supporting complex User Defined Functions (UDF's), tolerating failures, and scaling to thousands of nodes. Traditional MPP databases are strictly SQL compliant and heavily optimized for relational queries. The workload here is simply one set of queries that most of these systems these can complete.
Business intelligence (BI) and analytics leaders need to embrace four trends that are set to challenge traditional assumptions about these technology areas, according to Gartner, Inc. (The relentless march of predictive analytics...
Connected objects are not smart on their own, they only provide value when connected to a backend (often cloud-based) service. The recent advances in big data technologies enables providers of connected objects to deliver high-value services from and through their objects
People’s efforts have understandably been focused elsewhere. This week at the ITU Plenipotentiary Conference in Busan, the International Telecommunication Union (ITU), the GSMA and the Internet Society (ISOC) announced that they are joining forces in the fight against Ebola. This unity is an essential step forward, but along with the GSMA, United Nations Global Pulse, and a number of other data scientists, I really want to make sure we, and most importantly the African mobile operators, address this opportunity and truly harness the potential of the data available.
There are around 160 million workers in the US alone, and most companys’ largest expense is payroll. In fact in most businesses payroll is 40% or more of total revenue, meaning that total US payroll expense is many billions of dollars.
How well do organizations truly understand what drives performance among their workforce? The answer: not really very well. Do we know why one sales person outperforms his peers? Do we understand why certain leaders thrive and others flame out? Can we accurately predict whether a candidate will really perform well in our organization?
New version of Cascading released this week incorporates Hadoop 2 support and includes Cascading Lingual - an open source project that provides a comprehensive ANSI SQL interface for accessing Hadoop-based data...
Last month, Canonical, the organization behind the Ubuntu operating system, partnered with MapR, one of the Hadoop heavyweights, in an effort to make Hadoop available as an integrated part of Ubuntu through its repositories.
Sharing your scoops to your social media accounts is a must to distribute your curated content. Not only will it drive traffic and leads through your content, but it will help show your expertise with your followers.
How to integrate my topics' content to my website?
Integrating your curated content to your website or blog will allow you to increase your website visitors’ engagement, boost SEO and acquire new visitors. By redirecting your social media traffic to your website, Scoop.it will also help you generate more qualified traffic and leads from your curation work.
Distributing your curated content through a newsletter is a great way to nurture and engage your email subscribers will developing your traffic and visibility.
Creating engaging newsletters with your curated content is really easy.