Recursos de programación de apache
Apache Cassandra is a scalable database with high availability features. But they come with severe limitations in term of querying capabilities. Since the introduction of SASI in Cassandra 3.4, the limitations belong to the pass. Now you can create performant indices on your columns as well as benefit from **full text search** capabilities with the introduction of the new `LIKE %term%` syntax. To illustrate how SASI works, we'll use a database of 100 000 albums and artists. We'll also show how SASI can help to accelerate analytics scenarios with Spark using SparkSQL predicate-pushdown
I've just watched this very interesting talk by Martin Kleppmann Turning the database inside out with Apache Samza - por Garajeando
In this talk, we present Apache SAMOA, an open-source platform for mining big data streams with Apache Flink, Storm and Samza. Real time analytics is becoming the fastest and most efficient way to obtain useful knowledge from what is happening now, allowing organizations to react quickly when problems appear or to detect new trends helping to improve their performance. Apache SAMOA includes algorithms for the most common machine learning tasks such as classification and clustering. It provides a pluggable architecture that allows it to run on Apache Flink, but also with other several distributed stream processing engines such as Storm and Samza.
¿Quieres saber más? https://www.paradigmadigital.com/ Índice interactivo aquí debajo: 00:12 Presentación 02:04 Kafka y python 03:01 ¿Quién soy? 03:23 Kafka/origen 04:56 Kafka/origen 05:50 Kafka/motivation 06:58 Kafka/ How to? 08:34 Kafka/ Básicos 09:11 Kafka/ Cluster: Topics & Partitions 11:22 Kafka/ Partitions & Replication 14:13 Kafka/ Producers 16:06 Kafka/ Consumers 18:27 Kafka/ Efficiency 19:45 Kafka/ Python Clients 20:21 Kafka/ Python Clients/ Kafka-python 25:51 Kafka/ Python Clients/ Demo 26:16 Kafka y Python/ Questions 29:40 Redis 29:56 Índice 30:22 1. Introducción a Redis 30:24 Características generales 31:04 Casos de uso 31:28 Detalles de implementación 32:25 Operaciones atómicas 33:00 Tipos de datos 33:35 Consola de Redis (redis-cli) 34:16 Ejemplo String 34:42 Ejemplo List 35:01 Ejemplo Hash 35:24 Ejemplo Transacción 36:07 2. Algo de código 36:13 Clientes Redis 36:24 Cliente Python 37:08 Un ejemplo básico 38:25 Productor/consumidor 40:22 PUB/SUB 41:38 Queueing jobs 42:14 3. Algo sobre administración 42:16 Replicación (I) 43:30 Replicación (II) 43:32 Persistencia 45:15 Particiones/ Sharding 46:47 4. La competencia 46:55 La competencia (I) 47:59 La competencia (II) 49:02 La competencia (III) 49:30 Conclusiones 50:59 Preguntas 53:18 Presentación Python y Flink 53:57 Índice 53:59 ¿Quiénes somos? 54:13 Introducción 54:15 Aclaraciones 54:35 Madurez del BigData 55:30 Arquitectura típica 56:10 Despliegue 56:40 ¿Por qué Apache Flink? 56:43 Apache Flink 57:48 Ventanas 58:29 Ventanas por clave 59:04 Tiggers y Evictors 1:00:02 El tiempo es importante 1:00:43 Rendimiento 1:02:44 Funcionalidades 1:04:50 Experiencia con Python 1:05:36 Según la documentación 1:06:49 Prueba básica 1:09:06 Poca actividad 1:10:01 Conclusiones 1:11:12 Preguntas 1:13:00 Backup slides 1:13:02 Terasort 1:14:35 Streaming-Yahoo
Interesting talks/podcasts/presentations that I watched during may:RailsConf 2015 - Nothing is Something Another great talk about OO Design from Sandi Metz.Microservices: Software that Fits in Your Head Dan North at Craft Conf 2016 Ruby Midwest 2011 - Keynote: Architecture the Lost Years Robert C Martin (uncle Bob). The classic talk about how the boom of the web derived in a lack of macro design/architecture for our applications and the problems generated.YOW! Nights March 20...
What’s important about a technology is what you can use it to do. I’ve looked at what a number of groups are doing with Apache Hadoop and NoSQL in production, and I will relay what worked well for them and what did not. Drawing from real world use cases, I show how people who understand these new approaches can employ them well in conjunction with traditional approaches and existing applications. Thread Detection, Datawarehouse optimization, Marketing Efficiency, Biometric Database are some examples exposed during this presentation.