Performance at the right price is key, says AWS
Paid Feature The cloud has a habit of transforming on-premises technologies that have existed for decades. It absorbed business applications that used to run exclusively on local servers. It embraced the databases they relied on, presenting an alternative to costly proprietary implementations. And it has also driven new efficiencies into one of the most venerable on-premises data analytics technologies of all: the data warehouse. Data warehousing is a huge market. Allied Market Research put it at $21.18bn in 2019, and estimates that it will more than double to $51.18bn in 2028. The projected 10.7 percent CAGR between 2020 and 2028 comes from a raw hunger for data-driven insights that we’ve never seen before. It isn’t as though data warehousing is a new concept. It has been around since the late eighties, when researchers began building systems that funneled operational data through to decision-making systems. They wanted that data to help strategists understand the subtle currents that made a business tick. This product category initially targeted on-premises installations, with big iron servers capable of handling large computing workloads. Many of these systems were designed to scale up, adding more processors connected by proprietary backplanes. They were expensive to buy, complex to operate, and difficult to maintain. The upshot, AWS claims, was that companies found themselves spending a lot on these implementations and not getting enough value in return. As companies produced more data, it became harder for these implementations to keep up. Data volumes exploded, driven not just by the increase in structured records but also by an expansion in data types. Unstructured data, ranging from social media posts to streaming IoT data, has sent storage and processing requirements soaring. Cloud computing evolved around the same time, and AWS argues that it changed data warehousing for the better. Data Warehousing has been popular with customers in sectors like financial services and healthcare, which have been heavy analytics users. But the cloud has opened up the concept to far more companies thanks to lower prices and better performance, according to AWS. Applications previously restricted to multinational banks and academic labs are now open to smaller businesses. For example, you’re able to perform data analytics in the cloud with benefits like scale, elasticity, time to value, cost efficiency and readily available applications. The numbers bear this out. According to Research and Markets, the global market for data warehouse as a service (DWaaS) products will enjoy a 21.7 percent CAGR between 2021 and 2026, growing from $1.7bn to $4.5bn. The largest cloud players have leaped on this trend, with Microsoft offering its Synapse service and Google running BigQuery. AWS announced Redshift as the first cloud data warehouse to address the market in 2012. The idea was pretty simple, AWS told us. The company wanted to give customers a scalable solution, where they could use the flexibility of the cloud to manage data at any scale and velocity while remaining cost effective. Unlike online transaction processing databases like Amazon Aurora, Redshift targets online analytics processing (OLAP), offering support for fast queries thanks to scalable nodes with massive parallel processing (MPP) in a cluster.
