• Business decisions are based on stale data
• Fragmented data increases the risk of errors
• Multiple copies of data increases storage costs

These issues have driven many progressive, data-centric enterprises to re-architect their operational reporting platforms. In this new architecture, Kafka serves as a persistent message bus that ingests data from relational stores and provides failure resiliency and message buffering. Spark or Spark Streaming may be used for transformation, aggregation, and other lightweight stream processing. These processing nodes may be deployed in Docker containers. Finally, Apache Cassandra serves as a distributed data storage layer that provides linear scalability and high-availability. Kafka also enables the ability to add multiple data consumers such as Hadoop/HBase through Flafka.
However, as enterprises adopt operational reporting platforms, they must ensure that bad data feeds and human error do not result in data loss. Apache Cassandra natively replicates data but does not provide the capability to go back in time to recover data efficiently and at scale. That’s where Datos IO RecoverX comes in. Datos IO RecoverX, a scale-out data protection software, is an important element of this next-generation distributed data services architecture.

As enterprises onboard their critical applications to achieve development agility, scalability, and lower operating costs, they are also investing to protect their next-generation applications from data loss, and rightly so. If you are building your next-generation infrastructure around distributed databases, please get in touch with our experts who can guide you through different techniques to ensure data protection.