SlideShare une entreprise Scribd logo
1  sur  16
Rain Stor Reduce.  Retain.  Retrieve.
Rain Stor  – Company Background ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],“ Making the  Big Data  problem smaller”
What’s New at RainStor ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Rain Stor  -  Overview Rain Stor   is an infrastructure software company  that provides the leading repository  for historical structured data.  We enable our partners to deliver online data retention solutions that reduce the cost and complexity of preserving information in the enterprise and the cloud.
Big Data  Retention  Problem ,[object Object],[object Object],[object Object]
RainStor’s Patented Technology ,[object Object],[object Object],[object Object],[object Object],[object Object]
Big Data Retention Example: CDR Records Traditional database technologies not economically viable!
Big Data Retention Example: CDR Records Traditional database technologies not economically viable! RainStor Solution: 1/20 th  hardware 1/40 th  storage No design, tuning or maintenance RainStor requires 20x less hardware, 40x less storage and minimal resources.
Product ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Rain Stor   – Cloud Architecture EC2 S3 VM Software Appliance ODBC/JDBC Amazon 1. Compressed de-duplicated data sent to the cloud resulting in quicker and cheaper uploads. 2. Encrypted data stored in private containers ensuring security and easy management. 3. Data accessed on demand using standard SQL tools leveraging elasticity of the cloud
Low-cost and Low-touch
Solutions “Powered by RainStor” ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Partner  Case  Studies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Partner  Deployments
Rain Stor “ The structured data repository of choice for the retention and preservation of information within the   enterprise   and the  cloud ” R elational   A rchiving   IN frastructure
Interact With Us ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Contenu connexe

Tendances

Overview ppdm data_architecture_in_oil and gas_ industry
Overview ppdm data_architecture_in_oil and gas_ industryOverview ppdm data_architecture_in_oil and gas_ industry
Overview ppdm data_architecture_in_oil and gas_ industrySuvradeep Rudra
 
Big data in Engineering Application
Big data in Engineering ApplicationBig data in Engineering Application
Big data in Engineering ApplicationMathews Job
 
Virtualizing Telco Networks
Virtualizing Telco NetworksVirtualizing Telco Networks
Virtualizing Telco NetworksNetAppUK
 
Halloween Infographic
Halloween InfographicHalloween Infographic
Halloween InfographicNetAppUK
 
Cloud Storage: Enabling The Dynamic Datacenter
Cloud Storage: Enabling The Dynamic DatacenterCloud Storage: Enabling The Dynamic Datacenter
Cloud Storage: Enabling The Dynamic DatacenterEntel
 
bigdata (1)
bigdata (1)bigdata (1)
bigdata (1)DIVYA G
 
Visibility and insight - Understand what is going on with your IT infrastructure
Visibility and insight - Understand what is going on with your IT infrastructureVisibility and insight - Understand what is going on with your IT infrastructure
Visibility and insight - Understand what is going on with your IT infrastructureMarie Wilcox
 
Hitachi white-paper-storage-virtualization
Hitachi white-paper-storage-virtualizationHitachi white-paper-storage-virtualization
Hitachi white-paper-storage-virtualizationHitachi Vantara
 
Next Generation Data Centre - IDC Infravision
Next Generation Data Centre - IDC InfravisionNext Generation Data Centre - IDC Infravision
Next Generation Data Centre - IDC InfravisionDamian Hamilton
 
Prologis: How Data Virtualization Enables Data Scientists
Prologis: How Data Virtualization Enables Data ScientistsPrologis: How Data Virtualization Enables Data Scientists
Prologis: How Data Virtualization Enables Data ScientistsDenodo
 
Agile Data Management with Enterprise Data Fabric (Middle East)
Agile Data Management with Enterprise Data Fabric (Middle East)Agile Data Management with Enterprise Data Fabric (Middle East)
Agile Data Management with Enterprise Data Fabric (Middle East)Denodo
 
Optimize the performance, cost, and value of databases.pptx
Optimize the performance, cost, and value of databases.pptxOptimize the performance, cost, and value of databases.pptx
Optimize the performance, cost, and value of databases.pptxIDERA Software
 
10 Good Reasons: NetApp for Healthcare
10 Good Reasons: NetApp for Healthcare10 Good Reasons: NetApp for Healthcare
10 Good Reasons: NetApp for HealthcareNetApp
 
