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Alteryx
Alteryx

The first step in enabling self-service data analytics is ensuring that analysts can easily connect to, access, and utilize data from all available sources, including cloud data repositories and big data sources without requiring coding or waiting on other departments. In Alteryx Analytics 10.5, we continue to enable analysts to access data sources from a variety of sources such as Amazon Aurora, as well as prep and blend data directly in Apache Hive and Microsoft Azure SQL Data Warehouse.

 

Microsoft Azure SQL Data Warehouse

In late 2015, we announced a partnership with Microsoft to develop end-to-end integration with their data platforms. Now, with Alteryx Analytics 10.5 we’re excited to introduce full support for Microsoft Azure SQL Data Warehouse. This includes reading from and writing to Azure SQL Data Warehouse, as well as support for in-database (In-DB) workflows, which speed processing times by reducing or eliminating data transfer. Together, we’re providing access to a scalable, elastic, cloud-based data warehouse, compatible with queries across relational and non-relational data.

 

With the release of Alteryx Analytics 10.5, read support and write support via ODBC as well as Bulk write support will be included in the standard Input/Output tools as well as the In-DB tools.

 

Note that connectivity to SQL DW is configured using the SQL Server Native Client:

 

SQL DW is configured using the SQL Server Native Client

 

Bulk write in the standard Output tool is accessed through the SQL Server Bulk Connection option in Alteryx Analytics 10.5:

 

Bulk write in the standard Output tool is accessed through the SQL Server Bulk Connection option in Alteryx Analytics 10.5

 

In-DB Hive

Support for Hive in Alteryx Analytics is not new, but with 10.5 we have added In-DB support for Hive. Since many people using Hive have very large amounts of data, In-DB processing eliminates the need to transfer data into Alteryx and enables analyst to quickly blend and transform their data, and fully leverage the processing power of Hive. In addition, In-DB support makes data residing within Hive accessible to the everyday analysts by eliminating the need to write HiveQL (HQL) when querying. Full read support is offered via ODBC and write support via HDFS (in CSV or Avro formats).

 

Manage In-DB Connections

 

Amazon Aurora

Finally, we are introducing full read support via ODBC for Amazon Aurora with Alteryx Analytics 10.5. Amazon Aurora is Amazon’s relational database that is compatible with MySQL. It combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases, with much faster performance.  This database will be available using the standard Input tool within Alteryx Analytics.

 

We are excited to introduce support for these new data sources in 10.5, we encourage you to join us for a live webinar and see these and the other new features available in Alteryx Analytics 10.5 in our upcoming webinar on May 26th. Also, stay up-to-date about this release by following the Alteryx Analytics 10.5 blog series.

Alex Patten

Alex Patten is the Product Manager for Data Platforms at Alteryx. She is responsible for driving the execution and delivery of product features related to connectors as well as influencing the Alteryx roadmap towards a rich ecosystem of technical integration with emerging data platforms. She has a background in software development and leading teams. She holds a Master’s in Information Systems from the University of Florida.

Alex Patten is the Product Manager for Data Platforms at Alteryx. She is responsible for driving the execution and delivery of product features related to connectors as well as influencing the Alteryx roadmap towards a rich ecosystem of technical integration with emerging data platforms. She has a background in software development and leading teams. She holds a Master’s in Information Systems from the University of Florida.

Comments
Meteor

I tried to connect with In-DB Hive, while the ODBC test connection works, but I got the error message "[Error][28000] [Cloudera] [ThriftExtention] (2) Error occured during authentication. Any thoughts?