Apache Spark

Ingest transaction data via Kafka topics.

Apply Spark Structured Streaming to window the data.

Enrich stream with historical user profiles from HBase.

Execute pre-trained MLlib models to score transactions.

Trigger alerts to downstream security systems for scores exceeding threshold.

Load raw FASTQ or BAM files into a Spark cluster.

Use Spark-specific bio-informatics libraries (like ADAM) to process alignments.

Distribute variant calling algorithms across the cluster.

Store results in a Delta Lake table for downstream population-scale analytics.

Connect MQTT brokers to Spark streaming endpoints.

Perform time-series aggregation to calculate moving averages and variance.

Train a regression model using MLlib on historical failure data.

Run inference on live sensor telemetry.

Output maintenance schedules to an ERP system.

Extract logs from web servers and CRM databases.

Perform a large-scale join on anonymized user IDs using Spark SQL.

Run Collaborative Filtering algorithms (ALS) via MLlib.

Generate top-N recommendations for each active user.

Push recommendations to a low-latency NoSQL store for web serving.

Represent the supply chain as a graph using GraphX.

Execute shortest-path and network-flow algorithms.

Integrate real-time weather and traffic data via Spark Streaming.

Re-calculate optimal routes dynamically.

Visualize optimized paths in a corporate dashboard.