Backup and Recovery software is a critical component of any organization's data management and security strategy. This type of software is designed to create copies of data, or backups, that can be restored in the event of data loss due to hardware failure, human error, hacking, or natural disasters. The primary goal is to ensure data continuity and minimize downtime, which is vital for maintaining operational efficiency and protecting against financial losses. Key features of Backup and Recovery software typically include automated backup scheduling, which ensures that data is backed up at regular intervals without manual intervention; support for various storage types, including on-premises, cloud-based, and hybrid storage solutions; and encryption capabilities for securing data during transfer and storage. Additionally, this software often provides tools for efficient data recovery that enable businesses to quickly restore lost data from backups with minimal disruption to operations.
| Rank | Skills | Candidates |
|---|---|---|
| 1 | Google Cloud Backup | 331 |
| 2 | Retrospect | 62 |
| 3 | Ownbackup | 48 |
| 4 | Filecloud | 36 |
| 5 | Veeam Backup & Replication | 16 |
| 6 | Fbackup | 15 |
| 7 | R-studio | 15 |
| 8 | Urbackup | 15 |
| 9 | Bacula Enterprise | 13 |
| 10 | Backup Maker | 10 |
| 11 | Handy Backup | 10 |
| 12 | Novabackup | 8 |
| 13 | Veritas Netbackup | 8 |
| 14 | Backupassist | 7 |
| 15 | Backuppc | 7 |
| 16 | Convenient Backup | 7 |
| 17 | Azure Backup | 6 |
| 18 | Commvault | 5 |
| 19 | Box Backup | 4 |
| 20 | Cloudkick Backup | 3 |
Juscimara G.
Skills
This candidate possesses a robust academic foundation in Computer Science, complemented by a Master's degree and ongoing doctoral studies in the same field. With extensive experience in Data Science projects concentrated on Machine Learning applications, they have demonstrated proficiency in a wide array of tools including pandas, numpy, scipy, matplotlib, seaborn, sklearn, pytorch, and various AutoML frameworks such as H2O and TPOT. Current roles include leading AI initiatives focused on vulnerability analysis in information systems and conducting research on imbalanced regression problems. Previous engagements encompass collaborative research on time series and machine learning for renewable energy generation forecasting, as well as academic positions teaching critical computer science subjects, reflecting a strong blend of practical and theoretical expertise.