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.
Обновлено 2024-09-11 01:54:26 +03:00
Обновлено 2023-10-05 07:57:50 +03:00
Utilities to help HBase as a service in HDInsight Azure
Обновлено 2023-08-30 13:02:08 +03:00
Built for //BUILD 2017; this repo contains 15 minutes code challenges across data platform and analytics. Inclusive of; SQL Server on Linux, Azure SQL Database, Azure DocumentDB, Azure Search, HDInsight, MySQL as a Service, PostgreSQL as a Service, Bot Framework, Python Tools for Visual Studio, R Tools for Visual Studio. Get challenged, learn new stuff, fork the repo, contribute to the repo, and get a sneak peak the exciting world of Microsoft products and services.
Обновлено 2023-07-12 01:48:05 +03:00
This repo contains a walkthrough of how to use RServer for HDInsight with large data sets like Criteo.
Обновлено 2023-06-27 16:07:15 +03:00
This is for issue/feedback tracking on HDInsight Visual Studio Extension
Обновлено 2023-06-12 23:46:05 +03:00
Обновлено 2023-03-28 19:48:27 +03:00
.NET driver for Apache Phoenix and Phoenix Query Server
Обновлено 2023-03-28 19:47:57 +03:00
.NET for Apache® Spark™ makes Apache Spark™ easily accessible to .NET developers.
Обновлено 2023-02-18 00:56:32 +03:00
MCW Big data analytics and visualization
Обновлено 2022-07-01 19:47:51 +03:00
A set of example build and release pipelines for deploying Python and Scala to Azure Databricks and HDInsight
Обновлено 2020-06-04 21:46:48 +03:00
Sample data for Microsoft Learn modules for Azure HDInsight
Обновлено 2020-02-21 01:12:12 +03:00
AzureSMR is no longer being actively developed. For ongoing support of Azure in R, see: https://github.com/Azure/AzureR
Обновлено 2019-07-05 20:56:36 +03:00
Fast growing companies rely on data to make decisions that advance profitability. Lots of companies have data that reside both on premises and in cloud. Our observation is that most want to leverage the cloud for possible data congregation routes but lack how best to achieve this using Azure products.
Обновлено 2018-08-10 20:24:11 +03:00
Walkthrough demonstrating how trained DNNs (CNTK and TensorFlow) can be applied to massive image sets in ADLS using PySpark on Azure HDInsight clusters
Обновлено 2017-09-06 22:46:02 +03:00