The global cloud database and DBaaS market are expected to rise at a CAGR of 14.2% throughout the forecast period, growing from a valuation of US$ 16 Bn in 2022 to US$ 59.5 Bn by 2032. The increased demand for cloud database and DBaaS and self-driving cloud databases and the ability to process low-latency queries are driving the cloud database and DBaaS business.
The introduction of autonomous databases is a primary driver of the DBaaS and cloud database markets. This is due to its machine learning-based capability, which automates standard database activities, including database optimization, security, backups, and upgrades.
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A rise in current workloads as a result of the ongoing COVID-19 epidemic is predicted to promote the cloud database and DBaaS market growth. DBaaS reduces DBA errors, which can have a disastrous impact on an application’s performance, uptime, and security, increasing demand.
Through automation, data analytics has spread across many industries, supporting the expansion of the DBaaS and cloud database markets. This is due to the creation of autonomous data models that use AI and machine learning algorithms to automate provisioning, change management, upgrades, privacy, reliability, performance, and error prevention from start to finish. Cloud computing technology is in high demand because it lowers total infrastructure costs, which is projected to boost the cloud database and DBaaS market expansion throughout the forecast period.
One of the security concerns impeding the expansion of the DBaaS and cloud database markets is a data breach. Due to security flaws, data theft or breaches have financial consequences and have a detrimental influence on an organization’s brand.
As a result, security breaches expose a corporation to a variety of data privacy risks. For example, if a hacker acquires access to personal or private data kept in the cloud, such as a customer’s credit card number or e-mail address, financial losses are very likely. As per the global cloud database and DBaaS market study, because most information is housed online, data theft becomes more critical, harming millions of customers or employees and stifling market progress.
Key Takeaways
- One of the primary factors projected to drive the cloud database and DBaaS market growth is the growing need for self-driving cloud databases.
- The solution component of the cloud database and DBaas market is expected to rise at a CAGR of 14.2% throughout the forecast period.
- The structured query language segment is projected to rise at a CAGR of 13.6% throughout the forecast period.
- North America is predicted to be the most advanced region in terms of cloud database and DBaaS adoption.
BFSI, consumer goods and retail, healthcare, and life sciences, telecom and IT, government, media and entertainment, manufacturing, and energy and utilities are just a few of the main industries that have embraced cloud database and DBaaS solutions and services.
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Competitive Landscape
IBM Corporation, Microsoft Corporation, Alibaba Group Holding Limited, EnterpriseDB Corporation, Oracle Corporation, MongoDB Inc., SAP SE, Redis Labs Ltd., Rackspace Inc., Tencent Holdings Ltd., DataStax Inc., CenturyLink Inc., Amazon Web Services Inc., Teradata Corporation, and Neo4j Inc. are some of the major players in the global cloud database and DBaaS market.
To extend their customer base and achieve a greater market share, players in the cloud database and DBaaS market are focusing on adding new features to their products which is positively impacting the cloud database and DBaaS market adoption trends.
Recent Key Developments
In February of this year, AWS partnered with Lyft. Lyft used AWS services, including machine learning, database, and analytics. Lyft uses Amazon DynamoDB, a high-performance database that helps mission-critical workloads like ride-tracking systems.
Google launched an end-to-end AI platform in April 2019. New features such as AutoML Tables and AutoML Video Intelligence (currently in beta) were added with this release. AutoML Tables allow users to automatically develop a model that predicts the value of a given column by using existing tabular data from Google’s BigQuery database.
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