| title | description | services | documentationcenter | author | manager | editor | ms.assetid | ms.service | ms.workload | ms.tgt_pltfrm | ms.devlang | ms.topic | ms.date | ms.author |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Explore data in the Team Data Science Process | Microsoft Docs |
How to explore data in various storage environments. |
machine-learning,storage |
bradsev |
jhubbard |
cgronlun |
6eae8772-f479-4627-bb12-64f6d1440b22 |
machine-learning |
data-services |
na |
na |
article |
12/09/2016 |
bradsev |
This document covers how to explore data in four different storage environments that are typically used in the Data Science Process:
- Azure blob container data is explored using the Pandas Python package.
- SQL Server data is explored by using SQL and by using a programming language like Python.
- Hive table data is explored using Hive queries.
- Azure Machine Learning (AML) Studio data is explored using AML modules.
The following menu links to the topics that describe how to use these tools to explore data from various storage environments.
[!INCLUDE cap-explore-data-selector]