Drive Business Value and Continuity with Intelligent, Automated Cloud-Native Data Management

The current economic situation is driving businesses to lower their total cost of ownership, achieve operational efficiency, and increase productivity. In recent surveys done by PWC and Forbes, 77% of CEOs say their primary focus to drive revenue growth is to create operational efficiency, and 82% of CXOs say that cloud-based tools help businesses execute faster. Not surprisingly, many enterprises are rapidly moving their data and analytics infrastructure to the cloud as they embark on their digital transformation journey. They want to accelerate time to value, reduce costs, improve efficiency, and deliver trusted business decision-making insights.

There is a significant technology shift as organizations select cloud-native applications, cloud-native analytics, cloud computing databases, storage, and messaging across the stack. The cloud’s promise is being realized by organizations selecting cloud-native offerings that can help them realize their objectives.

Benefits of modernizing your data integration for cloud data warehouses and data lakes

Organizations today are either building new data warehouses or data lakes in the cloud or modernizing or consolidating on-premises data warehouses in the cloud to support their cloud analytics initiatives. In a recent TDWI survey, close to one-third of respondents reported already utilizing the cloud for their data warehouse, and the numbers will double in the next few years. There are numerous reasons for moving to the cloud, such as:

  • Scalable and elastic approach to data management needs
  • Separating compute from storage for high performance
  • Automated provisioning and management
  • Leapfrog cloud analytics and AI/ML use cases
  • Democratize data for data science and self-service analytics

But moving to a cloud data warehouse or a data lake has its own set of challenges. Customers are shifting their databases to the cloud and workloads from legacy systems, analytics, and visualization. So, one significant issue is data integration.

Many customers tend to use manual approaches such as hand coding to solve their data management and data integration challenges for their business use cases. Hand-coded data integration may initially appeal to IT departments. They think it is an easier path and less expensive for building data pipelines than procuring a data integration tool. But hand-coding is expensive, not future proof, lacks automation, and can’t offer enterprise breadth for data integration.

An intelligent and automated cloud-native data management solution is the answer to enable organizations to get maximum benefits of modernizing analytics in the cloud and unleash the full potential of cloud data warehouses and data lakes across a multi-cloud environment. It can help organizations improve operational efficiency, increase productivity, and lower total cost of ownership with best-in-class data integration, data quality and governance, and metadata management.

Design, build, and deploy cloud-native serverless data management pipelines

In the upcoming virtual fall launch, we have enhanced several cloud-native and advanced serverless data management capabilities to help you jumpstart your cloud data integration projects. Cloud-native and advanced serverless data management enables customers to leverage serverless computing to process data integration pipelines. It eliminates the need to manage hardware or software to simplify DevOps and DataOps, allowing developers to focus on business logic and deploy new data pipelines quickly. There’s no need to provision servers or clusters, and you have the ability to handle spikes with auto-scaling. Lower your TCO with no startup costs and usage-based pricing.

To learn more, join our fall virtual launch event (register for the event in your geo: North America, EMEA, or APJ) to hear how you can Accelerate Digital Transformation in a Data 4.0 World with Intelligence and Automation – and see the latest innovations for AI-powered cloud-native data management.