Dec

13

2017

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Why WhereScape and Snowflake Are Perfect for Each Other.

By: Jonathan Agee

As you modernize into leveraging the latest and greatest in terms of business intelligence tools, your strategy for managing the data will modernize with it. Snowflake and WhereScape are doing just that with their recently announced integration. They are made for each other, and WhereScape automation for Snowflake is another great step forward for business intelligence toolkits.

WhereScape and Snowflake come together to question the way we’ve been doing things all along and answer some of the problems with handling modern data. They do this by offering a scalable and agile data warehouse engine that can be built quickly and through the tried-and-true automation methods that WhereScape is known for.

Let’s dive into some of the reasons we are excited about WhereScape and Snowflake’s integration.

1. WhereScape Automation is Built to Adapt to New Databases

WhereScape automation for Snowflake proves, once again, that WhereScape is able to adapt and quickly support new types of databases. Now they are leading the charge in natively supporting cloud-based data warehouses. WhereScape’s approach of using easily updated code templates for automation allows the tool to be extended to natively support new cloud database platforms rapidly.

WhereScape is a good fit for these databases and their integration enhances both platforms and their capabilities thanks to their ability to quickly create warehouses and integrate seamlessly with new features from the chosen database.

2. Snowflake’s Cloud-Based Data Warehouse Combines Speed and Flexibility

Snowflake is the first built for the cloud data warehouse. This means it  takes full advantage of the underlying cloud architecture to design a platform that scales the way we expect the cloud to scale while giving us the cost savings we are looking for. The core idea that makes this work is decoupling storage space and computational power. Separating these two pieces of a data warehouse allows your organization to be more adaptive and efficient, while also optimizing the cost around your data warehouse.

Storage Space

Snowflake allows you to scale data storage to meet your needs and only pay for what you use. So if you need to expand your storage, instead of a long install job or forecasting your needs years in advance, you can scale up right away. The ability to scale at will allows organizations to better adapt to the current market, and handle fluctuating volumes of data without waste. In addition, Snowflake leverages AWS S3 which provides massive scalability, comforting redundancy, and extreme speed at one of the lowest possible prices for storage. Scaling storage independently means that companies can use Snowflake to store all kinds of data even if they don’t expect to use it right away.

Compute Power

With all your data sitting in S3 ready to be queried, Snowflake can spin up or down compute resources at will to respond directly to the data warehouse user’s needs. With a parallel processing engine and a dynamic query load balancer Snowflake can add or remove processing power to the same virtual warehouse or even create separate virtual warehouse’s that allow different workloads to remain separate and not impact each other’s performance. This ability to scale allows you to do away with overkill systems that are underutilized outside of peak hours, and removes the need to forecast future needs and the errors that can come with that. Snowflake allows your organization to process, query and use data however, whenever and in whatever volume is needed. And, just like storage you only pay for what you use down to how many seconds your virtual warehouse is running.

3. Changing How You Transform Data

With all your data in Snowflake no one wants to pull it out into a separate server just to do some transformations only to push it right back in. Taking the traditional ETL approach to data transformation is a foolish way to use the power of Snowflake. Instead, leverage Snowflake’s powerful engine to transform the data in place. This new approach is called ELT. Extract and Load once, Transform in place. To do this with traditional tools means writing thousands of lines of custom sql code. Enter WhereScape. From the ground up WhereScape is designed for ELT. With automated code generation that integrates data warehousing best practices the development and maintenance of a data warehouse takes only a fraction of the time it otherwise would. WhereScape is the perfect tool to use for building a data warehouse that fully leverages the power of Snowflake.

The Cloud-Based Automation Dream

WhereScape cuts the time it takes to create and design data warehouses, and Snowflake allows for them to scale and adapt to your needs in ways that weren’t possible with on-site storage solutions.

What excites us the most is that these two tools come together to answer some big pains we face in data warehousing. Increasing flexibility and reducing turnaround time allows for more organizations to get the most value from their data.

WhereScape automation for Snowflake is an exciting next step for business intelligence toolkits.