Presentation Transcript

1. RainStor and Dell DX:Online Structured Data Retention for Data Center Consolidation

   Presentation for AFPOA, August 26th, 2011 Ramon.chen@rainstor.com – VP Product Management, RainStor Craig_Warthen@dell.com – Product Marketing, Product Group, Storage
2. RainStor + Dell DX Archival solution for Reduction, Retention & on-demand Retrieval of historical (semi) structured data and big machine generated data at 10x Less TCO
3. Unified Platform for Data Consolidation Enterprise Information Archiving (EIA) will become a key infrastructure component by 2013, as the archiving of structured data and unstructured content into a single platform emerges. EIA products that support multiple information types are replacing stand-alone application-specific archiving products. -Enterprise Information Archiving Transforms the Strategy and Approach for Archiving -June 2010 – Gartner: Kenneth Chin, Sheila Childs DX Object Storage Platform CONSOLIDATE: All Your Static Data on one massively scalable repository with no complexity STORE BOTH Structured and unstructured
4. Active Transactional DataBecome A Fraction Of Total Storage Over Time Majority becomes Historical Data over time or even all historic when no longer active Data Application Performance 10% 100% Active 70% 90% Static 30% Cost $$$ and PAIN Transactional Data Time
5. Automated Data Creation Changes the Mix Machine Generated or Human Generated Immediately Historical Data US Telco 750 TBs of CDR data retained today SEC-store every trade and price in every market forever! Facebook over 1 PB of log data Smart Grid will generate over 1 EB of data in the US alone! Application Performance Data Static 100% Static 100% Static 100% Costs $$$ Time
6. Current Technology Approaches for Long Term Structured Data Retention RDBMS Warehouse / Store Tape Dev Test DR Operational copies compound the storage problem
7. Overburdened Resources Impact of Traditional or Absent Strategies More Data More Infrastructure More Resources Higher Costs More Risk Business Challenges: Flat or Reduced IT Budgets Limited IT Resources & Need to focus on core business systems Increasing compliance retention periods compound data volume and management issues Limited access to larger data sets impacts ability to perform deeper analysis Technical Challenges: Multi-terabytes of structured data in traditional RDBMS’ or Files Challenging to maintain & high cost to manage legacy systems – just for data access! Backup windows aren’t being met. Traditional RDBMS systems cannot ingest high volume data and store it Requires expert resources to provide significant care and feeding
8. Online Data Retention Requires New Technology Online Data Retention (OLDR) Analytical OLAP Transactional OLTP Static Machine-Generated Data (MGD)
9. How We Do It Reduce Size: Massive de-dupe ~97% savings in storage Hardware: On low-cost Dell servers and DX Object Storage Platform Resources: Without specialist DBA support and less storage management Retain Preserved: Massive record volumes in original form Immutable: Tamper proofed with audit trail and WORM Configurable: With retention & expiry policies Massively Scalable: With no complexity Long-Term Preservation: Optimized on object based technology platform with metadata Retrieve Standards: SQL & BI tools via ODBC/JDBC, HTTP Performant: Fast queries for large complex data sets Flexible: With schema evolution & point-in-time access
10. Disruptive Technology Patented Data Reduction through value and pattern de-duplication = Highest rate of compression available Fast Queries in stored format without re-inflation = Access via SQL and ODBC/JDBC, any BI tool (e.g. Cognos, Business Object)

11. Use case:Application Retirement

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12. Application Retirement 1000s of Legacy apps using Oracle, SQL Server, Mainframes etc. Retire apps and store data in optimized repository Same user searches & reports work. No changes needed. Search/ Analytics
13. App Retirement TCO Example 300 Legacy apps (250 GB each) = 75TB 25:1 Compression = 3TB Saving $5.7m/yr Retire apps and store data in optimized repository Same user searches & reports work. No changes needed. Search/ Analytics

14. Use case:Machine Generated Data Retention

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15. Machine Generated Data RetentionBillions of Human Activity or Machine Auto-generated Records Dell solution acts as primary repository -Closed payments/Transactions -Logs, IP Records -Facilities Management Sensor Data OK -Manufacturing Test, QC Ability to quickly access data, even as data continues to be ingested. Search
16. MGD Retention TCO Example 20B WAP logs/day. Retained over 3 Months = 2 Petabytes 20:1 Compression = 100TB Saving $11m/year Dell solution acts as primary repository -WAP Logs Ability to quickly access data, even as data continues to be ingested. Search
17. Retention & Compliance TCO Comparison
18. RainStor Cloud/Hosting Enabled OR Dell DX OR Other Compute

19. Dell DX Object Storage

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20. Solution Overview A long-term preservation optimized solution that solves for the “Big Data” storage problems and enables a better way to retire and archive legacy applications. Retired Apps Retired Apps Integrated Solution Stack Integrated SW and HW for maximum optimization of solution Services practice Dell storage and servers Integrated specialized database Dell networking platforms Dell cloud infrastructure Use Cases “Big Data” Machine Generated Data Application Retirement Application Archive (Future) Data Warehouse Archive (Future) SQL & BI Analytics Services Layer Consulting, Implementation, Support OLDR Layer Storage Layer -DX Object Store CDR Smart Meter Trade Network Logs
21. DX Object Storage PlatformEnterprise-class storage for fixed digital content Solution approach to tiered storage and archive/content management utilizing a common platform Manageability Non-disruptive and simple HW expansions, technology transitions, and retire Self managing, self healing Easy to retrieve data, HTTP API Selectable WORM capabilities Application Scalability Peer-scale architecture Scales to petabytes Near limitless number of files Can scale by as little as 1 node at a time X86-based modular architecture TCO Reduce cost of data management 50% X86-based arch. Power management features No backup infrastructure required
22. The Digital Object Doc Object = Metadata + File Data Stored together for life of object Metadata: Rich, descriptive data about the data Context persisted over time Enables policy-based management Object Address UUID Metadata 101000101010100111010101100010110100…110010 HTTP/1.1 200 OK Date: Thu, 26 Jun 2008 21:26:34 GMT Server: Object Cluster/2.2 Application-Name: MS Word Create-Date: 2008-06-26 21:26:14.687000 System-Cluster: Internet Demo Cluster System-Created: Thu, 26 Jun 2008 21:26:20 GMT Content-Disposition: inline; filename=Sports %Segment%206-26-08.doc Content-Length: 8619354 Content-type: application/doc lifepoint: [Thu, 03 Jul 2015 21:26:14 GMT] reps=2, deletable=True lifepoint: [] delete Replica-Count: 2 CUSTOM ELEMENTS
23. Simplified Expansion and Technology Refresh Adding capacity is simple Rack & Cable Power-up No config or provisioning Refresh is just as easy Upgrade without interruption Retire node or volume Replicates data to another node or volume Recovery & balancing is automatic Continuous data availability Load and capacity balanced
24. Enterprise Ready Solution Query Import
25. Key Benefits Low TCO Less Software, Hardware & People to maintain large data sets Ease of Use Very low admin & no tuning needed Peer-scale architecture No provisioning or configuring Backupless environment Performance High ingestion rate Fast queries Linear scale-out performance Optimized Massive Data Compression Compliant Configurable retention rules WORM options Auto disposition Scalable Massive scalability with no complexity Big Data volumes Consolidated One Database, One Platform