The Benefits and Challenges of Hybrid Data Management
Learn why companies opt for hybrid data estates and how observability is key to managing these diverse environments.
Learn the basics of hybrid data environments and why organizations might adopt them
Organizations that manage data across a hybrid environment must integrate on-premise and cloud databases. On-premise databases are installed and run on physical servers within an organization’s facilities, while cloud databases are hosted over the internet on remote servers.
While it may seem simpler to stick to one data environment, many organizations must employ both. The way data is used is often varied and complex, so leveraging both systems’ capabilities can be more resourceful.
For example, the direct control provided by on-premise systems can enable high security, privacy, and regulatory compliance for sensitive information. Meanwhile, cloud systems’ flexibility means your data is accessible anywhere at any time, scaling up or down is easy, and costs are lower.
Hybrid architectures are employed by industries that process diverse data types. Healthcare, for example, can store sensitive patient data on-premise but use cloud systems for patient monitoring devices and telemedicine. Financial services companies may manage regulatory data requirements on-premise while handling big data analytics in the cloud. Educational institutions store students’ grades on-premise but run virtual classrooms in the cloud.
Hybrid data management can be a challenge, given its complexity. EnterpriseDB (EDB) simplifies operations by providing a single intuitive interface with intelligent observability in diverse data environments.
How each data environment helps meet critical business demands
On-premise and cloud databases have their respective strengths when addressing business needs.
On-premise databases provide reliability, stability, and direct control.
- Data security and compliance
Security protocols are determined, customized, and managed directly by the organization, allowing strict adherence to regulatory requirements. Your data isn’t stored in a shared space that strangers can access and it doesn’t need to move across networks, risking exposure during transit. If handling highly-sensitive information, you could also have on-site security personnel to protect the data centers and IT teams to address incidents in real-time.
- Integration with legacy systems
On-premise systems are easy to integrate with legacy systems, as there are fewer compatibility issues and less need for complicated data transformation. Many legacy systems are, in fact, crafted for on-site operations. You can also ensure that security policies remain consistent between the two systems. Despite the initial investment and maintenance costs, on-premise options can be more practical if your company has already spent a lot on legacy software and infrastructure.
Cloud databases provide agility, scalability, and cost-effectiveness.
- Scalability and performance
Cloud systems are highly flexible, making it easy to scale your operations up or down and tune performance as needed. Auto-scaling options automatically adjust resources in real-time based on demand, while applications and services can be rapidly deployed to address changing needs and requirements. Cloud systems also have performance optimization features such as content delivery networks, caching solutions, and load balancing. Enhanced performance can also be achieved through integrated analytics and AI capabilities that offer advanced data processing and insights.
- Cost-effectiveness
Cloud systems’ remote, flexible nature cuts down on costs significantly. You don’t need to spend money on physical infrastructure, including recurring energy bills, rent and the replacement or upgrading of outdated equipment. There’s no need to hire on-site staff for maintenance either. Many cloud services are also on a pay-as-you-go model, letting you pay only for the resources you use. They also provide high availability features such as redundancy and failover mechanisms, preventing pricey outages and breakdowns.
How each data environment helps meet critical business demands
On-premise and cloud databases have their respective strengths when addressing business needs.
On-premise databases provide reliability, stability, and direct control.
- Data security and compliance
Security protocols are determined, customized, and managed directly by the organization, allowing strict adherence to regulatory requirements. Your data isn’t stored in a shared space that strangers can access and it doesn’t need to move across networks, risking exposure during transit. If handling highly-sensitive information, you could also have on-site security personnel to protect the data centers and IT teams to address incidents in real-time. - Integration with legacy systems
On-premise systems are easy to integrate with legacy systems, as there are fewer compatibility issues and less need for complicated data transformation. Many legacy systems are, in fact, crafted for on-site operations. You can also ensure that security policies remain consistent between the two systems. Despite the initial investment and maintenance costs, on-premise options can be more practical if your company has already spent a lot on legacy software and infrastructure.
Cloud databases provide agility, scalability, and cost-effectiveness.
