Big Data for Decision Makers

BD4DM - Version:1
This course is intended for decision makers, technical managers and team leaders, who are interested to learn how to design big data solutions. This course will introduce concepts, use-cases and leading products that are used to design a scalable solution for big data in the modern data landscape. This course will introduce the different components a big data solution is comprised of, using pre-defined use cases as an example on how to plan a solution, from start to finish.
Intended audience
Technical managers and Decision makers
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  • The modern data trends
    • Modern data trends and use cases
    • What is Big Data?
    • The building blocks of big data solutions
      • Ingesting, Querying, Indexing, Processing and Analyzing
      • Hadoop and Hadoop eco-system
    • The Lambda architecture, and other design patterns
      • Horizontal Scaling
      • Micro-Services
    • Introducing our Use-Cases
  • Ingesting Data
    • How data is collected?
    • Where to keep data?
    • Kafka vs. Flume vs. Sqoop
  • Saving and Querying Data
    • Distributed File Storage
      • Saving data over HDFS
      • Querying data using SQL over data files
      • Using Hive
    • NoSQL Introduction
      • Key-Value Stores (Riak)
      • Columnar Databases (Cassandra)
      • Document Databases (Couchbase)
      • Graph Databases (Neo4j)
  • Processing Data
    • Extract, Transform, Load
    • ETL using Pig
    • Batch Processing
    • Hadoop vs. Spark
    • Near-Real-Time Processing
    • Storm vs. Spark Streaming
  • Search and Indexing data
    • Full text search and beyond
    • Elastic Search/Solr
  • Big Data solutions, End-to-End
    • Public Cloud Deployments
    • Workflow Management
    • Security
  • Be familiar with data trends in recent years, the potential hidden in that data and the problems new data platforms need to handle with.
  • Be familiar with the different components needed to build a big data solution, such as data ingestion, analysis and transformation.
  • Be familiar with big data use cases
  • Be familiar with big data products