n, and call records) needed for detecting network and service conditions, etc. The collection will support both the real-time streaming as well as the batch methods of data collection. Network Data and Optimization 8.3.2.2 Data Movement This component facilitates the movement of messages and data between various publishers and interested subscribers. There are two types of data in the Network Cloud. Those that are frequent and in short bursts, such as messages and events, and those that are file-based and batched. Open DCAE adopts two technologies one is a KAFKA-based open source for message routing, and the other is a data router for enabling file transmissions. Those two technologies enable data to be streamed and moved within DCAE from edge to core, and enable movements of events across the Network Cloud platform to support services such as change management and closed-loop automation. 8.3.2.3 Edge, Central, and Core Lakes DCAE needs to support a variety of applications and use cases ranging from real-time applications that have stringent latency requirements as well as other analytic applications that have a need to process a range of unstructured and structured data. The DCAE storage lake needs to support these needs and must do SO in a way that allows for incorporating new storage technologies as they become available. This is done by encapsulating data access via APIs and minimizing application knowledge of the specific technology implementations. Given the scope of requirements around the volume, velocity, and variety of data that DCAE needs to support, the storage will leverage technologies that a big data framework must offer, such as support for NoSQL technologies, including in-memory repositories, and support for raw, structured, unstruc- tured, and semi-structured data. While there may be detailed data retained at the DCAE edge layer for analysis and trouble-shooting, applications should optimize the use of precious bandwidth and storage resources by ensuring they propagate only the required data (re