Concepts

Infrastructure for data-intensive applications (storage, computation, and networking)
Data storage and retrieval with modern large-scale data stores (databases, data lakes, data streams)
Engineering data processing pipelines using functional programming concepts (map, reduce, filter)
Servicing data lakes and data streams for analytics and transactional operation
Techniques

Functional programming with Java (Lambda functions, streams, and parallel processing)
Distributed data processing using Apache Spark (ingesting, streaming, transforming, and storing data)
Using the cloud environment (AWS) for deploying data solutions
Using SQL to extend traditional database skills for big data analytics

Module 1: Architecture and infrastructure
The infrastructure for intensive data applications
Parallel and distributed computing architectures
The software landscape for big data

Module 2: Functional programming and the map-reduce paradigm
Functional programming in Java
Streams and functional operations (map, reduce, filter)
Parallelism and concurrency models

Module 3: Scaling-out, distributed computing on computer clusters
Apache Spark, the unified analytics engine for distributed big data processing
Data ingestion and storage from/to files and data stores
Implementing data pipelines with Spark Resilient Distributed Datasets (RDDs), Spark Data Frames, and Spark SQL

Module 4: Deployment into production
Batching, queuing, and stream processing
The lambda architecture on the cloud
Deploying Spark on Amazon Web Services (AWS) Elastic MapReduce (EMR)

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