Hitachi Cloud Solutions Profile
Hitachi Cloud Solutions Profile Hitachi Cloud Solutions Profile
Hitachi Cloud Solutions Profile Hitachi Vantara
 
POWER POINT PRESENTATION ON DATA CENTER
POWER POINT PRESENTATION ON DATA CENTERPOWER POINT PRESENTATION ON DATA CENTER
POWER POINT PRESENTATION ON DATA CENTERvivekprajapatiankur
 
Fine Tune Your Archive: Best Practices for Optimizing Enterprise Vault
Fine Tune Your Archive: Best Practices for Optimizing Enterprise Vault Fine Tune Your Archive: Best Practices for Optimizing Enterprise Vault
Fine Tune Your Archive: Best Practices for Optimizing Enterprise Vault Veritas Technologies LLC
 
Database administrators (dbas) face increasing pressure to monitor databases
Database administrators (dbas) face increasing pressure to monitor databasesDatabase administrators (dbas) face increasing pressure to monitor databases
Database administrators (dbas) face increasing pressure to monitor databasesIDERA Software
 
Moving Applications to the Cloud
Moving Applications to the CloudMoving Applications to the Cloud
Moving Applications to the CloudGary Irwin
 

Tendances (20)

Overview ppdm data_architecture_in_oil and gas_ industry
Overview ppdm data_architecture_in_oil and gas_ industryOverview ppdm data_architecture_in_oil and gas_ industry
Overview ppdm data_architecture_in_oil and gas_ industry
 
Big data in Engineering Application
Big data in Engineering ApplicationBig data in Engineering Application
Big data in Engineering Application
 
Virtualizing Telco Networks
Virtualizing Telco NetworksVirtualizing Telco Networks
Virtualizing Telco Networks
 
Halloween Infographic
Halloween InfographicHalloween Infographic
Halloween Infographic
 
Cloud Storage: Enabling The Dynamic Datacenter
Cloud Storage: Enabling The Dynamic DatacenterCloud Storage: Enabling The Dynamic Datacenter
Cloud Storage: Enabling The Dynamic Datacenter
 
bigdata (1)
bigdata (1)bigdata (1)
bigdata (1)
 
Visibility and insight - Understand what is going on with your IT infrastructure
Visibility and insight - Understand what is going on with your IT infrastructureVisibility and insight - Understand what is going on with your IT infrastructure
Visibility and insight - Understand what is going on with your IT infrastructure
 
Hitachi white-paper-storage-virtualization
Hitachi white-paper-storage-virtualizationHitachi white-paper-storage-virtualization
Hitachi white-paper-storage-virtualization
 
Ditas Project Presentation v1.0
Ditas Project Presentation v1.0Ditas Project Presentation v1.0
Ditas Project Presentation v1.0
 
Next Generation Data Centre - IDC Infravision
Next Generation Data Centre - IDC InfravisionNext Generation Data Centre - IDC Infravision
Next Generation Data Centre - IDC Infravision
 
Prologis: How Data Virtualization Enables Data Scientists
Prologis: How Data Virtualization Enables Data ScientistsPrologis: How Data Virtualization Enables Data Scientists
Prologis: How Data Virtualization Enables Data Scientists
 
Agile Data Management with Enterprise Data Fabric (Middle East)
Agile Data Management with Enterprise Data Fabric (Middle East)Agile Data Management with Enterprise Data Fabric (Middle East)
Agile Data Management with Enterprise Data Fabric (Middle East)
 
DCiM_BIG_DATA
DCiM_BIG_DATADCiM_BIG_DATA
DCiM_BIG_DATA
 
Optimize the performance, cost, and value of databases.pptx
Optimize the performance, cost, and value of databases.pptxOptimize the performance, cost, and value of databases.pptx
Optimize the performance, cost, and value of databases.pptx
 
10 Good Reasons: NetApp for Healthcare
10 Good Reasons: NetApp for Healthcare10 Good Reasons: NetApp for Healthcare
10 Good Reasons: NetApp for Healthcare
 
Hitachi Cloud Solutions Profile
Hitachi Cloud Solutions Profile Hitachi Cloud Solutions Profile
Hitachi Cloud Solutions Profile
 
POWER POINT PRESENTATION ON DATA CENTER
POWER POINT PRESENTATION ON DATA CENTERPOWER POINT PRESENTATION ON DATA CENTER
POWER POINT PRESENTATION ON DATA CENTER
 
