Data Management
Modernising Your Infrastructure for the Data-Driven Era
In an era defined by data, organisations need more than just storage. They need insight, agility, and security. Google Cloud Data Management helps you modernise infrastructure, simplify data operations, and ensure your data is protected and accessible, so you can make smarter decisions and drive innovation with confidence.
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The Data Management – Services Specialisation have demonstrated proven success in helping customers design, build, and manage data workloads on Google Cloud. Whether it's migrating from on-premises, private cloud or other public cloud platforms, or even starting fresh with greenfield deployments, be equipped with seamless, scalable, and secure solutions.
This specialisation covers Google Cloud’s core data services, including: Cloud SQL, Spanner, AlloyDB for PostgreSQL, Bigtable, Firestore, Firebase, and Memorystore. Solutions involving Bare Metal for Oracle workloads are also in scope.
Benefits
Faster Time to Value
Accelerate transformation using Google Cloud's proven frameworks and automations.
Enhanced Security and Compliance
Protected by default encryption, enterprise security, and real-time monitoring.
Cost Optimisation
Pay only for what you use with scalable storage and compute resources.
Improved Agility
Develop applications and deploy emerging technologies without infrastructure constraints.
Data-Driven Innovation
Experiment and innovate by leveraging the power of Google Cloud AI, ML, and advanced analytics.
Our Solutions
Your data matters, and at Awantec, we make its protection a top priority.
We use a Lift and Shift approach through Google Cloud to move your workloads to the cloud smoothly and with minimal disruption. By leveraging Google Cloud’s scalable tools and infrastructure, we help you migrate quickly while keeping performance steady.
Assess and Strategise
Seamless Data Migration and Integration
Unified Data Management and Governance
Analytics and AI-Driven Insights
Security, Backup, and Disaster Recovery
Case Study:
Public University
Challenges Faced
Performance bottlenecks
The old system struggled to handle heavy workloads, leading to slow performance.
Limited scalability
The system couldn't easily grow to accommodate more users or data.
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Increased vulnerability during peak usage
The system was prone to issues when many users were active, impacting service reliability and the experience for students on the Online Digital Learning (ODL) platform.
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Custom export/import processes
Data was extracted from the old system and loaded into the new one using specifically designed procedures.
Scripting to ensure data integrity and business continuity
Automated scripts were used to make sure data remained accurate and that operations could continue without interruption during the migration.
Real-time data replication
Data changes in the old system were instantly copied to the new one, allowing for a smooth transition with no downtime or data loss.
How Data was Collected
Type and Method of Data Management
Type: Lift-and-optimize approach
The existing system was moved to the cloud, and then improved for better security and continuous availability.
Method: Custom export/import, scripting, and real-time data replication
These were the specific techniques used to move and synchronise the data.
Testimonial
To modernise our legacy system, we migrated to Google Cloud SQL with High Availability and autoscaling. This move significantly improved our system's performance, security, and uptime—especially critical for platforms like Online Digital Learning. With real-time replication, the transition was seamless, allowing us to innovate more effectively while reducing operational complexity."
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Public University
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Case Study:
Government Agency
Challenges Faced
High operational costs
Running the old system was expensive.
Limited scalability
The system couldn't easily expand to meet growing demands.
System reliability issues
The old system was prone to failures.
Inefficiency of the legacy on-premises system to meet evolving demands
The outdated system couldn't keep up with the changing needs of the organisation.
System downtime risks and maintenance burdens
There was a high risk of the system being unavailable, and maintaining it required a lot of effort
How Data was Collected
Structured migration of on-premise MySQL databases to Cloud SQL
Data from the existing MySQL databases on the company's servers was systematically moved to Google Cloud's managed SQL service.
Type and Method of Data Management
Type: Transformational migration
This wasn't just moving data; it was about redesigning and optimising it to work efficiently in a cloud environment.
Method: Structured migration of on-premise MySQL databases to Cloud SQL and implementation of Redis MemoryStore
The specific actions involved moving the databases to Cloud SQL and adding Redis MemoryStore for extremely fast data access in memory.
Testimonial
The successful transition has strengthened our confidence in cloud computing, especially with Google Cloud Platform. We truly value the seamless migration experience and are excited to explore more cloud solutions in the near future.”
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Government Agency
