Quick recap of introduction part

Services
Services shall be main element of architecture of distributed systems. In this section we focus on these aspects of Service which are the important from the perspective of distributed IoT and High Volume systems.

• Services separation and isolation
• Service SLA criteria
• Domain Driven Development from 10k feets
• Microservies from 10k feets

Distributed computing
One of the main purpose of the distributed system is to provide more computing resources distributed among different type services and machines. This section focuses on type services, models of their organization and interactions. We will also look at some examples to discuss capabilities and consequences of some patterns.

• Stateless processing
• Stateful service with own state(subscription), shared state(cache)
• Chain of services – dependencies and eventually consistent
• Distribute computing between nodes
• Choreography and Orchestration
• Balance processing
• Latency and Throughput
• Examples of systems architectures
• Technology Examples: Storm, Hadoop, Spark, AWS Lambda

Communication in distributed systems
This section focuses on the communication, APIs, between internal system services and with outside world. We will discuss common models for IoT and High Volume systems. We will focuses also on the aspects which are important for distributed and high computing systems.

• Low coupling
• Requests and Events
• Synchronized API
• Asynchronized API
• Events
• Messaging and Push Notification API
• Message Bus
• Stream of events
• Persistency of events – failures and replay
• Examples of systems architectures
• Technology Examples: Kafka and AWS IoT

Distributed storage
IoT and High Volume systems works with large amount of data which can be provided by lots of parallel input in short time. This section focus on strategies for data storing and processing and on consequences of their apply.

• Scale of data
• BASE and ACID or NoSQL vs SQL
• Types of NoSQL
• NoSQL limitations
• API and data structures normalization
• Partitioning/Sharding and different strategies
• Replication
• Operations and level of consistency
• Data conflicts
• Immutable vs transactional
• Single data owner
• Single source of truth
• Race conditions
• Transactions in distributed systems
• Examples of systems architectures
• Technology Examples: MongoDB, CouchDB
• Technology Examples: ETCD and Zookeeper

Distributed cache
Cache is volatile type of data storage with provide high access and in many cases they are required to achieve low latency. This section will discuss strategies of cache implementation in distributed architecture

• Scale of data and fast access
• Data duplication and data transformation
• Cache synchronization and lazy cache build
• Examples of systems architectures
• Technology Example: REDIS

Upgrade of distributed systems
this section focuses on the different upgrade strategies of distributed systems. We will walk through different solutions which can be applied and we will discuss their pluses and minuses.

• System upgrade strategies
• Services upgrade strategy
• Data upgrade strategy
• Examples of upgrade flow

Big picture
In this section we will combine together all discussed aspects to build few examples of complete, end to end architecture of IoT and High Volume systems.

• Examples of end to end architecture for IoT systems
• Examples of end to end architecture for High Volume systems