Fine Tune Your Archive: Best Practices for Optimizing Enterprise Vault
Fine Tune Your Archive: Best Practices for Optimizing Enterprise Vault Fine Tune Your Archive: Best Practices for Optimizing Enterprise Vault
Fine Tune Your Archive: Best Practices for Optimizing Enterprise Vault
 
Database administrators (dbas) face increasing pressure to monitor databases
Database administrators (dbas) face increasing pressure to monitor databasesDatabase administrators (dbas) face increasing pressure to monitor databases
Database administrators (dbas) face increasing pressure to monitor databases
 
Moving Applications to the Cloud
Moving Applications to the CloudMoving Applications to the Cloud
Moving Applications to the Cloud
 

Similaire à RainStor 3.5 Overview

Logicalis Backup as a Service: Re-defining Data Protection
Logicalis Backup as a Service: Re-defining Data ProtectionLogicalis Backup as a Service: Re-defining Data Protection
Logicalis Backup as a Service: Re-defining Data ProtectionLogicalis Australia
 
Presentation riverbed steelhead appliance main 2010
Presentation   riverbed steelhead appliance main 2010Presentation   riverbed steelhead appliance main 2010
Presentation riverbed steelhead appliance main 2010chanwitcs
 
New Database and Application Development Technology
New Database and Application Development TechnologyNew Database and Application Development Technology
New Database and Application Development TechnologyMaurice Staal
 
Effectively Managing Your Historical Data
Effectively Managing Your Historical DataEffectively Managing Your Historical Data
Effectively Managing Your Historical DataCallidus Software
 
Modernize storage infrastructure with hybrid cloud & flash
Modernize storage infrastructure with hybrid cloud & flashModernize storage infrastructure with hybrid cloud & flash
Modernize storage infrastructure with hybrid cloud & flashCraig McKenna
 
When SAP alone is not enough
When SAP alone is not enoughWhen SAP alone is not enough
When SAP alone is not enoughCloudera, Inc.
 
IDT Replaces On-Premises Appliances with Primary Backup on AWS
 IDT Replaces On-Premises Appliances with Primary Backup on AWS IDT Replaces On-Premises Appliances with Primary Backup on AWS
IDT Replaces On-Premises Appliances with Primary Backup on AWSAmazon Web Services
 
Take Control Over Storage Costs with Intuitive Management and Simplicity
Take Control Over Storage Costs with Intuitive Management and SimplicityTake Control Over Storage Costs with Intuitive Management and Simplicity
Take Control Over Storage Costs with Intuitive Management and SimplicityVeritas Technologies LLC
 
Smarter Data Protection And Storage Management Solutions
Smarter Data Protection And Storage Management SolutionsSmarter Data Protection And Storage Management Solutions
Smarter Data Protection And Storage Management Solutionsaejaz7
 
Connecting Silos in Real Time with Data Virtualization
Connecting Silos in Real Time with Data VirtualizationConnecting Silos in Real Time with Data Virtualization
Connecting Silos in Real Time with Data VirtualizationDenodo
 
Building a Single Logical Data Lake: For Advanced Analytics, Data Science, an...
Building a Single Logical Data Lake: For Advanced Analytics, Data Science, an...Building a Single Logical Data Lake: For Advanced Analytics, Data Science, an...
Building a Single Logical Data Lake: For Advanced Analytics, Data Science, an...Denodo
 
Introduction to Modern Data Virtualization 2021 (APAC)
Introduction to Modern Data Virtualization 2021 (APAC)Introduction to Modern Data Virtualization 2021 (APAC)
Introduction to Modern Data Virtualization 2021 (APAC)Denodo
 
A Logical Architecture is Always a Flexible Architecture (ASEAN)
A Logical Architecture is Always a Flexible Architecture (ASEAN)A Logical Architecture is Always a Flexible Architecture (ASEAN)
A Logical Architecture is Always a Flexible Architecture (ASEAN)Denodo
 
datacore-1-341M4XT
datacore-1-341M4XTdatacore-1-341M4XT
datacore-1-341M4XTGary Mason
 
Big Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATION
Big Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATIONBig Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATION
Big Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATIONMatt Stubbs
 
Analysis of SOFTWARE DEFINED STORAGE (SDS)
Analysis of SOFTWARE DEFINED STORAGE (SDS)Analysis of SOFTWARE DEFINED STORAGE (SDS)
Analysis of SOFTWARE DEFINED STORAGE (SDS)Kaushik Rajan
 
