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Course objectives
Know in depth the different processing paradigms in Big Data systems and master the main technologies and their use for the design of scalable architectures adapted to each project.
Course Description
The course consists of 8 units.
The approximate duration of the course is 115 hours distributed between the course content and the collaboration tools and upon completion, the student will receive an accrediting diploma.
The training is carried out through our Virtual Campus, with this modality you will have all the didactic content on the course platform and it will be accessible, from the start day of the course, 24 hours a day, every day of the week. The student will also have participation forums, as well as continuous tutoring.
Course content
Introduction to the course
Unit 1. BATCH PROCESSING
1.1. Hadoop.
1.2. Pig.
1.3. Hive.
1.4. Sqoop.
1.5. Flume.
1.6. SparkCore.
1.7. Spark 2.0.
Unit 2. STREAMING PROCESSING
2.1. Fundamentals of Streaming Processing.
2.2. SparkStreaming.
23. Kafka.
2.4. Pulsar and Apache Apex.
2.5. Implementation of a real-time system.
Unit 3. NOSQL SYSTEMS
3.1. Hbase.
3.2. Cassandra.
3.3. MongoDB.
3.4. Neo4J.
3.5. Redis.
3.6. Berkeley DB.
3.7 Cosmos DB.
Unit 4. INTERACTIVE QUERY
4.1. SQL
4.2. NoSQL
4.3. Lucene + Solr.
Unit 5. HYBRID COMPUTER SYSTEMS
5.1. Lambda architecture.
5.2. Kappa Architecture.
5.3. Apache Flink and practical implementations.
5.4. Druid.
5.5. ElasticSearch.
5.6. Logstash.
5.7. Kibana.
Unit 6. CLOUD COMPUTING
6.1. Amazon Web Services.
6.2. Google Cloud Platform.
6.3. Microsoft Azure
Unit 7. BIG SYSTEMS MANAGEMENT
7.1. Administration and Installation of clusters: Cloudera and Hortonworks.
7.2. Optimization and monitoring of services.
7.3. Security: Apache Knox, Ranger and Sentry.
Unit 8. DATA VISUALIZATION
8.1. Visualization tools: Tableau and CartoDB.
8.2. Visualization Libraries: D3, Leaflet, Cytoscape.
Final evaluation
Quality Questionnaire.
Previous requirements
No prior technical requirements are necessary to take this course. However, basic computer skills and knowledge of environments related to Information Technology are recommended.
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