Unified data processing under Spark: the end of technical silos

Databricks announces the general availability of Real-Time Mode (RTM) for its Spark Declarative Pipelines (SDP), a major evolution that unifies batch processing, ETL, and low-latency streaming within a single framework: Apache Spark. According to Databricks, this integration eliminates the need for specialized engines like Apache Flink for real-time operational use cases. RTM is presented as an evolution of the Spark Structured Streaming engine, now offering comparable, or even superior, performance to dedicated solutions.

The announcement highlights that RTM enables end-to-end latencies on the order of milliseconds for operational streaming workloads. Databricks cites an improvement of up to 92% in speed compared to engines like Flink, while maintaining the advantages of declarative pipelines (SDP), such as automatic dependency management, incremental processing, or fault recovery.

Real-Time Mode eliminates the need for specialized engines like Apache Flink while offering end-to-end latencies in the millisecond range, a first for Spark pipelines.

Critical performance for demanding use cases: fraud, alerts, and real-time features

RTM on SDP targets scenarios where responsiveness is crucial. Databricks illustrates its potential with applications such as fraud detection, real-time alerts, or the instant updating of features in operational stores. For example, the Databricks Feature Store uses this architecture to achieve a p99 latency of 200 milliseconds from Kafka to the online feature store, a rare level of performance in critical environments.

Another innovation lies in the ability to write features directly into operational stores like Lakebase or Redis, via ForEach sinks. This enables versionless updates and low-downtime maintenance, assets for systems requiring constant availability.

What this changes here: opportunities for Beninese and West African companies requiring agility

For businesses and administrations in Benin and West Africa, where cybersecurity challenges, financial transaction management, or critical infrastructure monitoring are multiplying, this advancement could transform how data is processed in real time. For example, financial institutions could strengthen their fraud detection systems by reducing analysis delays to just milliseconds, thereby limiting losses from suspicious transactions.

Telecommunications operators or mobile payment platforms, facing massive transaction volumes, could optimize their processing pipelines without relying on complex technical stacks. Similarly, public administrations, in their pursuit of more responsive public services (such as digital identity management or electrical grid monitoring), could benefit from reduced latency for real-time alerts or updates.

Finally, native integration with tools like Redis or Lakebase could simplify local system architectures, reducing maintenance costs and downtime. However, its adoption will depend on the maturity of cloud infrastructures and the training of technical teams on declarative pipelines and Spark best practices.

An architecture that simplifies without compromising performance

One of RTM’s key strengths lies in its ability to simplify architectures while maintaining high performance. SDP automatically handles complex tasks such as resolving pipeline dependencies, applying data quality rules, or orchestrating workflows. This reduces the workload of technical teams, who no longer need to manually configure tools like Flink for critical latency requirements.

Databricks highlights that RTM was tested by clients last year, enabling them to process streaming data up to 100 times faster than traditional micro-batch architectures. This scalability could be particularly useful for West African companies whose data volumes are exploding with accelerated digitalization (mobile banking, e-commerce, industrial IoT).