Big Data for Decision Makers
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Description
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
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Modern data trends and use cases
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What is Big Data?
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The building blocks of big data solutions
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Ingesting, Querying, Indexing, Processing and Analyzing
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Hadoop and Hadoop eco-system
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The Lambda architecture, and other design patterns
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Horizontal Scaling
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Micro-Services
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Introducing our Use-Cases
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Ingesting Data
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How data is collected?
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Where to keep data?
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Kafka vs. Flume vs. Sqoop
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Saving and Querying Data
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Distributed File Storage
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Saving data over HDFS
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Querying data using SQL over data files
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Using Hive
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NoSQL Introduction
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Key-Value Stores (Riak)
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Columnar Databases (Cassandra)
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Document Databases (Couchbase)
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Graph Databases (Neo4j)
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Processing Data
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Extract, Transform, Load
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ETL using Pig
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Batch Processing
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Hadoop vs. Spark
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Near-Real-Time Processing
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Storm vs. Spark Streaming
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Search and Indexing data
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Full text search and beyond
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Elastic Search/Solr
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Big Data solutions, End-to-End
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Public Cloud Deployments
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Workflow Management
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Security
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- 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
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