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Generate relevant synthetic data quickly for your projects. The Databricks Labs synthetic data generator (aka `dbldatagen`) may be used to generate large simulated / synthetic data sets for test, POCs, and other uses in Databricks environments including in Delta Live Tables pipelines
SQL stream processing, analytics, and management. We decouple storage and compute to offer instant failover, dynamic scaling, speedy bootstrapping, and efficient joins.
Real-Time Sentiment Analysis on Twitter Streams is a web application that categorizes tweets into sentiments like Negative, Positive, Neutral, or Irrelevant. Built using Apache Kafka , Spark and PySpark ML models, it offers real-time analysis capabilities.
Data Accelerator for Apache Spark simplifies onboarding to Streaming of Big Data. It offers a rich, easy to use experience to help with creation, editing and management of Spark jobs on Azure HDInsights or Databricks while enabling the full power of the Spark engine.
This is a comprehensive solution for real-time football analytics, leveraging Apache Spark execution on yarn for both streaming and batch processing, Hadoop HDFS for distributed storage, Kafka for real-time data ingestion, rethinkdb for live data updates , a custom built search engine and Next.js for data visualization.
Discover real-time weather analysis through stream and batch processing with Apache Kafka, Apache Spark, and MySQL. This project seamlessly integrates both techniques to compute essential weather metrics, offering valuable insights into weather patterns. Join us in exploring dynamic weather datasets and uncovering actionable insights