Project Management, IT Training, Agile Coaching
Quiero compartir un Jupyter Book que he realizado con los apuntes elaborados para el curso de Fundamentos de Big Data.
Durante el curso he utilizado los cuadernos de Jupyter ya que son extremadamente útiles para hacer presentaciones, tutoriales ejecutando código, y demás. Ahora he tenido tiempo para instalar Jupyter Lab y finalmente probar los Jupyter Book.
I’m sharing Big Data Fundamentals, Part II, (Part I is here) with an introduction to Big Data covering: Big Data processes: ingest, store, process/query, visualize; tools and technologies: Hadoop, Sqoop, Kafka, Mesos, Redis, CouchDB; Document stores: MongoDB; Column stores: HBase + Cassandra; Big Data analytics: Spark, Storm; and Elastic Stack: Logstash, ElasticSearch and Kibana.
We’ll see also Machine learning techniques with Spark (MLlib, Streaming) and TensorFlow.
I’m sharing Big Data Fundamentals, Part I, with an introduction to Big Data covering: Big Data market and trends, definition and history, Big Data types (structured, unstructured, semi-structured), some use cases, best practices for Big Data analytics; and an overview of Apache Hadoop: HDFS, MapReduce, YARN.
In this tutorial we’ll install the Big Data framework Apache Hadoop on a previously installed CentOS 8 virtual machine. We’ll use Docker containers for the cluster creation.
This is for testing purposes and not for production. Be careful and don’t expose it to Internet since I’m not setting up any security measure for it.