Microsoft SQL Server - Parallel Data Warehouse Presentation
Microsoft SQL Server - Parallel Data Warehouse PresentationMicrosoft SQL Server - Parallel Data Warehouse Presentation
Microsoft SQL Server - Parallel Data Warehouse PresentationMicrosoft Private Cloud
 

Similaire à RainStor 3.5 Overview (20)

Logicalis Backup as a Service: Re-defining Data Protection
Logicalis Backup as a Service: Re-defining Data ProtectionLogicalis Backup as a Service: Re-defining Data Protection
Logicalis Backup as a Service: Re-defining Data Protection
 
Presentation riverbed steelhead appliance main 2010
Presentation   riverbed steelhead appliance main 2010Presentation   riverbed steelhead appliance main 2010
Presentation riverbed steelhead appliance main 2010
 
New Database and Application Development Technology
New Database and Application Development TechnologyNew Database and Application Development Technology
New Database and Application Development Technology
 
Effectively Managing Your Historical Data
Effectively Managing Your Historical DataEffectively Managing Your Historical Data
Effectively Managing Your Historical Data
 
Modernize storage infrastructure with hybrid cloud & flash
Modernize storage infrastructure with hybrid cloud & flashModernize storage infrastructure with hybrid cloud & flash
Modernize storage infrastructure with hybrid cloud & flash
 
When SAP alone is not enough
When SAP alone is not enoughWhen SAP alone is not enough
When SAP alone is not enough
 
IDT Replaces On-Premises Appliances with Primary Backup on AWS
 IDT Replaces On-Premises Appliances with Primary Backup on AWS IDT Replaces On-Premises Appliances with Primary Backup on AWS
IDT Replaces On-Premises Appliances with Primary Backup on AWS
 
Take Control Over Storage Costs with Intuitive Management and Simplicity
Take Control Over Storage Costs with Intuitive Management and SimplicityTake Control Over Storage Costs with Intuitive Management and Simplicity
Take Control Over Storage Costs with Intuitive Management and Simplicity
 
Smarter Data Protection And Storage Management Solutions
Smarter Data Protection And Storage Management SolutionsSmarter Data Protection And Storage Management Solutions
Smarter Data Protection And Storage Management Solutions
 
Connecting Silos in Real Time with Data Virtualization
Connecting Silos in Real Time with Data VirtualizationConnecting Silos in Real Time with Data Virtualization
Connecting Silos in Real Time with Data Virtualization
 
Building a Single Logical Data Lake: For Advanced Analytics, Data Science, an...
Building a Single Logical Data Lake: For Advanced Analytics, Data Science, an...Building a Single Logical Data Lake: For Advanced Analytics, Data Science, an...
Building a Single Logical Data Lake: For Advanced Analytics, Data Science, an...
 
Introduction to Modern Data Virtualization 2021 (APAC)
Introduction to Modern Data Virtualization 2021 (APAC)Introduction to Modern Data Virtualization 2021 (APAC)
Introduction to Modern Data Virtualization 2021 (APAC)
 
A Logical Architecture is Always a Flexible Architecture (ASEAN)
A Logical Architecture is Always a Flexible Architecture (ASEAN)A Logical Architecture is Always a Flexible Architecture (ASEAN)
A Logical Architecture is Always a Flexible Architecture (ASEAN)
 
datacore-1-341M4XT
datacore-1-341M4XTdatacore-1-341M4XT
datacore-1-341M4XT
 
Big Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATION
Big Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATIONBig Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATION
Big Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATION
 
Analysis of SOFTWARE DEFINED STORAGE (SDS)
Analysis of SOFTWARE DEFINED STORAGE (SDS)Analysis of SOFTWARE DEFINED STORAGE (SDS)
Analysis of SOFTWARE DEFINED STORAGE (SDS)
 
Software defined storage
Software defined storageSoftware defined storage
Software defined storage
 
Cleversafe.PPTX
Cleversafe.PPTXCleversafe.PPTX
Cleversafe.PPTX
 
Why the Cloud?
Why the Cloud?Why the Cloud?
Why the Cloud?
 
