WHAT WE DO
Database Management Services:
Infrastructure planning, database installations, database maintenance, upgrading and patching
Disaster Recovery Services:
Review databases to identify potential risk scenarios and implement disaster recovery architecture
Monitor, assess and optimize your databases for performance, stability and availability.
Database Migration Services:
Support cross-platform, version and OS migrations.
Big Data Services
Analysis, architecture and design, solution development (DWH, Data lake, ETL/ELT), Big data software updates, Data management (Cleaning, backup and recovery), Performance monitoring and Troubleshooting.
Cloud Database Management
Elastic data storage, Replication, ACID transactions, Change data capture, Auto-scaling, Data encryption, IAM, backup and Point-in-recovery.
Comprehensive Backup Plan
DBAs need to make a comprehensive backup plan for databases for which they are accountable. The backup plan should include all types of RDBMS within the enterprise and should decide what needs to be backed up, appropriate backup type to use for the data, where to store backups and backup retention policy. For Oracle databases, we put tablespaces in backup mode and backup the associated data files using OS copy command or RMAN. It is important to review the RMAN compatibility matrix for the database. the DBA can reduce the backup window for VLDBs by allocating multiple channels and fine-tuning backups, can save disk space by using compressed backups, and can block tracking with incremental backup techniques with the latest versions. The DBA must review the version and edition of the database to confirm availability of this option. Alternatively, the DBA can consider setting up split mirror backups. For SQL Server, the DBA can partition the database among multiple files and use the file or filegroup backup strategy. Using multiple backup devices in SQL Server allows backups to be written to all devices in parallel. It is good practice to select a backup window at a point when the lowest amount of activity affects the database so that the backup does not reduce available database server resources and slow down the database user’s activity. The DBA can tune the backup window by parallelizing backups using multiple channels however, the DBA must review the version and edition of the database to confirm availability of this option. In most of the cases, it is best to set up a weekly backup cycle starting with full backups and then incremental/differential backups. Archive/transaction log backups can be scheduled for every few hours, depending on the volatility of the database.
Setting Business Goals for smooth running of Databases
It is important for an organization to know how much it’s willing to spend on its database management, which encompasses the specific types of databases (Oracle, SQL Server, NoSQL, MySQL, etc.), the number of staff and level of expertise on-hand, and the specific deployment strategy that’s best-suited for enterprise databases. We ask our customers to strategically designate how much it’s willing to invest in its database management team. (In-house, Consultant, Cloud) Depending on the size of the company and the amount of data, a DBA may only specialize in one or two areas of managing and optimizing your databases. It is crucial that an organization’s IT department isn’t spread too thin and to be careful of the accidental DBA. It’s vital for DBAs to know what the plan is with the data that’s being collected and to focus solely on the information that’s relevant towards the overall business goals. This will guarantee the organization’s database stays well-organized and continues to run smoothly without becoming overcrowded with data that is obsolete. Knowing your organization’s business goals helps to keep only the data that is useful to your organization, in turn simplifying the upkeep and management of your databases.
Cloud operating model for Big Data Implementation
A well planned public-private cloud provisioning and security strategy plays an integral role in supporting ever changing requirements. The advantage of a public cloud is that it can be provisioned and scaled up instantly. In those cases where the sensitivity of the data allows quick prototyping, this can be very effective. With the flexibility of cloud, our data scientists construct data experiments and prototypes using their preferred languages and programming environments. Only after a successful proof of concept, we systematically reprogram and/or reconfigure these implementations. This approach is especially beneficial considering the ever-changing landscape of big data technologies. We create analytical sandboxes on-demand and manage resource needs to have a control of the entire data flow, from pre-processing, integration, in-database summarization, post-processing, and analytical modeling.