Achieving Digital Transformation with DSE

Macquarie Bank uses DSE to help power its digital transformation across a variety of customer-focused channels.

Macquarie Bank

Products & Services

DataStax Enterprise


Financial Services & Insurance


Sydney, Australia
Contact Sales

<2 Years
to transform from no retail presence to digital banking juggernaut

360 Degree
view of the customer thanks to real-time data consolidated from disparate systems

100 Million
people using Macquarie managed infrastructure assets every day


Choosing the right platform to support our digital journey

Our customer’s technological needs and expectations are constantly changing, so we must change with them to help us deliver a truly world-class and innovative digital experience. We believe a digital bank needs to be more human and intuitive than a traditional brick-and-mortar bank, with an intimate understanding of what the customer wants and needs. Macquarie has a branchless network and a sound financial services business with solid partnerships with some of Australia’s leading brands, putting us in a favorable position to deliver an experience customers are not able to get elsewhere. We needed to look at the architectural foundations to support our aspirations to ensure we become even more relevant in today’s digital world.

What was needed to drive our digital capability?

When selecting a database that would help us embark on our digital journey, we needed to understand the necessities involved. Firstly, the digital architecture must operate in a continuous real-time environment and capture fast-occurring events and data streams. As consumers have become accustomed to highly personalized digital experiences 24 hours a day, seven days a week, our systems also need to be personal and in the moment. We want users to have the same experience they currently have with digital companies like Facebook or Apple. Macquarie Bank must replicate such a dynamic through an engaging online and mobile banking experience that gives customers the power to take control of their finances online to reach their goals.

Personalization had to be at the forefront of our solution to enable customers to understand how they spend their money, how their finances are performing, and where they need to improve. To do so meant utilizing both real-time and batched analytical solutions. Analytics needs to be in real-time, alongside historical data provisioning quick feedback loops for customers. Data pushed on the edge, recommendations, and personalization must-have analysis done in near real-time at the point of interaction or each transactional action or event. An important digital feature for us was giving users the power to search their accounts and transactions in natural language, so they can engage with their finances and gain genuine insights. This means augmenting the data collected from the customer with information sourced at the enterprise level to provide meaningful and searchable analysis, so not only can you search spending on categories like coffee or fashion, but by store and the location of expenditure.

Additionally, to support a great customer experience, the volume of transactional data, events, and actions have to be considerably large, stored for long periods, and importantly be accessible at high speeds. The architecture has to respond to the ever-changing needs and demands of future customers and technologies, given the rapid and flexible platform changes necessary to be and remain relevant in today’s digital market.

The co-location of data and technology with Cassandra and Solr for search and Cassandra with Spark for analytics serves as the main benefit of DSE for Macquarie Bank. The results in the real-time nodes having access to data instantly and not requiring time-consuming or costly ETL processes to move data between systems because all data replicates in the cluster.

Rajay RaiDigital Architect at Macquarie Bank

The solution to building our digital credibility

To support our digital banking transformation, we selected Cassandra due to its elastic linear scale, tunable consistency, in-memory capability, and peer-to-peer architecture with DataStax Enterprise. The implementation of Spark provided for near real-time stream processing and in-memory distributed computing capability with support for machine learning and Solr for its search and indexing. All of these come in the DataStax Enterprise (DSE) platform from DataStax.

The key benefit of using these technologies from DSE is the co-location of the data and technology with Cassandra and Solr for search and Cassandra with Spark for analytics. This results in the real-time nodes having access to data instantly and not requiring time-consuming or costly ETL processes to move data between systems, because all the data is transparently replicated in the cluster. The vision of having HTAP (Hybrid Transactional/Analytical Processing) architecture enacted as an achievement through workload segregation allowing data centers dedicated to analytics and search.

DataStax provided the necessary training to help initiate the first phase of our project and simple upgrades to the latest software release with no downtime. Datastax is more relevant with the most up-to-date technological capabilities.

The DSE OpsCenter provides monitoring and alerting capability, providing platform stability and helps manage platform cost. The management of costs through alerts raised when best practices are not implemented in the platform, providing platform stability, and with the right amount of automation, helps in managing the cost of the platform. The technology-enabled us to reduce the complexity for implementing stream processing for automated push notification and distributed batch processing using Spark. We have been able to deliver a rich user experience based on proximity search by providing users with location-based information and natural language search has built on top of the indexing capability of Solr. Data can be served up at high speeds to various devices using the low latency reads provided by Cassandra. Where required we use in memory features of Cassandra to store reference data in memory which is used to accelerate data enrichment at the time of stream processing.

The advantage of using these technologies is that it is founded on the principles of ease of use, low latency, distribution and fault tolerance. These technologies allow us to focus on delivering an exceptional experience and value, while benefiting from DataStax’s commitment to platform innovation. A good example is the soon-to- be introduced graph technology on DSE which will further enhance the multi-model capability of the platform, and these features will allow us to focus on execution of sophisticated personalized recommendations, advice and experience quickly and efficiently.