Microsoft SQL Server - Parallel Data Warehouse Presentation
Microsoft SQL Server - Parallel Data Warehouse PresentationMicrosoft SQL Server - Parallel Data Warehouse Presentation
Microsoft SQL Server - Parallel Data Warehouse Presentation
 

Plus de RainStor

Archiving is a No-brainer - Bloor Analyst and RainStor Executive Discuss
Archiving is a No-brainer - Bloor Analyst and RainStor Executive DiscussArchiving is a No-brainer - Bloor Analyst and RainStor Executive Discuss
Archiving is a No-brainer - Bloor Analyst and RainStor Executive DiscussRainStor
 
Big Data Analytics on Hadoop RainStor Infographic
Big Data Analytics on Hadoop RainStor InfographicBig Data Analytics on Hadoop RainStor Infographic
Big Data Analytics on Hadoop RainStor InfographicRainStor
 
TDWI Checklist Report: Active Data Archiving
TDWI Checklist Report:  Active Data ArchivingTDWI Checklist Report:  Active Data Archiving
TDWI Checklist Report: Active Data ArchivingRainStor
 
Rain stor isilon_emc_real_Examine the Real Cost of Storing & Analyzing Your M...
Rain stor isilon_emc_real_Examine the Real Cost of Storing & Analyzing Your M...Rain stor isilon_emc_real_Examine the Real Cost of Storing & Analyzing Your M...
Rain stor isilon_emc_real_Examine the Real Cost of Storing & Analyzing Your M...RainStor
 
Smarter Management for Your Data Growth
Smarter Management for Your Data GrowthSmarter Management for Your Data Growth
Smarter Management for Your Data GrowthRainStor
 
Big Data Retention Opportunity or Burden? - Featuring Merv Adrian July 2010
Big Data Retention Opportunity or Burden? - Featuring Merv Adrian July 2010Big Data Retention Opportunity or Burden? - Featuring Merv Adrian July 2010
Big Data Retention Opportunity or Burden? - Featuring Merv Adrian July 2010RainStor
 

Plus de RainStor (6)

Archiving is a No-brainer - Bloor Analyst and RainStor Executive Discuss
Archiving is a No-brainer - Bloor Analyst and RainStor Executive DiscussArchiving is a No-brainer - Bloor Analyst and RainStor Executive Discuss
Archiving is a No-brainer - Bloor Analyst and RainStor Executive Discuss
 
Big Data Analytics on Hadoop RainStor Infographic
Big Data Analytics on Hadoop RainStor InfographicBig Data Analytics on Hadoop RainStor Infographic
Big Data Analytics on Hadoop RainStor Infographic
 
TDWI Checklist Report: Active Data Archiving
TDWI Checklist Report:  Active Data ArchivingTDWI Checklist Report:  Active Data Archiving
TDWI Checklist Report: Active Data Archiving
 
Rain stor isilon_emc_real_Examine the Real Cost of Storing & Analyzing Your M...
Rain stor isilon_emc_real_Examine the Real Cost of Storing & Analyzing Your M...Rain stor isilon_emc_real_Examine the Real Cost of Storing & Analyzing Your M...
Rain stor isilon_emc_real_Examine the Real Cost of Storing & Analyzing Your M...
 
Smarter Management for Your Data Growth
Smarter Management for Your Data GrowthSmarter Management for Your Data Growth
Smarter Management for Your Data Growth
 
Big Data Retention Opportunity or Burden? - Featuring Merv Adrian July 2010
Big Data Retention Opportunity or Burden? - Featuring Merv Adrian July 2010Big Data Retention Opportunity or Burden? - Featuring Merv Adrian July 2010
Big Data Retention Opportunity or Burden? - Featuring Merv Adrian July 2010
 

Dernier

Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxLoriGlavin3
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfMounikaPolabathina
 
unit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxunit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxBkGupta21
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersRaghuram Pandurangan
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfPrecisely
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxNavinnSomaal
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024Lonnie McRorey
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupFlorian Wilhelm
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 

Dernier (20)

Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdf
 
unit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxunit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptx
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information Developers
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptx
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 

