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With multiple applications in your IT infrastructure reading and writing to and from different data stores in varying formats, it is imperative to implement a process that will let you integrate the data so that they can be easily used by anyone in your company.
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View
- Easiest one
- Largest support
- Possible performance issues
- Strong Consistency
- One database must be reachable by the other
- DBLink
- Updatable (?)
Materialized View
- Better performance
- Strong or Eventual Consistency
- One database must be reachable by the other
- DBLink
- Updatable (?)
Mirror Table using Trigger
- Depends on Database Support
- Strong
- One database must be reachable by the other
- DBLink
Mirror Table using Transactional Code
Mirror Table using Transactional Code
- *Any* code
- Strong Consistency
- Stored Procedures or Distributed Transactions
- Cohesion and coupling issues
- Updatable (?)
Mirror Table using ETL tools
ETL, (Batch Select)
- Lots of available tools
- Requires external trigger (usually time-based)
- Can aggregate from multiple datasources
- Read Only
Event Sourcing (Stream)
Event Sourcing: one of the hardest one
- State of data is a stream of events
- Eases auditing
- Eventual Consistency
- Distributable stream through a Message Bus
Example: Change Data Capture
Immutable append only log + materialized view
Known also as:
- lambda/kappa architecture;
- database inside-out/unbundled;
- state machine replication;
- etc