SQL stream processing, analytics, and management. We decouple storage and compute to offer instant failover, dynamic scaling, speedy bootstrapping, and efficient joins.
-
Updated
May 30, 2024 - Rust
SQL stream processing, analytics, and management. We decouple storage and compute to offer instant failover, dynamic scaling, speedy bootstrapping, and efficient joins.
🏆 Spark4You Design patterns
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
a suite of benchmark applications for distributed data stream processing systems
Sentiment Analysis on streaming data and batch data from Reddit
An open source framework for building data analytic applications.
Structured data streaming using Spark’ s Structured Streaming API ,kafka for data ingestion and cassandra for data storing
This repo contains Big Data Project, its about "Real Time Twitter Sentiment Analysis via Kafka, Spark Streaming, MongoDB and Django Dashboard".
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.
Schema Registry
Data Streaming with Debezium, Kafka, Spark Streaming, Delta Lake, and MinIO
Ophelia a PySpark analytics wrapper.
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 and Next.js for data visualization. as well as a custom built search engine.
Enabling Continuous Data Processing with Apache Spark and Azure Event Hubs
Using various data processing tool for real time data pipeline with Kafka
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
💶Kafka-SparkStreamNLP 是一个基于docker容器化管理的实时金融文本分析平台,通过新闻api,采用 Kafka 进行数据流管理,使用 Spark Streaming 结合微调预训练模型finetuning进行NLP处理,并通过输出流将结果存储在clickhouse以便后续使用可视化平台进行olap分析⭐️⭐️⭐️⭐️⭐️
Add a description, image, and links to the spark-streaming topic page so that developers can more easily learn about it.
To associate your repository with the spark-streaming topic, visit your repo's landing page and select "manage topics."