RainStor 3.5 Overview

  • 1. Rain Stor Reduce. Retain. Retrieve.
  • 2.
  • 3.
  • 4. Rain Stor - Overview Rain Stor is an infrastructure software company that provides the leading repository for historical structured data. We enable our partners to deliver online data retention solutions that reduce the cost and complexity of preserving information in the enterprise and the cloud.
  • 5.
  • 6.
  • 7. Big Data Retention Example: CDR Records Traditional database technologies not economically viable!
  • 8. Big Data Retention Example: CDR Records Traditional database technologies not economically viable! RainStor Solution: 1/20 th hardware 1/40 th storage No design, tuning or maintenance RainStor requires 20x less hardware, 40x less storage and minimal resources.
  • 9.
  • 10. Rain Stor – Cloud Architecture EC2 S3 VM Software Appliance ODBC/JDBC Amazon 1. Compressed de-duplicated data sent to the cloud resulting in quicker and cheaper uploads. 2. Encrypted data stored in private containers ensuring security and easy management. 3. Data accessed on demand using standard SQL tools leveraging elasticity of the cloud
  • 12.
  • 13.
  • 15. Rain Stor “ The structured data repository of choice for the retention and preservation of information within the enterprise and the cloud ” R elational A rchiving IN frastructure
  • 16.

