Retail banks get transactions at a high overall volume from a wide variety of sources, like credit card transactions, tellers, electronic transfers, and ATMs. The primary transaction system data is replicated in several places to provide disaster recovery and operational data store services. Customers make queries about individual transactions in the last day, month, and year, but the storage and query performance required to give full information about all transactions is beyond the capacity of traditional architectures.
Retail Bank Services
Operational data store with near real time data capture unified with historical data, with high ingest and high query rates.
Customers need to query their recent balances and their transactions from months or even years ago. They also want more information than can easily be stored in a separated architecture story (vendor name, ATM location, transfer location, etc.), information that can vary depending on the type of transaction.
Query data from the current day’s transactions with high reliability and low latency, without impacting the performance of the primary transactional system
Consolidate the historical data with near real time data for user transactions so that customers and bank employees can look at transactions with no little latency, whether the data is from a few minutes ago, a few hours ago, a few days ago, a few months ago, or even last year, while minimizing data storage cost, query complexity, and data redundancy.
EsgynDB initially provides an ODS for the mission-critical transaction system, offloading near-real-time queries there to allow the primary transactional system to meet its SLAs.
The same data lake also includes the historical data, allowing for seamless connection of data over time, with no extra data replication. And with EsgynDB’ s ability to integrate structured, semi-structured, and unstructured data, customers and bank employees have access to more information about each transaction.
Along with more data and fast access comes the ability to delight bank customers, by letting them look at their financial history in new ways. Customers can search by transaction value (all transactions over $500, say), or at a particular store. The data can also be leveraged to provide additional financial services, analyzing transaction types and changes across the last six months, services that differentiate the bank from its competitors.