ВЪПРОСНИК: Кариера, Мисия, Свобода - Ясен Николов: технологии на успеха ти. 2/2Yasen Nikolov
2 незабравими часа преживяване и учене с Ясен Николов.
Планирай и изчиствай неяснотите!
Проектирай Свободата си!
Изкуството и науката на личностното и груповото развитие.
Автономията и искреността са на ваша страна.
100% ще успеем.
Short overview of the Church's lambda calculus, with a focus on the on the operational semantics of the calculus. No previous mathematical background is required.
ВЪПРОСНИК: Кариера, Мисия, Свобода - Ясен Николов: технологии на успеха ти. 2/2Yasen Nikolov
2 незабравими часа преживяване и учене с Ясен Николов.
Планирай и изчиствай неяснотите!
Проектирай Свободата си!
Изкуството и науката на личностното и груповото развитие.
Автономията и искреността са на ваша страна.
100% ще успеем.
Short overview of the Church's lambda calculus, with a focus on the on the operational semantics of the calculus. No previous mathematical background is required.
February 2016 HUG: Apache Kudu (incubating): New Apache Hadoop Storage for Fa...Yahoo Developer Network
Over the past several years, the Hadoop ecosystem has made great strides in its real-time access capabilities, narrowing the gap compared to traditional database technologies. With systems such as Impala and Apache Spark, analysts can now run complex queries or jobs over large datasets within a matter of seconds. With systems such as Apache HBase and Apache Phoenix, applications can achieve millisecond-scale random access to arbitrarily-sized datasets. Despite these advances, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing workloads. This talk will investigate the trade-offs between real-time transactional access and fast analytic performance from the perspective of storage engine internals. It will also describe Kudu, the new addition to the open source Hadoop ecosystem with out-of-the-box integration with Apache Spark, that fills the gap described above to provide a new option to achieve fast scans and fast random access from a single API.
Speakers:
David Alves. Software engineer at Cloudera working on the Kudu team, and a PhD student at UT Austin. David is a committer at the Apache Software Foundation and has contributed to several open source projects, including Apache Cassandra and Apache Drill.
Algunas notas y lecciones aprendidas sobre nuestra experiencia trabajando en código abierto. 8 proyectos interesantes que abrimos en los últimos meses.
metodología de la investigación científica en el campo administrativoariel kapell
información general sobre la metodología de la investigación científica, desde contenidos generales de la investigación, y pasos de la investigación científica, hasta como redactar y presentar el informe escrito
February 2016 HUG: Apache Kudu (incubating): New Apache Hadoop Storage for Fa...Yahoo Developer Network
Over the past several years, the Hadoop ecosystem has made great strides in its real-time access capabilities, narrowing the gap compared to traditional database technologies. With systems such as Impala and Apache Spark, analysts can now run complex queries or jobs over large datasets within a matter of seconds. With systems such as Apache HBase and Apache Phoenix, applications can achieve millisecond-scale random access to arbitrarily-sized datasets. Despite these advances, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing workloads. This talk will investigate the trade-offs between real-time transactional access and fast analytic performance from the perspective of storage engine internals. It will also describe Kudu, the new addition to the open source Hadoop ecosystem with out-of-the-box integration with Apache Spark, that fills the gap described above to provide a new option to achieve fast scans and fast random access from a single API.
Speakers:
David Alves. Software engineer at Cloudera working on the Kudu team, and a PhD student at UT Austin. David is a committer at the Apache Software Foundation and has contributed to several open source projects, including Apache Cassandra and Apache Drill.
Algunas notas y lecciones aprendidas sobre nuestra experiencia trabajando en código abierto. 8 proyectos interesantes que abrimos en los últimos meses.
metodología de la investigación científica en el campo administrativoariel kapell
información general sobre la metodología de la investigación científica, desde contenidos generales de la investigación, y pasos de la investigación científica, hasta como redactar y presentar el informe escrito
1. PubMatic
PatriziaGuarino
Is presented with the
STAR OF THE QUARTER AWARD
in Q3 2015
For going beyond the call of duty to benefit our customers and the company.
_________________________
Rajeev Goel, Chief Executive Officer