Upload
sap-technology
View
2.059
Download
2
Embed Size (px)
Citation preview
Spark Usage in Enterprise Business Operations
Ken Tsai VP, Data Management & Platform-as-Services SAP @kentsaiSAP
2.17.16: Spark Summit, NYC
© 2016 SAP SE or an SAP affiliate company. All rights reserved. Spark Summit New York, 2.17.16
© 2016 SAP SE or an SAP affiliate company. All rights reserved.
No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE or an SAP affiliate company. SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries. Please see http://global12.sap.com/corporate-en/legal/copyright/index.epx for additional trademark information and notices. Some software products marketed by SAP SE and its distributors contain proprietary software components of other software vendors. National product specifications may vary. These materials are provided by SAP SE or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP SE or its affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP SE or SAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty. In particular, SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation, and SAP SE’s or its affiliated companies’ strategy and possible future developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.
© 2016 SAP SE or an SAP affiliate company. All rights reserved. Spark Summit New York, 2.17.16
SAP – Our Quick Snapshot in the Enterprise Computing World
74% of the world’s transaction revenue touches an SAP system.
SAP’s product focus:
Enterprise Applications
Business Networks
Platforms – 15 yrs on IMC
SAP customers represent 87% of Forbes Global 2,000 companies.
SAP touches $16 trillion of world consumer purchases.
© 2016 SAP SE or an SAP affiliate company. All rights reserved. Spark Summit New York, 2.17.16
SAP HANA – An In-Memory Platform to Enable New Business Scenarios Previously Not Feasible
BKPF BSEG BSEG BSEG
no indices no aggregates no redundancies
CORE DATA STRUCTURE REMAINS UNCHANGED
• Soft financial close anytime • Real-time revenue and cost analysis • Real-time liquidity forecasts • Real-time alerts and blocks on suspicious
transactions
© 2016 SAP SE or an SAP affiliate company. All rights reserved. Spark Summit New York, 2.17.16
Distributed Big Data Is Everywhere How to better use it in core enterprise business applications?
~79% of Data Reservoirs/Lakes are still disconnected
from core business operations
How do I embed big data signal into my business applications
and enterprise analytics?
53 Difficulty integrating with CRM and/or other systems
% 49 Unable to apply or integrate external data quickly enough to inform real-time decision making
% 59 Only a few analysts with specialized training can analyze big data
%
Harvard Business Review Analytic Services, Global Survey of 251 Respondents, Sept. 2015
© 2016 SAP SE or an SAP affiliate company. All rights reserved. Spark Summit New York, 2.17.16
Introducing SAP HANA Vora
An in-memory query engine that extends the Apache Spark execution framework to enrich the interactive analytics experiences on massively distributed computing clusters
• OLAP processing • In-Memory
Computing for high performance
• Connecting to Enterprise Systems
• Unified System Management
SAP HANA
ERP DATA BIG DATA
Parallelized Queries
Vora
© 2016 SAP SE or an SAP affiliate company. All rights reserved. Spark Summit New York, 2.17.16
Key Open Source Contribution to Apache Spark Ecosystem Spark to HANA Push-downs & Data Hierarchies
scala>valhierarchy=sqlContext.sql(s"""SELECTLVL,COUNT(*),ROUND(AVG(P_RETAILPRICE),2)FROM(SELECTLEVEL(node)ASLVL,P_RETAILPRICEFROMHIERARCHY(USINGPART_HIERARCHYAScJOINPARENTpONc.P_PARENT=p.P_PARTKEYSEARCHBYP_PARTKEYASCSTARTWHEREP_PARTKEY=1SETnode)ASH0)T1GROUPBYLVL""".stripMargin).collect().foreach(println)
901
903
913 912
904
911
+---+---+------------+|LEVEL|COUNT|AVG(P_RETAILPRICE)|+-----+-----+------------------+|0|1|901||1|2|903.5||2|3|912|+-----+-----+------------------+
valoptions=Map("dbschema"->config.user,"host"->config.host,"instance"->config.instance)#HANALiveCustomerBasicDataVirtualDataModelvalcustConf=options+("path"->s"""sap.hba.ecc/CustomerBasicData""")valcust=sqlContext.read.format("com.sap.spark.hana").options(custConf).load()cust.registerTempTable("customer")#HANALiveSalesOrderHeaderVDMvalsohConf=options+("path"->s"""sap.hba.ecc/SalesOrderHeader""")valsoh=sqlContext.read.format("com.sap.spark.hana").options(sohConf).load()soh.registerTempTable(soh)
#Top5CountriesbySalesOrderVolumesalesOrder=sqlContext.sql("select"Country",count(*)asFrequencyfromsalesOrderassLEFTOUTERJOINcustomerascons.soldToParty=c.CustomerGROUPBYCountryORDERBYFrequencydesc”)
© 2016 SAP SE or an SAP affiliate company. All rights reserved. Spark Summit New York, 2.17.16
Airline Use Case – Optimize MRO scheduling with Sensor Data
Challenges
• $10,000 loss for every hour spent on maintenance, repair, and overhaul (MRO)
• Predictive MRO generates TB of sensor data per flight
Solution
• SAP HANA Vora rapidly processes sensor data in HDFS and combines it with flight schedule and staffing data in SAP HANA to prioritize maintenance jobs and accelerate MRO
Why SAP HANA Vora
• Optimize MRO operations with interactive, on-demand drill down by airport, flight route, etc.
