recommends: MIT’s Digital Program -Tackling the Challenges of Big Data’ New Session! Tackling the Challenges of Big Data, running October 6 – November 17 « This Digital Programs course will survey state-of-the-art topics in Big Data, looking at data collection (smartphones, sensors, the Web), data storage and processing (scalable relational databases, Hadoop, Spark, etc.), extracting […]
hadoop
[informatiquenews] BigDataFr recommande : L’étincelle Spark embrase le big data
BigDataFr recommande : L’étincelle Spark embrase le big data « Cloudera veut pousser Spark plus près d’Hadoop en remplacement de MapReduce. Avec le rachat d’Onyara, Hortonworks veut unifier la plate-forme Hadoop. Dans l’univers Hadoop, Hortonworks, Cloudera et MapR sont les trois étoiles les plus brillantes avec des positionnements différents : pure open source pour le premier, […]
[Cloudera] BigDataFr recommends: Ibis on Impala – Python at Scale for Data Science
BigDataFr recommends: Ibis on Impala – Python at Scale for Data Science ‘This new Cloudera Labs project promises to deliver the great Python user experience and ecosystem at Hadoop scale. Across the user community, you will find general agreement that the Apache Hadoop stack has progressed dramatically in just the past few years. For example, […]
[HAL] BigDataFr recommends: HaoLap – a Hadoop based OLAP system for big data #datascientist
BigDataFr recommends: HaoLap – a Hadoop based OLAP system for big data Abstract ‘In recent years, facing information explosion, industry and academia have adopted distributed file system and MapReduce programming model to address new challenges the big data has brought. Based on these technologies, this paper presents HaoLap (Hadoop based oLap), an OLAP (OnLine Analytical […]
[LeMagIT] BigDataFr recommande : Spark contre MapReduce – quelle solution pour les entreprises
BigDataFr recommande : Spark contre MapReduce – quelle solution pour les entreprises « Out MapReduce. La percée fut belle, mais les développeurs Big Data actuels ont faim de simplicité et de rapidité. Et quand il s’agit de choisir un framework pour exécuter des tâches dans un environnement Hadoop, ils sont de plus en plus nombreux à […]
[O’R] BigDataFr recommends: The O’Reilly Data Show Podcast-Alice Zheng Strata + Hadoop #machine learning
BigDataFr recommends: The O’Reilly Data Show Podcast-Alice Zheng Strata + Hadoop Bridging the divide – Business users and machine learning experts […] ‘As tools for advanced analytics become more accessible, data scientist’s roles will evolve. Most media stories emphasize a need for expertise in algorithms and quantitative techniques (machine learning, statistics, probability), and yet the […]
[O’R] BigDataFr recommends: Hadoop for business – Analytics across industries
BigDataFr recommends: Hadoop for business – Analytics across industries Ben Sharma on the business impact of Hadoop and the evolution of tools ‘In this episode of the O’Reilly Podcast, O’Reilly’s Ben Lorica chats with Ben Sharma, CEO and co-founder of Zaloni, a company that provides enterprise data management solutions for Hadoop. Sharma was one of […]
[CIO] BigDataFr recommends: 5 things CIOs need to know about data lakes #datascientist
BigDataFr recommends: 5 things CIOs need to know about data lakes ‘1.The concept is still quite new. The term data lake, credited to Pentaho CTO James Dixon, has been bandied about for several years. But the idea of data lakes as corporate resources is still in its infancy, according to IDC analyst Ashish Nadkarni. A […]
[Decideo] BigDataFr recommande : Le Big Data, c’est bien. Avec Hadoop et NoSQL, c’est mieux
« BigDataFr recommande : Le Big Data, c’est bien. Avec Hadoop et NoSQL, c’est mieux ! Hadoop et NoSQL – une rivalité complémentaire Outre le framework Hadoop, de nombreuses entreprises utilisent actuellement la technologie de gestion de base de données NoSQL pour traiter le Big Data. L’un et l’autre peuvent gérer des volumes de données importants […]
[HAL] BigDataFr recommends: FP-Hadoop: Efficient Execution of Parallel Jobs Over Skewed Data #datascientist
BigDataFr recommends: FP-Hadoop: Efficient Execution of Parallel Jobs Over Skewed Data Abstract ‘Big data parallel frameworks, such as MapReduce or Spark have been praised for their high scalability and performance, but show poor performance in the case of data skew. There are important cases where a high percentage of processing in the reduce side ends […]