Optimize Data Management with Key Architecture Principles
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These slides are from a webinar that featured a discussion on ways companies can address shifts in big data infrastructure and design the appropriate data management architecture for optimal performance and scale. It covers lessons gleaned from real customer challenges and implementations, offering attendees practical advice on the kinds of design decisions that can optimize the protection and management modern data platforms from Hadoop to NoSQL databases.
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Optimize Data Management with Key Architecture Principles
- 1. Confidential and Proprietary1 Key Architecture & Performance Principles to Optimize Data Management May 18, 2017
- 2. Confidential and Proprietary2 Webinar Goals Outline key architecture principles for backup/recovery & test data management in a modern data world Illustrate the importance of these principles using real customer examples Identify specific tradeoffs that can be made when deploying your data management infrastructure
- 3. Confidential and Proprietary3 Attributes of Modern Data Platforms Scale out to petabyte workloads Analytics-driven Intelligence Storage optimization across diverse data & environments Minimize copies Create storage and compute pools on commodity H/W
- 4. Confidential and Proprietary4 Why Incremental Forever: Backups Traditional Approach Big Data Platform Approach Day 1 Days 2-7 Day 8 Full backup Incremental backups Full backup Incremental backups Full backup Incremental backup Days 9-14 Incremental backups Incremental backups Day 15 Full backup Incremental backup
- 5. Confidential and Proprietary5 Why Incremental Forever: Restores Day 1 2 3 . . . . 80 81 Data Size 1 TB 1.02 TB 1.03 TB . . . . 1.2TB Developer error Changed data from last backup - 50 GB 50 GB . . . . 50GB Backup type Full Incr Incr . . . . Incr Data recovered by traditional approach: 1 TB + 79 x 50 GB = 4.95 TB Data recovered by big data approach: 1.2 TB Key concept: the notion of a “virtualized full” image
- 6. Confidential and Proprietary6 The Importance of Parallelism Test Platform Utility Talena Differential Full backup 8 hours, 17 min 2 hours, 20 min 3.5x Incremental backup 4 hours, 55 minutes 26 min, 7 seconds 11.3x Full restore to different cluster 40 hours, 28 min 14 hours, 55 minutes 2.7x Full restore to same cluster 6 hours, 21 min 1 hour, 58 minutes 2.2x Full restore using incremental restore point to same cluster 21 hours, 28 min 2 hours, 5 min 10.3x Eliminate choke points Tradeoff between backup/restore performance versus production cluster Bandwidth efficiency
- 7. Confidential and Proprietary7 Elastic Scaling: What Are The Issues Multi-DC Cassandra Cluster 100-nodes, 320 TB ARCHIVE Data Center #1 Data Center #2 Cassandra Cluster 50-node, 125 TB Data Center #1 Year 1 Year 2 Topic Key consideration Scaling backup infrastructure Just adding nodes or forklift Agents/listeners Manageability Multi-DC awareness Minimize WAN bandwidth overhead
- 8. Confidential and Proprietary8 The Cloud Effect NoSQL/Hado op/EDW Local Storage Production Cluster Object Storage Cold Storage• Storage tiering • Transparent access • Bandwidth impact
- 9. Confidential and Proprietary9 The Evolution of Data Management THE NEXT 25 YEARS THE TRADITIONAL WORLD Data ManagementData Platforms
- 10. Confidential and Proprietary10 The Talena Architecture • Deep de-duplication and compression with app-aware architecture • Incremental-forever backup architecture • High availability via erasure coding in distributed cluster architecture Smart Storage Optimizer
- 11. Confidential and Proprietary11 The Talena Architecture Native querying and analytics via active compute layer Unbounded scale with a Hadoop-native architecture Smart Storage Optimizer Active Compute Services Distributed File System
- 12. Confidential and Proprietary12 The Talena Architecture • Google-like catalog shortens data recovery time • Automatic schema generation for mirroring and backups • Granular recovery at an object level • Recovery to multiple topologies • Native integration with LDAP and Kerberos for authentication • Role-based access control defines specific privileges • Transparent data encryption • Masking for PII data Smart Storage Optimizer Active Compute Services Distributed File System Metadata Catalog Data Orchestration ServicesSecurity Services
- 13. Confidential and Proprietary13 Smart Storage Optimizer The Talena Architecture GUI CLI API Active Compute Services Distributed File System • ‘Single pane of glass’ for multiple use cases and data platforms • Agentless architecture minimizes management overhead • GUI, CLI, REST-based Talena API options Metadata Catalog Data Orchestration ServicesSecurity Services
- 14. Confidential and Proprietary14 Q&A We’ll send you a link to our architecture white paper Additional resources: talena- inc.com/resources and talena-inc.com/blog Ping us with any additional questions: info@talena- inc.com
Editor's Notes
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- Starting over 20 years ago, the traditional database market became the foundation of enterprise applications. A whole ecosystem of data management products emerged to provide capabilities like backup/recovery (Veritas), storage pooling (Data Domain) test/dev management (Delphix) and Iron Mountain (archiving). But, companies had to purchase separate products to provide a full data management solution for their enterprise. Over the past few years and into the foreseeable future, modern data platforms will become new hubs of enterprise applications. These modern data platforms also need data management capabilities, similar to what happened with traditional databases. (Click for build) Our vision is to help companies with their critical data management needs in a single software product, one that is optimized specifically for these modern Big Data environments.
- The next few slides will introduce the unique Talena architecture and highlight how this architecture delivers on these core business benefits. One of the most significant components of our architecture is our Smart Storage Optimizer. By integrating compute and storage management into our storage optimizer, we’re able to deliver significant cost savings. Our application-aware architecture enables us to do deep de-duplication and compression. Our backup process is incremental-forever, saving on storage costs, and by incorporating erasure coding we also ensure high availability no matter how large a Talena cluster you choose to deploy.
- Supports transparent data encryption in the security services section
- Our agentless architecture makes Talena an ideal solution for big data architectures and minimizes your operational overhead. Furthermore, Talena can support multiple data platforms, versions, and use cases in a single deployment of Talena, thereby providing a “single pane of glass” for all your big data management needs. While most of our clients work within our user interface, we also provide a REST-based API to accomplish the same tasks.