If you google the term ‘Big Data’ you’ll find countless articles about the massive increases in the volume of data that is collected, and that organizations have to work with. In fact, just this week Walmart talked about how the world’s biggest retailer is in the process of building the world’s biggest private cloud, to process 2.5 petabytes of data every hour.
But no matter how you and your organization define ‘Big Data’, one things is clear – Transferring files, cleansing the dataset, and blending large amounts of data is a time-consuming and resource-intensive task. So, how can you reduce the time it takes to work with big datasets, and thus speed up your time to insight? The answer is to leave the data where it is (in the database or data warehouse) instead of transferring it out, and to utilize in-database tools to perform the tedious work of data blending, preparation, and cleansing.
Leveraging the massive processing power of the database or data warehouse can provide significant performance improvements and deeper insights over traditional approaches, which require data to be moved into a separate environment for processing or force you to only leverage a subset of your entire dataset. In addition, with the movement of data to the cloud, utilizing cloud-based databases or data warehouses further improves your ability to work with very large datasets.
Microsoft Azure SQL Data Warehouse is one of the most popular cloud-based data warehouses because you can deploy a petabyte-scale data warehouse within seconds, leverage best in-class massively parallel processing power, and perform independent scaling of compute and storage in seconds.
But how do you effectively work with all that data, finding just what you need without grinding the process to a halt? That’s where Alteryx comes in. Alteryx enables you to easily perform in-database operations (prep, blend and analyze data) using a repeatable workflow and intuitive drag-and-drop interface. In this video you’ll see how Alteryx can be used for in-database blending with Microsoft Azure SQL Data Warehouse.
To learn more about how you can improve your work with big datasets, check out this whitepaper on In-Database Blending for Big Data Preparation – Using Alteryx with Microsoft Azure SQL DW.
