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Traditional Data Warehouse Architecture: 4 Reasons to Modernize

4 reasons to modernize your data warehouse

 

There’s no doubt–data is your competitive edge. How fast organizations can collect, process, and analyze data will determine who leads the market in the next five years. Your data warehouse plays a critical role. And the traditional data warehouse architecture is feeling the strain in 2019.

 

The limitations of a traditional data warehouse

Traditional on-premises data warehouses, while still fine for some purposes, have their challenges within a modern data architecture. They just aren’t scalable enough or cost-effective to support the petabytes of data we generate. And that amount that will only increase with the Internet of Things and other new sources. 

 

Traditional vs. cloud: What’s the difference?

When it comes to data, the future of data analytics and insight lies in cloud data warehouses like Amazon Redshift, Snowflake, and Google Big Query. All three of these platforms are purpose-built for handling virtually limitless amounts and types of data without breaking a sweat, thanks to infinite scalability and Multiple Parallel Processing. 

 

If you’re thinking of moving from your traditional data warehouse architecture to the cloud, here are four major reasons to do it sooner rather than later. We talk about these benefits and more in our ebook, Modernize your data warehouse.

 

Pay and provision as you need to 

Need to expand your on-prem data warehouse to accommodate your increasing volume, velocity, and types of data? It’ll cost you, in both hardware costs and staff resources. Not only that, but you’ll likely need to go through a lengthy sales and procurement process, at the end of which you’ll be locked into a static contract. These contracts require you to assess your data needs for years down the line. You may not know what your needs are going to be in a month, let alone a year. You end up in a “Goldilocks dilemma,” where you do one of two things:

 

  • Pay upfront for dedicated hardware and software that you may not fully utilize for years
  • Or, underestimate what you need, causing you to go back to your CTO within six months to go through the whole procurement circus again

 

A cloud data warehouse is all about right-sizing to meet your storage and computing requirements. Not just in a year, or a month, but on a moment-by-moment basis. You use what you need, when you need it, and pay as you go. Workloads can be procured and provisioned in a matter of minutes, with no lengthy sales process or restrictive contracts. 

 

Fail fast and be bold 

Another great thing about not being under licensing and hardware constraints is that you can try things. And if those things don’t work, you can keep iterating. Working in a cloud data warehouse and with a product like Matillion enables you to spin up, trial, and run economical proofs of concept. Then you can dismantle and redeploy them just as quickly. You gain new agility and freedom to test new ideas and innovate with less risk. 

 

This agility also enables you to determine the best possible modern data architecture for your enterprise. Finally, it gives you more options when planning for your migration. Try out different IT configurations and iterate, implementing what works and letting go of what doesn’t. 

 

Handle ALL kinds of data

With a traditional on-premises data warehouse, integrating existing operational data with semi-structured and unstructured ‘Big Data’ can be a major technical challenge. Legacy warehouses aren’t designed to consume the latter types of data naturally, leaving you with few good options. 

 

A cloud data warehouse is truly built for every kind of data. It even provides features and functionality designed specifically for the consumption of raw semi-structured data, along with the tools to unravel it. In addition, cloud-native, cloud-deployed tools like Matillion are designed specifically to bring data in all formats into the cloud and leverage the speed and scalability of the cloud data warehouse to transform it. 

 

Hold Big (and Bigger) Data

If you’re trying to scale a traditional data warehouse in the age of “Big Data,” you’re basically trying to make a product do something that it was never meant to do. On-premises data warehouses weren’t designed to anticipate the massive amounts of data we see today–either in speed or scale. Scaling means bumping up both hardware and human resources, which a. you can’t do infinitely, and b. you probably can’t afford (most enterprises can’t). As for speed, the bigger your data is and the more jobs you’re running at once, the greater impact it will have on performance.  Sooner or later, there will be a bottleneck, and it will only get worse over time.

 

In terms of data volume and processing speed, the cloud offers virtually infinite scalability, both vertically speaking (sizing up to accommodate bigger and more complex jobs) and horizontally (the number of jobs you can run at once with multiple parallel processing). Again, scalability goes two ways, up and down. You can scale on demand, and can even automate the process. 

 

Matillion: Helping you move beyond a traditional data warehouse architecture

When you’re ready to modernize, Matillion is purpose-built data transformation for the cloud. You can procure and deploy Matillion directly into your cloud infrastructure. Billed hourly through the marketplace of your choosing, Matillion can support your overall cloud data strategy. 

 

Learn more

To learn more about the benefits of a modern data architecture and get valuable advice on cloud data migration, download Modernize your data warehouse.