Big Data Movement ApplianceProduct Overview
Big Data Movement ApplianceData Warehouses are under significant pressure toexpand capacity and make information available...
Data Warehousing With Tervela On Demand Web Stack(Java, .NET, PHP, Ruby)                              Tervela Appliances  ...
With Our Solution…1. Turn hot-cold data warehouses into hot-hot systems…   Effectively doubling capacity for incremental i...
Hot-Hot Backup & Disaster Recovery   Hot-Cold DR                            Hot-Hot DR                                    ...
Distributed Operational Data Stores                                                                             •   ODS sy...
Sensor Networks                                                                    •   Central grids                      ...
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Big data movement for data warehouses

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Tervela's Big Data Movement Appliance provides real-time data access, disaster recovery, and global distribution for large data warehouses

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  • Messaging, ESB, SOA: JMS (Tibco, MQ, ActiveMQ, RabbitMQ)On-Demand Web Stack: RESTful (Java, Ruby, etc.)EAI (Enterprise App Integration) – adapter (salesforce, SAP, Peoplesoft, etc)Database replication – SQL, Log Adapters (MySql, Oracle, DB2, vertica, Hadoop, etc)EDI (Electronic Data integration) – (supply chain data, financial data, etc) File replication – File system adapter (CIFS, NFS)Warehouse:Netezza, TeradataETLBIAnalyticsBenefits:pre-existing adaptersSchema framework makes integration with ETLs easier
  • Transcript of "Big data movement for data warehouses"

    1. 1. Big Data Movement ApplianceProduct Overview
    2. 2. Big Data Movement ApplianceData Warehouses are under significant pressure toexpand capacity and make information available fasterTervela provides a big data movement appliance for data warehouse& analytics owners that: • Turns active-standby disaster recovery into active-active • Provides real-time data acquisition, distribution, processing, and loading for warehouses • Executes high-performance data ingress • Automatically buffers data during peak load • Preserves investment in existing technologies 2
    3. 3. Data Warehousing With Tervela On Demand Web Stack(Java, .NET, PHP, Ruby) Tervela Appliances REPORTING (physical or virtual) File System vBDMA (CIFS, NFS) BI Analytics A D A P T E R S NoSQL/NewSQL BDMA (Vertica, Hadoop) Financial data (Oracle, IBM) ETL Operational Data Store (MySQL, Oracle, DB2) BDMA ERP (SAP, Oracle) Middleware(TIBCO, MQ, ActiveMQ) vBDMA Primary Data Cloud-based CRM Warehouse (Salesforce, Oracle) Secondary Data Warehouse 3
    4. 4. With Our Solution…1. Turn hot-cold data warehouses into hot-hot systems… Effectively doubling capacity for incremental investment.2. Provide real-time access to data and analytics… Satisfying customer demand for faster and more comprehensive access to data and decision support.3. Distribute warehoused data across the globe… Supporting global demand for information in real-time. 4
    5. 5. Hot-Hot Backup & Disaster Recovery Hot-Cold DR Hot-Hot DR with Tervela Apps Apps (can be virtualized) Primary Data Center DBs & File Systems BDMA Tervela Data guaranteed available in the fabric, even if not Restore yet archived to required in remote servers case of No restore disaster required Local CloudSecondary Cloud Data Backup All data centersdata center Backup Center Remote are active, utilized Data Center 5
    6. 6. Distributed Operational Data Stores • ODS systems operate Kirkland ODS as regional big data • Persisted data and BDMA interesting events are New York identified and ODSOakland ODS propagated onto the fabric BDMA BDMA Data • Tervela moves data to Aggregated Baltimore ODS Central BI Fabric the central BI system: • High-performance BDMA • High reliability • Buffered ingress • Slow consumer BDMA management Orlando ODS • Relevant data or events can be sent back out to BDMA regional systems 6
    7. 7. Sensor Networks • Central grids Health synchronized across Store Sensors Patient Data data fabric backboneInformation • Sensors connect to data fabric through virtual vDBMA vBDMA vBDMA appliances • Data uploaded into the Data Fabric fabric by sensors is BDMA immediately available, BDMA even before persisted in BDMA grids East West • Data fabric buffers Central Central ingress for burst Mid Grid Grid management & slow Central consumers Grid • Connection Aggregation and Footprint Reduction reducing infrastructure requirements 7

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