DataStax Enterprise is particularly well suited to organizations within finance and banking. As more customers continue to choose online financial services over their brick-and-mortar counterparts, the ability to achieve customer-focused personalization, virtually zero downtime, and a lower total cost of ownership (TCO) is key to success.

Financial and banking organizations have relied on mainframes to provide services for both their customers and their employees for decades. Yet mainframes were not originally designed to interact with customer-facing applications. As old technology collides with new technology, mainframes are being pressed to work harder, and in ways for which they were not designed. Banking customers now expect their data to be instantly available, whether from a smartphone, tablet, or laptop. Trading analysts expect market data to be available in real time. This change means that financial and banking data models must support write-intensive, read-intensive, balanced read/write, and high-performance characteristics.

Application modernization in finance and banking entails four key traits:

• Cost savings—Because mainframe usage fees consist of a significant percentage of mainframe TCO, IT organizations within finance and banking are looking for ways to reduce their reliance on mainframes for customer-facing applications while still giving their customers a highly personalized experience. Offloading customer-facing application interactions lowers mainframe use, which can lead to cost savings.
• Ability to capture and act on large amounts of data in real time—Financial and banking organizations generate and consume massive amounts of data, from recording customer financial transactions to gathering real-time market data that trading analysts and customers can use. DataStax Enterprise is well suited for capturing and writing large amounts of data in real time. Captured data can then be quickly and automatically analyzed, giving trading analysts and customers fast access to critical financial information.
• Historical data storage—Both customers and financial analysts require access to vast amounts of historical financial data, whether it be transactions for banking customers or historical financial data for publicly traded companies. This data needs to be immediately accessible, while the data storage and analysis platform needs to be flexible as customer demands and datatypes continue to evolve. DataStax Enterprise can store and read these vast amounts of data using its distributed data model, which gives analysts and customers immediate access to the information that they need.
• Personalization—Today’s financial services and banking customers are more involved in their financial planning than ever before—from tracking how they spend money to planning for retirement. Providing insights into their finances requires both batch and real-time analytics that are simple to use. Also, financial analysts need to have access to up-to-the-second market information and analysis. DataStax Enterprise can provide financial analysts and banking customers quick access to their personalized data while also giving them the ability to make split-second market decisions.

The peer-to-peer architecture built into DataStax Enterprise enables it to scale data processing elastically, meaning that it can ingest and process data across any number of nodes and data centers. Also, DataStax Enterprise integrates with tools such as Apache Spark, which provides near-real-time processing of data streams, and new Storage-Attached Indexing (SAI) index technologies for search and indexing. As a platform, DataStax Enterprise functions as a hybrid transactional/analytical processing (HTAP) architecture by transparently replicating data across Apache Cassandra nodes without requiring costly and complex extract, transform, and load (ETL) processes to move data between the nodes. This replication allows individual nodes to access data instantly, while Apache Spark and SAI provide analytics and search capabilities.

DataStax Enterprise also provides the following enterprise-grade features, which can help companies modernize their applications:

• Advanced security—DataStax Enterprise provides security features such as unified authentication and role management; integration with Kerberos, Lightweight Directory Access Protocol (LDAP), and Microsoft Active Directory; data auditing that tracks user access to records, in addition to data changes; row-level access control that restricts to which rows of data a user has access; private schemas that restrict schema visibility; role-based auditing; and separation of duties, which can give administrators full database control while restricting data visibility.
• High performance—The Apache Cassandra storage engine in DataStax Enterprise can store multiple data models, such as JSON, tabular data, graph data, and key-value data. The storage engine also avoids reading data before writing, which dramatically reduces read performance latencies and data collisions.
• Distributed high availability—DataStax Enterprise uses the fault-tolerant storage system and fault detection in Apache Cassandra to provide high availability. Redundant data is automatically replicated among Apache Cassandra cluster nodes that can be located across multiple geographic locations. If a node fails, Apache Cassandra replicates the failed node’s data to the failed node’s replacement from neighboring cluster nodes.

• Extensibility—DataStax Enterprise supports a wide range of programming languages that developers can use to create applications, including C/C++, C#, Java, and Python. Stargate, an open-source API framework from DataStax, allows multiple APIs to access Apache Cassandra. DataStax Enterprise also supports creating APIs for databases using REST, gRPC, and GraphQL, which can help drive microservices development.