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RainStor 3.5 Overview
 

RainStor 3.5 Overview

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RainStor is a specialized repository for historical structured data retention. This sildeshare is an introduction to RainStor.

RainStor is a specialized repository for historical structured data retention. This sildeshare is an introduction to RainStor.

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  • 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.”
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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
  • 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.
  • 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.

RainStor 3.5 Overview RainStor 3.5 Overview Presentation Transcript

  • Rain Stor Reduce. Retain. Retrieve.
  • Rain Stor – Company Background
    • 20+ employees
    • Formerly Clearpace Software
    • Technology developed in UK defense industry
    • Partner focused go-to-market strategy
    • Over 50 customer deployments, cross industry and geography
    • Offices in San Francisco, USA and Gloucester, UK
    • Provides disruptive technology for preserving structured data
    “ Making the Big Data problem smaller”
  • What’s New at RainStor
    • RainStor enters the US market
      • New San Francisco HQ to support growing partner portfolio
      • New US-based employees to drive international growth
      • CEO relocates to USA
    • RainStor 3.5 now generally available
      • Architected for BIG DATA
      • Optimized for cost-efficient scalability in the enterprise and the cloud
    • RainStor signs new partners
      • Informatica extends partnership
      • Group2000 becomes latest Telco partner
  • 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
    • Massive data and storage growth
    • Strong business and regulatory drivers
    • Requirements outstripping resources
  • RainStor’s Patented Technology
    • De-dupe for databases
    • Stores unique values and patterns
    • Full access to data without re-inflation
    • Highly efficient in memory and on disk
    • Patented
  • 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
    • Reduce
      • E xtreme data dedupe compression ~40:1
      • No schema or index design
      • Minimal ongoing management
    • Retrieve
      • Full SQL access (ODBC/JDBC)
      • Schema evolution preserved
      • Query ‘AS OF’ prior point in time
    • Retain
      • Original records stored
      • Immutable data model
      • D ata retention policies
  • 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”
    • Log and Event Retention – Retain log and event data captured from networks that must be preserved and analyzed for regulatory or security reasons.
    • Application Retirement – Retain historical data from legacy applications that must be retired during migrations to new enterprise software applications or SaaS.
    •  
    • SaaS Data Escrow – Provide a third-party copy of SaaS data to ensure the data within SaaS applications is always available or provide more flexible reporting.
    • Application Archiving - Retain inactive data that must be offloaded from production databases to improve system performance and reduce maintenance costs.
    • Warehouse Archiving - Retain cold data that is infrequently accessed within data warehouses but might need to be re-instantiated in the future.
    • Appliance Archiving – Retain data that must be removed from appliances as they reach capacity
  • Partner Case Studies
    • Adaptive Mobile
    • Sector : Telco
    • Solution : Message (SMS/MMS) and traffic log management
    • Retaining 1000s of messages a second while keeping accessible for regulatory purposes
    • OnPoint Technologies
    • Sector : Public
    • Solution : Unemployment records management
    • Storing data from dozens of distinct applications with no design and providing flexible analysis for fraud
    • Informatica
    • Sector : Various/Horizontal
    • Solution : ILM
    • Retaining historical data from highly complex packaged applications while keeping accessible for business and regulatory purposes
  • 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
    • Website
    • www.rainstor.com
    • Email
    • [email_address]
    • YouTube
    • rainstorcompany
    • Twitter
    • twitter.com/rainstor