Database Management
Proactive Database Management and Performance Optimization
What We Do
Database Management Services:
Tasks include infrastructure planning, installing databases, maintaining them, and managing upgrades and patches.
​
Disaster Recovery Services:
Reviewing databases to identify potential risk scenarios and implementing disaster recovery architecture.
​
Performance Monitoring:
Monitoring, assessing, and optimizing databases for performance, stability, and availability.
​
Database Migration Services:
Provide support for migrations across different platforms, versions, and operating systems.​
​
Big Data Services
Tasks include analysis, architecture, and design, developing solutions (such as Data Warehouses, Data Lakes, ETL/ELT processes), updating Big Data software, managing data (including cleaning, backup, and recovery), monitoring performance, and troubleshooting issues.​
​
Cloud Database Management
Features include elastic data storage, replication, ACID transactions, change data capture, auto-scaling, data encryption, IAM (Identity and Access Management), backup, and point-in-recovery.
Benefits
Tiered database services:
It tackles all conceivable database issues and delivers utmost value to the customer.
​
Proactive Database Management:
Ensure databases remain healthy, accessible, regularly backed up, and secure.
​
Advanced Database Tuning:
Enable enhanced performance and accelerated innovation through advanced troubleshooting techniques.
​
Rapid import-export process
Reduce data loss and minimize downtime during your database migration.​
​
Keep pace with Advancements
Our collaborations with top-tier technology leaders such as AWS, Microsoft, and Oracle empower us to stay at the forefront of technological advancements.
​
Flexible Cloud based DB platforms
Querying big data with automated infrastructure provisioning, encryption of data in transit and at rest, security at both row and column levels, performance monitoring via CloudWatch, continuous backups, and database access control through Cloud IAM.
COMPREHENSIVE BACKUP PLAN
Database Administrators (DBAs) are responsible for creating a comprehensive backup plan for all databases under their purview. This plan should encompass all types of Relational Database Management Systems (RDBMS) used across the enterprise and include decisions on what data needs to be backed up, the appropriate backup types, storage locations for backups, and the backup retention policy.
​
For Oracle databases, DBAs typically place tablespaces in backup mode and back up associated data files using either OS copy commands or Oracle Recovery Manager (RMAN). It's crucial to review the RMAN compatibility matrix specific to the Oracle database version in use. DBAs can optimize backup performance for Very Large Databases (VLDBs) by allocating multiple channels and fine-tuning backups. Using compressed backups helps conserve disk space, and implementing incremental backup techniques further reduces backup times. DBAs should verify the database version and edition to ensure compatibility with these options. Alternatively, they may consider implementing split mirror backups for added redundancy.
​
In SQL Server environments, DBAs can enhance backup efficiency by partitioning databases across multiple files and adopting a file or filegroup backup strategy. Using multiple backup devices enables parallel writing of backups to enhance performance. DBAs should schedule backup windows during periods of low database activity to minimize impact on server resources and user activity. They can further optimize backup times by parallelizing backups through multiple channels, contingent upon database version and edition compatibility.
​
A recommended backup strategy typically involves initiating weekly full backups, followed by incremental or differential backups. Archive and transaction log backups are scheduled more frequently, depending on the database's volatility and transaction rates.
Overall, DBAs play a critical role in ensuring robust backup strategies are in place, leveraging advanced techniques and tools to safeguard data integrity and availability across enterprise databases.
SETTING BUSINESS GOALS FOR SMOOTH RUNNING OF DATABASES
Understanding the financial commitment to database management is crucial for organizations, encompassing considerations such as the types of databases (Oracle, SQL Server, NoSQL, MySQL, etc.), the size of the database management team, their expertise levels, and the optimal deployment strategy (in-house, consultant, or cloud-based).
​
When engaging with our customers, we encourage strategic allocation of resources towards their database management team. Depending on company size and data volume, a Database Administrator (DBA) may specialize in specific areas like Oracle or SQL Server management. It's essential for the IT department to avoid overextending resources and to be wary of relying on accidental DBAs—individuals not fully trained for the role but tasked with database management.
​
A clear understanding of the organization's data usage plan is critical. DBAs should focus solely on data relevant to business goals, ensuring databases remain organized and efficient. This approach avoids unnecessary data accumulation, simplifying database upkeep and management. Aligning database management practices with organizational objectives ensures that only pertinent data is retained, facilitating streamlined operations and enhancing overall database performance.
CLOUD OPERATING MODEL FOR BIG DATA IMPLEMENTATION
A well-planned strategy for provisioning and securing public-private cloud environments is essential to meet dynamic business requirements effectively. The primary advantage of leveraging a public cloud is its ability to provision and scale resources instantly. This agility is particularly advantageous for scenarios where data sensitivity permits rapid prototyping and experimentation.
​
Within our organization, the flexibility of the cloud empowers our data scientists to conduct data experiments and develop prototypes using their preferred programming languages and environments. Once a proof of concept is successful, we methodically reprogram or reconfigure these implementations for production readiness. This iterative approach is crucial given the rapidly evolving landscape of big data technologies.
​
We facilitate the creation of analytical sandboxes on-demand and carefully manage resource allocation throughout the entire data lifecycle. This includes pre-processing, integration, in-database summarization, post-processing, and analytical modeling. By maintaining control over these stages, we ensure efficient data flow and support robust analytical insights.