- Scalability and performance
Cloud systems are highly flexible, making it easy to scale your operations up or down and tune performance as needed. Auto-scaling options automatically adjust resources in real-time based on demand, while applications and services can be rapidly deployed to address changing needs and requirements. Cloud systems also have performance optimization features such as content delivery networks, caching solutions, and load balancing. Enhanced performance can also be achieved through integrated analytics and AI capabilities that offer advanced data processing and insights. - Cost-effectiveness
Cloud systems’ remote, flexible nature cuts down on costs significantly. You don’t need to spend money on physical infrastructure, including recurring energy bills, rent and the replacement or upgrading of outdated equipment. There’s no need to hire on-site staff for maintenance either. Many cloud services are also on a pay-as-you-go model, letting you pay only for the resources you use. They also provide high availability features such as redundancy and failover mechanisms, preventing pricey outages and breakdowns.
What makes managing data on disparate systems a tough task
Data sprawl – the large influx of data received across a variety of platforms and structures – is the main challenge of hybrid data estates. Managing these disparate systems becomes increasingly difficult and costly, particularly when it comes to ensuring uptime, security, and compliance.
It can also be an operational nightmare. Hybrid estates call for complex architecture, which requires skills, training, and tools to ensure data quality. It’s no easy task handling the myriad of interfaces and tooling on top of learning the unique skills required to use them.
Given these issues, you may not be able to harness the total value of your organization’s data. This is a blow to your business, as well-leveraged data allows for an intensive market understanding and sharp decision-making. Streamlining observability into your data can result in operational efficiency, improved customer satisfaction, and increased revenue.
Making full use of your data despite complications requires the right tools and expertise, so operating on a platform built for hybrid strategies is key to unlocking those benefits. In an ideal world, your platform enables you to analyze performance and see diagnostics across your entire estate, regardless of your applications’ environments.
Simplify hybrid data management with a single, intuitive interface offering intelligent observability into diverse data environments.
It’s critical to operate on a platform that supports the hybrid approach. EDB Postgres AI fits the bill: an intelligent platform for transactional, analytical, and AI workloads that can be deployed as a cloud service or software for on-premises systems, all powered by the same enhanced Postgres engine. It is hybrid by design to support customers that are increasingly intentional about where and how they deploy.
What makes it best for hybrid data management is that it offers advanced observability into diverse data environments through a single interface. Seeing everything at a glance at any time simplifies and speeds up operations and administration. You can ensure security, uptime, compliance, and governance for new and growing Postgres deployments.
The platform’s key features include:
- Single pane of glass
Deploy and manage your Postgres databases using one universal interface. - Intelligent optimization recommendations
Receive automatic suggestions on how to improve performance. - Performance diagnostics
Analyze database performance and see diagnostics (including metrics on queries users, and wait events)
Data Management and Observability – Content and Resources
Additional information on supervising hybrid data estates
Learn the specific challenges of going hybrid and the outcomes you can deliver with EDB
How did a key tech milestone in the ‘60s transform today’s data estate management for the better?
EDB Postgres AI ushers in a new era for EDB and the industry. What are this platform’s key capabilities for data estate management?
This database is installed and run on physical servers within an organization’s facilities. Its benefits include full control over policies and configurations, strict security, and extensive customization.
This is a database hosted over the internet on remote servers. Its benefits include increased performance, improved scalability, greater agility, and reduced costs.
- Virtual machine
PostgreSQL can be installed on virtual machines in cloud environments, providing more control over the database server and its environment. This suits users who require custom configurations that are not supported by fully managed services. - Hybrid
PostgreSQL can be deployed in a hybrid environment, with some components managed on-premises and others in the cloud. This can be useful for organizations that must keep sensitive data on-premises for compliance reasons while still benefiting from the cloud’s scalability. - Containerized
PostgreSQL can be deployed in containerized environments using Kubernetes, which provides high availability, failover and other cluster operations. EDB Postgres Distributed for Kubernetes is one example where PostgreSQL is deployed using a Kubernetes operator. - Database as a Service (DBaaS)
These fully managed services handle most database management tasks such as installation, maintenance and upgrades. This allows users to focus more on their applications than on database operations. Examples include EDB BigAnimal, Amazon RDS for PostgreSQL, and Azure Database for PostgreSQL.
