Why are companies moving to a modern data warehouse?

If you’re on the fence about modernizing your data warehouse, consider this: Data warehouse technology hasn’t changed much in almost 40 years, the equivalent of eons in the fast-paced technology sector. During that time, it’s performed admirably as the centralized repository for enterprise-wide analytics, capable of storing and processing comparatively large and homogenous data sets. Support for strong-typing, enforced constraints, and CRUD (Create, Read, Update, Delete) operations was essential for its wide adoption and ultimate success. But in recent years, the advent of ‘Big Data’ and a voracious appetite for data in general has exposed some technological gaps that legacy warehouses just aren’t equipped to handle.

Massive data volumes require scalability: Enter the modern data warehouse

Organizations are continually looking to new technologies, such as Internet of Things (IoT) devices and log mining, to help answer critical business questions. These sources generate petabytes of data. IDC predicts that the amount of IoT data that is analyzed and used to change business processes in 2025 will reach nearly 80 zettabytes(1). Shortly, legacy warehouses won’t be able to contain the amount of data produced.

Businesses are already anticipating the data deluge—71 percent of enterprises expect investments in data and analytics to increase in the next three years and beyond (2). In the past, you’d react to this increased demand by building out existing infrastructure. The velocity of data growth, combined with long implementation times and exorbitant costs, renders this scalability strategy unsustainable. The static nature of bare metal resources that house legacy warehouses creates a constant tug-of-war between under and overutilization, ultimately leading to poor performance or wasted dollars.

The Hadoop ecosystem was developed to solve some of these problems, but it comes with its own challenges. Adoption demands a large investment in human capital, highly skilled engineers, and technicians to build, scale, and maintain a complex environment. The hodge-podge semblance of bolt-on utilities, each with its own varied levels of support and community interest, creates a maintenance nightmare with no simple way to gauge the larger platform’s efficacy. In navigating these challenges, organizations may find value in engaging a Data Engineer Consultant to provide expertise and guidance in effectively managing and optimizing their Hadoop ecosystem.

Some formats compromise data quality

Web-friendly formats, like JSON, enable applications to easily pass around complex data structures. NoSQL databases were built to natively store these formats, unburdening developers from the strictures of predefined schemas, foreign keys, and enforced constraints. This freedom, however, comes with a significant cost to data consistency and cleanliness, confusing even simple relationships that analytic models depend on to be valuable.

In other words, each of these technologies only solves a specific problem, and at the cost of functionality that has made the data warehouse so successful. Until now.

Usage-based pricing models

Historically, legacy warehouses burdened your IT department with the need to assess and extrapolate your data laboriously needs one, three, or five years down the line. In reality, no business can exactly forecast and predict future data needs in order to scale accordingly. You end up in a 'Goldilocks Dilemma': Paying upfront for dedicated hardware and software that might not be fully utilized for years, or underestimating, sending you back to your CTO within the first six months to go through the painful procurement process all over again. It's a finite solution even if you can forecast your data needs and build the infrastructure to match scale. 

In contrast, a cloud data warehouse can be right-sized on a moment-by-moment basis, ensuring that your costs match your requirements now and in the future, procured and provisions in a matter of minutes. 

A modern data warehouse provides storage for all of your data

The cumbersome process of monitoring disk utilization and purchasing, installing, and configuring new disks on expensive network-attached storage (NAS) appliances is a thing of the past. The cloud data warehouse works with incredibly large volumes in a variety of formats and storage options, solving the problems that arise from increased data demands and complexity, all while simplifying the management of your storage layer. Snowflake has taken a unique approach to this problem by natively separating storage and computing so that each can be managed independently of the other. Amazon Redshift Spectrum and External Data Sources for BigQuery add similar functionality to your cloud data warehouse as an optional feature, giving you the flexibility to mix and match internal and external storage as you see fit.

Next steps

The cloud is redefining how we think about storing and processing data. The power afforded by a modern data warehouse and technology stack can give your business a competitive advantage like never before. You've learned several things to keep in mind as you try out solutions, move to the cloud, and start your cloud-based approach to data analytics. Time to push your business forward and unlock the potential of your data. Change the way you think about your technology and data landscape, and consider engaging a Data Engineer Consultant to maximize the efficiency and effectiveness of your cloud-based data solutions. Download the Modernize your Data Warehouse eBook linked below to save this information and read more about better workload management, comparing on-prem to cloud, and lessons for a successful cloud migration. 

(1) 152,000 Smart Devices Every Minute in 2025: IDC Outlines the Future of Smart Things," Forbes, March 3, 2016
(2) Global State of Enterprise Analytics, 2018," Forbes, August 8, 2018