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A Flexible and Powerful Parameter Server for large-scale machine learning
machine-learning scala spark model spark-streaming online-learning parameter-server high-dimensional
酷玩 Spark: Spark 源代码解析、Spark 类库等
历史学习项目：Spark 电影推荐、爬虫、Web 与后台。新版持续开发见 cinematch-recommender。
mysql java nginx scala spark hive hadoop spark-streaming scrapy recommendation-system ssm-maven spark-mllib learning-project
.NET for Apache® Spark™ makes Apache Spark™ easily accessible to .NET developers.
microsoft emr machine-learning streaming spark apache-spark csharp fsharp analytics dotnet azure bigdata spark-streaming dotnet-core dotnet-standard databricks spark-sql tpcds hdinsight tpch
scala、spark使用过程中，各种测试用例以及相关资料整理
Wormhole is a SPaaS (Stream Processing as a Service) Platform
C# and F# language binding and extensions to Apache Spark
streaming spark apache-spark csharp fsharp bigdata dataset spark-streaming eventhubs mapreduce dataframe rdd dstream mobius kafka-streaming near-real-time
Streaming System 相关的论文读物
streaming apache-spark storm stream-processing spark-streaming dataflow flink heron drizzle millwheel s4 streaming-engine spe stream-processing-engine
Real Time Analytics and Data Pipelines based on Spark Streaming
workflow lambda streaming real-time scala kafka spark analytics spark-streaming olap hdfs sparksql triggers streaming-data stratio sparta stratio-sparta acctbl-compae
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
python spark faker pyspark spark-streaming data-generation databricks synthetic-data datagen datagenerator deltalake datageneration delta-live-tables
Databricks framework to validate Data Quality of pySpark DataFrames and Tables
spark spark-streaming databricks data-quality data-profiling data-quality-monitoring unity-catalog databricks-cli-installable lakeflow
Scala examples for learning to use Spark
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.
react nodejs docker iot streaming kafka big-data spark apache-spark azure iothub spark-streaming sparksql kafka-streams streaming-data spark-sql servicefabric hdinsight cosmosdb eventhub
Benchmarks for data processing systems: Pathway Live Data Framework, Spark, Flink, Kafka Streams
streaming latency pagerank spark-streaming wordcount benchmark-framework flink kafka-streams streaming-data pathway
Enabling Continuous Data Processing with Apache Spark and Azure Event Hubs
microsoft streaming real-time scala kafka spark apache-spark stream connector azure bigdata apache spark-streaming eventhubs ingestion continuous event-hubs databricks structured-streaming
Spark, Spark Streaming and Spark SQL unit testing strategies
A complete example of a big data application using : Kubernetes (kops/aws), Apache Spark SQL/Streaming/MLib, Apache Flink, Scala, Python, Apache Kafka, Apache Hbase, Apache Parquet, Apache Avro, Apache Storm, Twitter Api, MongoDB, NodeJS, Angular, GraphQL
nodejs python graphql docker machine-learning angular scala kafka big-data apache-spark mongodb hadoop avro twitter-api hbase spark-streaming parquet apache-flink kops spark-sql
Self-contained examples of Apache Spark streaming integrated with Apache Kafka.
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