CSE Developer Blog

Running Parallel Apache Spark Notebook Workloads On Azure Databricks

This article walks through the development of a technique for running Spark jobs in parallel on Azure Databricks. The technique enabled us to reduce the processing times for JetBlue's reporting threefold while keeping the business logic implementation straight forward. The technique can be re-used for any notebooks-based Spark workload on Azure Databricks.

Permissively-Licensed Named Entity Recognition on the JVM

The ability to correctly identify entities, such as places, people, and organizations, adds a powerful level of natural language understanding to applications. This post introduces a MIT-licensed one-click deployment to Azure for web services that lets developers get started with a wide range of natural language tasks in 5 minutes or less, by consuming simple HTTP services for language identification, tokenization, part-of-speech-tagging and named entity recognition.

Building a Custom Spark Connector for Near Real-Time Speech-to-Text Transcription

This post describes in detail the Azure Cognitive Services speech-to-text WebSocket protocol and shows how to implement the protocol in Java. This enables us to transcribe audio to text in near real-time. We then show how to feed the transcribed radio into a pipeline based on Spark Streaming for further analysis, augmentation, and aggregation. The Java client is reusable across a wide range of text-to-speech scenarios that require time-efficient speech-to-text transcription in more than 10 languages including English, French, Spanish, German and Chinese.

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