The choice of cloud model for hosting PostgreSQL databases depends on your organization's specific needs and goals. The advantages of each of the five models are as follows:
- Private cloud (on-premise or remote)
Private clouds allow organizations to customize their infrastructure to meet specific performance, security, or regulatory needs. They provide enhanced security since resources are kept private, which is crucial for handling sensitive data or strict compliance. Costs are also more predictable, fixed based on capacity than variable usage, which helps with budgeting and planning. - Public cloud
Public clouds offer scalability, allowing PostgreSQL databases to quickly scale up or down based on demand. They're cost-effective for variable workloads with a pay-as-you-go model and provide access to a broad ecosystem of integrated services and innovative tools to enhance database functionality. - Hybrid cloud
Hybrid clouds combine the advantages of both private and public clouds, keeping sensitive data on-premises while leveraging public cloud scalability for non-sensitive applications. This mitigates single points of failure, ensures higher availability, and helps meet regulatory requirements. - Multi-cloud
Using multiple clouds can prevent vendor lock-in, allowing organizations to choose the best services and pricing from different providers. This optimizes operations, reduces downtime risks, and enhances business resilience by not relying on a single cloud provider. - Polycloud
Polycloud lets organizations use specialized services from different providers when their unique capabilities are best for specific PostgreSQL management tasks. This harnesses each provider's strengths for efficiency, performance and a competitive edge. For instance, one cloud could gather and process IoT data while another handles complex analytics.
PostgreSQL cloud databases can match or surpass on-premise deployments depending on the configuration and the cloud provider's infrastructure.
Cloud-based deployments allow developers to dynamically adjust performance settings and scale resources to optimize application performance. However, they remain vulnerable to network connectivity and multi-tenant workloads, which can impact performance.
To ensure a successful migration, developers should assess their performance needs, evaluate the provider's SLAs and infrastructure and thoroughly test the chosen configuration.
This management approach integrates on-premise and cloud databases so that the organization benefits from their respective strengths. It involves handling diverse data structures and knowing which data environment is better for which need. For example, sensitive or regulated data is best kept on-premises, while less critical data is fit for the cloud. Large volumes of data not frequently accessed can be left in the cloud, while more frequently accessed data can be kept on-premises.
This is the ability to monitor and gain insight into data systems for their accuracy, reliability and usefulness. Observability is more comprehensive than passive surveillance. It includes identifying and addressing issues like missing values, recording the history and context of data, setting up alerts for data anomalies and measuring the performance of data pipelines, among other capabilities.
There are different KPIs for observability depending on your organization’s needs. Some of these indicators are response time, memory usage, requests served, latency, error rates, CPU capacity, and peak load.
This platform consolidates data from multiple sources to provide one clear overview of an organization’s systems and processes. It also merges monitoring, alerting, and management functions in one place. Because it is a single platform, you don’t need to switch between dashboards, making workflows quick and efficient. Getting the bigger, real-time picture of your operations helps you make informed decisions and implement speedy resolutions to issues.
EDB Postgres AI is extremely versatile. It can handle transactional, analytical, and new AI workloads deployed as a cloud-managed service, self-managed software, or physical appliance all through a single pane of glass. You can analyze database performance, see diagnostics (including metrics on queries, users and wait events), and get automatic suggestions on improving performance regardless of data environment.
Unified management and observability are achieved via the EDB Postgres AI Agent, which enables users to connect self-managed deployments to the EDB Postgres AI Console. Organizations get visibility of their self-managed Postgres databases, Amazon RDS Postgres databases and EDB Postgres AI Cloud Service databases from a single pane of glass, where they can observe metadata including OS and PG versions.
Conquer data sprawl, streamline operations, and enhance analyses
Complex data needs call for simple solutions. Get a clear view and full control of your on-premise and cloud databases through a single pane of glass. Harness full observability to mine richer insights and make faster decisions across multiple data environments.