'Data lake' presentation slideshows

Presentation: DW Architechtures

Presentation: DW Architechtures. Erik Perjons DSV, Stockholm University. Four types of Data Warehouse architectures. Independent Data Mart Architecture. ( Kimball & Ross, 2013 ). Hub -and- Spoke Corporate Information Factury Archiecture ( Inmon ). ( Kimball & Ross, 2013 ).

556 views • 55 slides

How to Architect Azure for Insights & Analytics

How to Architect Azure for Insights & Analytics. Mehmet Bakkaloglu. Business Insights & Analytics, Hitachi Consulting SQLBits Conference, London, Feb 2018. Vast array of Platform-as-a-Service components. Azure Blob Storage. Azure Data Lake Store. Azure IoT Hub. Stream Analytics. Cosmos DB.

414 views • 41 slides

Adaptive Protocols and the DDI 4 Process Model

Adaptive Protocols and the DDI 4 Process Model. Jay Greenfield Sophia Kuan Booz Allen Hamilton NSF-Census Research Network Meeting Spring 2014 Suitland, MD. Agenda. Describe several uses of an emerging process model that is part of the future DDI 4 specification

483 views • 36 slides

Microsoft Ignite NZ

Microsoft Ignite NZ. 25-28 October 2016 SKYCITY, Auckland. Azure Data Lake. Julian Lee Advanced Analytics Technical Specialist Global Black Belt - ANZ. Building a Data Lake and Gaining Insight [M368]. Traditionally, analytics have been over pre-defined structures. BI and visualizations.

355 views • 35 slides

Harnessing the Power of Hadoop: Cloud Scale with Microsoft Azure HDInsight

Harnessing the Power of Hadoop: Cloud Scale with Microsoft Azure HDInsight. Lance Olson Partner Group Program Manager. BRK2557. Agenda. Big data and traditional data warehouse Big data in the cloud Cloud versus on-premises Patterns and case studies HDInsight workloads.

300 views • 29 slides

Big Data Analytics

Big Data Analytics. A Presentation by Meg Monsen , Michael Leonard, and Eric Zeng. Agenda. Big Data Analytics and its Objective s Financial Impact Structured vs Unstructured Data Us ers of Big Data Relevant Technologies ( Hadoop, MongoDB) Coding Examples Future of A nalytics.

454 views • 25 slides

Russian academic institutes participation in WLCG Data Lake project

Russian academic institutes participation in WLCG Data Lake project. Andrey Kiryanov , Xavier Espinal, Alexei Klimentov, Andrey Zarochentsev. LHC Run 3 and HL-LHC Run 4 Computing Challenges. Raw data volume. We are here.

267 views • 25 slides

WEBINAR Big Data Fabric Drives Innovation And Growth

WEBINAR Big Data Fabric Drives Innovation And Growth. Noel Yuhanna, Principal Analyst. September 12, 2017. Call in at 10:55 a.m. Eastern time. Trend. Moving to the next-generation of data architecture. Real-time. Lots of data. Self-service. Digital business. BT. Batch. IT.

237 views • 23 slides

Microsoft Azure The cloud platform for digital transformation

Microsoft Azure The cloud platform for digital transformation. Diogo Perestrelo Azure Specialist Public Sector (v-dipere@microsoft.com). Why consider the cloud?. Public Cloud Platform. $30. Speed. Scale. Economics. $25. $20. Market Share (in Billions). $15. $10. $5. 2008. 2009.

284 views • 20 slides

The WLCG strategy document for HL-LHC

The WLCG strategy document for HL-LHC. Ian Bird Simone Campana. The HSF Community White Paper. The HSF Community White Paper defines a roadmap for HEP software and computing R&Ds for the 2020s For WLCG this is crucial in preparation for HL-LHC

165 views • 16 slides

Work Package II - DIOS

303 views • 13 slides

Computing Models, Facilities, Distributed Computing

Computing Models, Facilities, Distributed Computing. Draft Chapter Scope

123 views • 12 slides

Case study: The Practicalities of building an enterprise knowledge graph

235 views • 10 slides

Work Package II - DIOS

Work Package II - DIOS. Simone Campana CERN, Geneva, Switzerland Annecy, 7 February 2019. Data Infrastructure for Open Science (DIOS). Goal: design, implement and operate a cloud of data services for open access and open science at the Exabyte scale

106 views • 10 slides

Amazon S3 Data Lake

Reduce data deployment time from months to hours and get multiple benefits with BryteFlow Enterprise edition. Efficiently merge, replicate, and transform data to Amazon S3, Amazon Redshift, and Snowflake. Reconciled data in minutes regardless of type of database, file, or API. High availability software with built-in resiliency.

71 views • 5 slides