© 2016 SAP SE or an SAP affiliate company. All rights reserved. Spark Summit New York, 2.17.16
© 2016 SAP SE or an SAP affiliate company. All rights reserved. Spark Summit New York, 2.17.16
Utility Use Case – CenterPoint Energy
Challenge
• Smart meters generate TBs of data/month
• Regulatory requirement to retain data for 10 years
• Current storage solution full by end-2016
• Need to leverage HDFS as an additional tier for storage
Solution
• SAP HANA for most recent sensor signal and operational data, Dynamic Tiering for 1~2yrs old data, HDFS for historical sensor data
• SAP HANA Vora accesses and queries data across all tiers
Why SAP HANA Vora
• SAP HANA Vora provides enterprise analytics & OLAP like experience across data warehouse and HDFS.
© 2016 SAP SE or an SAP affiliate company. All rights reserved. Spark Summit New York, 2.17.16
© 2016 SAP SE or an SAP affiliate company. All rights reserved. Spark Summit New York, 2.17.16
Utility Use Case – How It Works CenterPoint Energy
Our benchmark tests proved that SAP HANA paired with SAP HANA Vora are the right solutions for us. We expect immediate cost benefits and to see competitive differentiation in the future.”
Gary Hayes, CIO & SVP at CenterPoint Energy
© 2016 SAP SE or an SAP affiliate company. All rights reserved. Spark Summit New York, 2.17.16
SAP HANA MOST RECENT SENSOR DATA
Dynamie Tiering
1-2 YR OLD DATA
Parallelized Queries
HDFS
HISTORICAL SENSOR DATA
Query data within and across tiers
© 2016 SAP SE or an SAP affiliate company. All rights reserved. Spark Summit New York, 2.17.16
Financial Services Use Case – Extend Fraud Pattern Detection
Challenges
• 100+ million business transactions daily, 25% growth YoY
• Limited access to archived data • Difficult to detect patterns in
historical transactions
Solution
• Current transactions in SAP HANA, historical transactions in HDFS clusters
• Real-time detection of abnormalities
Why SAP HANA Vora
• Real-time, aggregated insights from current and historical transactions
© 2016 SAP SE or an SAP affiliate company. All rights reserved. Spark Summit New York, 2.17.16
© 2016 SAP SE or an SAP affiliate company. All rights reserved. Spark Summit New York, 2.17.16
2016 and the Road Ahead
Customers in North America, APJ, and EMEA
Dev edition
available on AWS
TODAY
General Availability
Vora Modeler to build and query
OLAP style cubes on data
COMING SOON
Planning (HR, Financial) Extend engine support
for time series Transaction
management
Analytics on archived ERP data in Hadoop
FUTURE
© 2016 SAP SE or an SAP affiliate company. All rights reserved. Spark Summit New York, 2.17.16 © 2016 SAP SE or an SAP affiliate company. All rights reserved. Spark Summit New York, 2.17.16
Contribute to Spark Ecosystem, Embrace Best of Community Innovation
Contribution to Open Source:
Hierarchy capabilities
Connection to ERP: predicate pushdown to HANA
On-the-market solution
SAP HANA Vora
Thank you! Ken Tsai: [email protected] @kentsaiSAP
Enter to Win a GoPro HERO4
Session at SAP Booth 102
Learn More @ hana.sap.com/vora
Try Dev Edition bit.ly/1K1qLyo
We’re Hiring: https://spark-summit.org/east-2016/jobs/