Big Data Architecture - SCORM

Course Description: Big Data Architecture

This course is taught in online mode and consists of 8 units . The duration of the course is 115 hours which is distributed between the content and the collaboration tools. Upon completion, the student will receive a certificate of completion.

Training is done through our Virtual Campus, With this modality you will have all the educational content on the course platform and it will be accessible, from the day the course starts, 24 hours a day, every day of the week. The student will also have access to participation forums , as well as a continuous tutoring .

• Access to the platform: 8 weeks
• Individualized support

Course objectives

Gain in-depth knowledge of 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 content

Unit 1. BATCH PROCESSING

1.1. Hadoop.
1.2. Pig.
1.3. Hive.
1.4. Sqoop.
1.5. Flume.
1.6. Spark Core.
1.7. Spark 2.0.

Unit 2. STREAMING PROCESSING

2.1. Fundamentals of Streaming Processing.
2.2. Spark Streaming.
2.3. 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 COMPUTING 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 ADMINISTRATION

7.1. Cluster Administration and Installation: Cloudera and Hortonworks.
7.2. Service optimization and monitoring.
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

Prerequisites

There are no technical prerequisites required to take this course. However, basic computer skills and knowledge of environments related to Information Technology are recommended.