Notes de l'éditeur

  1. Hello. I’m delighted you could join me today for this introduction to RainStor the Company and our technology. RainStor is a young company but one with very mature technology. Years of research and development by the Ministry of Defence in the United Kingdom forms the basis for our commercialization of a powerful and unique piece of infrastructure software you will hear about today. Despite our youth as a commercial business our go to market model, which involves embedding our software with application and other infrastructure partners has enabled us to scale to 50 customer deployments by end of 2009, in a very short space of time. Our focus is on the preservation of the massive quantities of structured data and information being generated today. Put another way, we believe we “make the big data problem smaller.”
  2. Before we delve into our technology and the Big Data problem, let me share some of our latest company news with you. We have opened up an office in San Francisco and our CEO John Bantleman, a previous veteran of 2 successful NASDAQ IPOs is relocating back to the US which reflects the terrific momentum we have seen over the last 12 months. Out latest release, RainStor 3.5 has a single architecture that is scalable for use in the enterprise and the cloud And last but not least, we are excited to announce an extension to our partnership with Informatica in which they OEM and embed RainStor within their Informatica Data Archive. Additionally, Group 2000 a dutch based company becomes our latest application partner for the telecommunications marketplace.
  3. With that as a backdrop, let me formerly introduce ourselves. We are an infrastructure software company that provides a repository for historical structured data. Unlike traditional databases such as Oracle and DB2, who are traditionally used for OLTP transaction processing, OR specialized data warehouses such as Teradata , our focus is solely on the efficient storage, retention and accessibility of HISTORICAL structured data.
  4. So how do we solve the Big Data retention problem? While there are many solutions on the market that deal with unstructured data such as email, documents and video, we specialized in structured Big data and data that is historical in context. As previously mentioned structured data can come from relational databases such as Oracle, OR be in specialized data warehouses such as Teradata However, structured data can come directly from other sources including logs, SMS text messages and Call Data Records. The structured Big Data problem is an issue that is being manifested by the flood of information being generated as well as increasing business or regulatory compliance mandated to retain and secure this data for mandated periods of time. With resources scarce, companies are finding it impossible to keep up. RainStor was conceived to solve this problem. Firstly to reduce the total cost of ownership resulting from hardware, resources as well as people power. Secondly to provide built-in immutability and configurable retention polices not available in traditional databases. Finally to ensure that despite all these efficiencies and capabilities, that data is queryable on demand at performance levels that meet or exceed business or compliance needs.
  5. Here is an example that shows how RainStor is unique and different. Rather than merely compressing data at the document and block level, we de-duplicate data values and detect patterns as they are being loaded into our repository. Our worldwide patent for this technology allows us to efficiently uniquely store values once and only once. As illustrated in this diagram, the surname Smith is shared between the first and second records loaded and is therefore only stored once. We store the information in a tree structure while still maintaining a full representation of the original records through this networked approach. Imagine this method of storage for highly repetitive data such as stock transactions, call data records and you can understand how significant the de-duplication can be . This capability combined with additional algorithmic and byte level compression allows RainStor to reduce the storage footprint of data to previously unseen levels. But wait you say, surely there is a penalty to be paid at retrieval time given the extreme compression. Not so, in fact our method of storage combined with our industry standard SQL query capabilities means that information can be accessed as fast if not faster than typical RDBMS queries. All this without ever having to re-inflate the data.
  6. To illustrate how this looks in a real life use case, take the following example: A Telco customer needing to meet requirements of storing and making queryable 1B+ Call Data Records a day would require significant hardware to run this on a traditional RDBMS, 2x40 core specialized servers and over 100TB of storage , as well as significant human effort through Database administration in order to ensure tuning for proper query performance to meet SLAs.
  7. In contrast RainStor is able to implement the exact same solution in place of a traditional RDBMS resulting in a significant savings in processing power required, about 1/20 th the box specifications. As well as 1/40 th of the disk storage space. Additionally RainStor is designed to be extremely low touch with no administration required.
  8. This diagram illustrates how RainStor can consume any structured data source on the left and be fully queried by existing SQL through ODBC/JDBC. Another unique element of RainStor is our ability to preserve schema evolution. Typically as an application is upgraded from release to release, the schema evolves – new fields and tables are added, columns may be dropped. In a RDBMS, those changes are permanent. With RainStor, we are able to retain schema change history together with the data we retain, thereby allowing queries to be performed at any point in time. Like Tivo you can choose to rewind and present the exact representation of the data regardless of the query you issue. RainStor is able to map any query statement you issue and intelligently return you only the fields and records that existed at that moment in time. Our unique way of storing records in simple file blocks or containers also allows us to place our repository on boxes such as EMC Centera, which prior to RainStor was only able to store emails, documents and other forms of unstructured data. In that example immutability would be obtained together with RainStor’s configurable rules at the repository level and EMC Centera at the hardware level. In summary with RainStor, data can be taken from any source, stored on any platform and queried using any reporting tool…we are cost-efficient, agnostic and unobtrusive compared to traditional RDBMs alternatives.
  9. RainStor’s architecture unchanged is ideally suited for the cloud. In fact, by significantly de-duplicating data for compression prior to transmission to the cloud, RainStor solves the bandwidth dilemma problem as well. Through added encryption and an average 40 times compression, transmission of large quantities of structured data into the cloud is now a feasible proposition. Because of RainStor’s simple file storage containers, the repository is automatically multi-tenanted. RainStor is therefore able to be deployed immediately in any cloud-based service. This example shows RainStor running on Amazon AWS and leveraging EC2 for query. RainStor’s ability to run massively paralleled queries can tap into the elasticity of the processing power in the cloud, allowing the flexible trade off between query performance and cost of compute power.
  10. To summarize the major benefits: RainStor’s extreme de-duplication data compression, simplified data management and our ability to run on commodity hardware on the enterprise and in the cloud results in an offering which we believe dramatically changes the economics of structured data retention.
  11. There are many use cases for RainStor’s technology. We follow the Intel inside model in that we embed our repository with our partner solutions through a “Powered by RainStor” paradigm. As in the CDR example, log and event data can similarly be captured and retained using RainStor as the primary repository. RainStor can also be used for Application Retirement whereby large numbers of legacy systems can be turned off while still allowing their core business data to be accessible while stored in Rainstor. This frees up major resources in terms of hardware and licenses and people power. Many view this as a necessary activity prior to moving ahead with virtualization initiatives. “Rationalize” before you Virtualize is the concept here. SaaS Data Escrow is a new type of service that enables many copies to exist either in the cloud or on premise so that the customer can access their data, anytime, anywhere outside of the SaaS vendor environment. This is becoming more a requirement due to the need for analytics and recoverability as well as offloading some of the older data in massive production SaaS repositories which are affecting performance of these applications. Application Archiving refers to our partners who embed our technology as a specific module for their customers within their app. Finally data warehouse and appliance archiving are another example of historical structured data sources that can be loaded into our repository to address fixed capacity issues
  12. Illustrated here are 3 partner case studies each unique in the type of data and business need. Adaptive Mobile archives SMS text messages for Telco's, and uses RainStor as their primary repository for over 14k messages per second they must process and make queryable. This use case illustrates how RainStor is the best option for efficiently retaining structured data which immediately becomes historical as soon as it is created. OnPoint technologies uses RainStor for analytics of historical employment records for fraud detection Informatica we previously mentioned as part of our latest announcement, embed RainStor within their Informatica Data Archive suite to store database data from major ERP and financial applications like SAP and Oracle.
  13. As I mentioned, despite being young we have been able to deploy through our partners to many blue chip corporations, some of which are highlighted here
  14. In conclusion, our goal is to provide our partners with a cost-efficient and unique structured data repository for the retention and preservation of information within the enterprise and the cloud.
  15. Thank you for your time, we would be delighted to speak to you if you would like more information. Please visit or follow us at any of the options listed here.