In this paper , We will introduce DataStax Enterprise How to help Australia's largest investment bank Macquarie's Digital Banking , Real time analysis and natural language search are realized , And provide users with personalized user experience .
DSE Enables us to focus on providing superior experience and value , At the same time, continue to benefit from DataStax Continuous investment in platform innovation .
——Rajay Rai, Macquarie digital architect
One minute quick reading of cases
industry
banking
The opportunity to
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Improve Digital Banking Based on a proper understanding of user needs , Make it more humanized and easy to use than traditional offline banking outlets
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In today's digital world , Providing users with innovative digital experiences is becoming more and more important
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Make the online mobile banking experience easy to use and attractive , So that users can control their personal financial situation online , And then reach your personal goals
Solution
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DataStax Enterprise (DSE) Provides elastic linear scalability 、 Adjustable consistency levels and in memory database performance , And equivalence (peer-to-peer) The architecture of
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DSE Search and DSE Analytics Provides near real-time streaming processing 、 Machine learning and search capabilities
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DSE OpsCenter（ Operation and maintenance center ） Provide monitoring and alarm functions , It is helpful to the stability and cost management of the platform
results
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DSE The features of Macquarie make it possible for Macquarie to focus on refined personalized recommendations 、 Implementation level of suggestion and use experience
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Provide a shorter and more efficient speed of value realization for Macquarie's development team and customers (time to value)
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Easily upgrade to the latest DSE Version and no downtime , This allows Macquarie to use most of the latest technology features more quickly
01 About Macquarie Bank
Macquarie Bank is a multinational investment bank and diversified financial services group , To institutions around the world 、 Corporate and retail customers provide banking services 、 Financial advisory and investment funds . Macquarie is Australia's largest investment bank , Its headquarters are in Sydney .
Macquarie uses DataStax Enterprise Max To empower a number of projects in their digital journey . Digital architect for Macquarie Bank and financial services group Rajay Rai Describes in depth how they use DSE Of .
02 Choose the right platform to support digital travel
Our customers' needs and expectations for technology are constantly changing , We change with it to truly deliver a world-class and innovative digital experience —— It's very important for us . We believe that digital banking should be improved based on a proper understanding of user needs , Make it more humanized and easy to use than traditional offline banking outlets .
Macquarie has a banking network without physical outlets , And a solid financial services business —— These businesses are based on strong partnerships with several of Australia's leading brands . These foundations give us the ability to provide users with a user experience they won't get elsewhere .
To help us deliver innovative digital experiences to our customers , We need to look at the infrastructure that underpins our ambitions , To ensure that we have a place in today's digital world .
03 What is needed to drive digital capabilities ？
When we choose a database that will help us open our digital journey , We have to really understand what we need . First , The right digital architecture must be able to run continuously in a real environment , Capture rapidly emerging events and data streams . As customers get used to the 24 / 7 and personalized digital experience , Our own systems also need to be personalized and on call .
We want users to have things like Facebook and Apple A kind of user experience that digital companies provide for them , So it's very important for us to build an easy-to-use and attractive online mobile banking experience , Because it allows users to control their personal financial situation online to achieve their goals .
Personalization comes first in our overall solution —— Let customers understand how their money is spent 、 What is the financial situation and what improvements can they make . This means that we don't just need to take advantage of real-time analysis , You also need batch analysis scenarios .
To quickly provide feedback to customers , We need to combine real-time analysis of historical data . Data in hand , Both recommendation and personalization require data analysis to be almost in user interaction 、 To complete at the same time as an action or event occurs .
One of the most important digital features we're going to offer users is to enable them to search their accounts and transactions in natural languages , In this way, they will be more concerned about their financial situation and can really get a deep understanding . That means we need to get more data from our customers , And supplemented by information from enterprise sources , To provide meaningful and searchable analysis —— in other words , You can't just by product type （ Like coffee 、 dress ） Search your expense records , You can also search by store or place of consumption .
in addition , In order to provide a good customer experience , Trading data 、 The amount of events and actions has to be very large , Plus, it's going to take a long time to store , And it's very important to be able to read at high speed . This means that the ideal architecture must be able to respond to the changing needs of future customers and technologies —— actually , Rapid and flexible platform changes are necessary and meaningful in today's technology market .
04 Solutions for building digital credibility
To support the transformation of our Digital Banking , We chose Cassandra, Because of its elastic linear scalability 、 Adjustable consistency levels and in memory database performance , And equivalence (peer-to-peer) The architecture of .
come from DataStax Of DataStax Enterprise(DSE) Platform integration Spark, Not only provides near real-time streaming processing , It's also based on machine learning and Solr The search and index capabilities provide distributed computing power in memory .
Use DSE The key benefit of these technologies in is ： It's data 、Cassandra And Solr The combination of search capabilities and Cassandra And Spark The confluence of combined analytical capabilities . Because the replication of all data in the cluster is transparent , The result is that real-time nodes can get data immediately , Without the time-consuming and laborious transfer of data between systems ETL The process .
Have mixed transaction analysis processing (Hybrid Transactional / Analytical Processing, namely HTAP) The vision of the architecture of has been separated by workload (workload segregation) Realization , This allows the data center to be used exclusively for analysis and search .
DataStax Provided the necessary training , Helped us open the first phase of the project . besides ,DataStax It is also easy to upgrade to the latest version of the software without downtime , This allows us to use most of the latest technical features more quickly .
DSE OpsCenter（ Operation and maintenance center ） Provide monitoring and alarm functions , It is helpful to the stability and cost management of the platform . Alerts will sound when best practices are not followed in the platform , Provides stability for the platform . Alarms and proper automation , It's also good for managing platform overhead .
In the use of Spark When automatically pushing notifications and performing distributed batch processing , These technologies allow us to reduce the complexity of streaming . Based on the Solr On top of the indexing ability of , We can provide users with location-based information and natural language search capabilities . Through these functions , We can use proximity search (proximity search) Provide a rich and colorful user experience .
Cassandra Provides low latency read performance , So we can deliver data to different devices at high speed —— We need to use Cassandra Of in-memory Feature stores reference data in memory (reference data), In order to speed up data enrichment in streaming processing (data enrichment).
The advantage of these technologies is that they are developed on the basis of ease of use 、 Low latency 、 Distributed and fault tolerant . These technologies allow us to focus on delivering superior experience and value , At the same time, continue to benefit from DataStax Continuous investment in platform innovation .
Let me give you an illustrative example ：DSE Graph technology of (graph technology) Further strengthen the platform's multi model (multi-model) function . These features will allow us to focus on mature personalized recommendations 、 Implementation level of suggestion and use experience .
