Data Lineage

Big data systems scale horizontally i.e. increase capacity by adding new hardware or software entities into the distributed system. The distributed system acts as a single entity in the logical level even though it comprises multiple hardware and software entities. The system should continue to maintain this property after horizontal scaling. An important advantage of horizontal scalability is that it can provide the ability to increase capacity on the fly. The biggest plus point is that horizontal scaling can be done using commodity hardware.

The horizontal scaling feature of Big Data systems should be taken into account while creating the architecture of lineage store. This is essential because the lineage store itself should also be able to scale in parallel with the Big data system. The number of associations and amount of storage required to store lineage will increase with the increase in size and capacity of the system. The architecture of Big data systems makes the use of a single lineage store not appropriate and impossible to scale. The immediate solution to this problem is to distribute the lineage store itself.

The best-case scenario is to use a local lineage store for every machine in the distributed system network. This allows the lineage store also to scale horizontally. In this design, the lineage of data transformations applied to the data on a particular machine is stored on the local lineage store of that specific machine. The lineage store typically stores association tables. Each actor is represented by its own association table. The rows are the associations themselves and columns represent inputs and outputs. This design solves 2 problems. It allows horizontal scaling of the lineage store. If a single centralized lineage store was used, then this information had to be carried over the network, which would cause additional network latency. The network latency is also avoided by the use of a distributed lineage store.

Architecture of lineage systems