Syncsort, Tableau, & Cloudera present: Break the Barriers to Big Data Insight

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Information about Syncsort, Tableau, & Cloudera present: Break the Barriers to Big Data...
Technology

Published on March 10, 2014

Author: cloudera

Source: slideshare.net

Description

Rethink data management and learn how to break down barriers to Big Data insight with Cloudera's enterprise data hub (EDH), Syncsort offload solutions, and Tableau Software visualization and analytics.

1 ©2014Cloudera, Inc. All rights reserved.1 ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved. ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

2 Agenda ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved. • Data Warehouse Vision & Reality • What is legacy data & why an Enterprise Data Hub • Offloading legacy data and workloads to Hadoop • Transform all types of data into self-service analytics • Live Demonstration • Customer case study • Q&A

3 What is this? ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.3

4 Real-Time Mainframe Oracle ERP ETL ETL Data Mart Data Warehouse File XML The Data Warehouse Vision -1998 4 Data Integration & ETL Tools would enable a Single, Consistent Version of the Truth Data Mart Data Mart ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

5 Data Warehouse Reality 2014 5 Real-Time Mainframe Oracle ERP ETL ETL Data Mart File XML Data Integration & ETL Tools would enable a Single, Consistent Version of the Truth Data Mart Data Mart Dormant Data Staging / ELT New Reports SLA’s New Column Complete History ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

6 The Data Warehouse Vision vs Reality Fresher data Longer history data Faster analytics More data sources Lower costs Longer ELT batch windows Shorter data retention Slower queries Weeks/months just to add new data fields Growing costs Vision Reality ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

7 Mainframes | A Critical Source of Big Data 7 Top 25 World Banks 9 of World’s Top Insurers 23 of Top 25 US Retailers 71% Fortune 500 30 Billion Bus. Transactions / day ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

8 Suits & Hoodies – Working Together 8 Integration Gaps Expertise Gaps • COBOL appeared in 1959, Hadoop in 2005 • Mainframe & Hadoop skills shortage Security Gaps • Hosts mission critical sensitive data • Very difficult to install new software on MF Costs Gaps • Mainframe data is (expensive) Big Data • Even FTP costs CPU cycles (MIPS) • Connectivity • Data conversion (EBCDIC vs ASCII) Suits & Hoodies idea: Merv Adrian, Gartner Research. ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

9 Expanding Data Requires A New Approach 9 1980s Bring Data to Compute Now Bring Compute to Data Relative size & complexity Data Information-centric businesses use all data: Multi-structured, internal & external data of all types Compute Compute Compute Process-centric businesses use: • Structured data mainly • Internal data only • “Important” data only Compute Compute Compute Data Data Data Data ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

10 From Apache Hadoop to an enterprise data hub 10 Open Source Scalable Flexible Cost-Effective ✔ Managed Open Architecture Secure and Governed ✖ ✖ ✖ BATCH PROCESSING STORAGE FOR ANY TYPE OF DATA UNIFIED, ELASTIC, RESILIENT, SECURE FILESYSTEM MAPREDUCE HDFS Core Apache Hadoop is great, but… 1) Hard to use and manage. 2) Only supports batch processing. 3) Not comprehensively secure. ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

11 From Apache Hadoop to an enterprise data hub 11 Open Source Scalable Flexible Cost-Effective ✔ Managed Open Architecture Secure and Governed ✔ BATCH PROCESSING STORAGE FOR ANY TYPE OF DATA UNIFIED, ELASTIC, RESILIENT, SECURE SYSTEM MANAGEMENT FILESYSTEM MAPREDUCE HDFS CLOUDERAMANAGER ✖ ✖ ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

12 From Apache Hadoop to an enterprise data hub 12 Open Source Scalable Flexible Cost-Effective ✔ Managed Open Architecture Secure and Governed ✔ ✔ BATCH PROCESSING ANALYTIC SQL SEARCH ENGINE MACHINE LEARNING STREAM PROCESSING 3RD PARTY APPS WORKLOAD MANAGEMENT STORAGE FOR ANY TYPE OF DATA UNIFIED, ELASTIC, RESILIENT, SECURE SYSTEM MANAGEMENT FILESYSTEM ONLINE NOSQL MAPREDUCE IMPALA SOLR SPARK SPARK STREAMING YARN HDFS HBASE CLOUDERAMANAGER ✖ ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

13 From Apache Hadoop to an enterprise data hub 13 Open Source Scalable Flexible Cost-Effective ✔ Managed Open Architecture Secure and Governed ✔ ✔ ✔ BATCH PROCESSING ANALYTIC SQL SEARCH ENGINE MACHINE LEARNING STREAM PROCESSING 3RD PARTY APPS WORKLOAD MANAGEMENT STORAGE FOR ANY TYPE OF DATA UNIFIED, ELASTIC, RESILIENT, SECURE DATA MANAGEMENT SYSTEM MANAGEMENT FILESYSTEM ONLINE NOSQL MAPREDUCE IMPALA SOLR SPARK SPARK STREAMING YARN HDFS HBASE CLOUDERANAVIGATORCLOUDERAMANAGER SENTRY ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

14 From Apache Hadoop to an enterprise data hub 14 Open Source Scalable Flexible Cost-Effective ✔ Managed Open Architecture Secure and Governed ✔ ✔ ✔ BATCH PROCESSING ANALYTIC SQL SEARCH ENGINE MACHINE LEARNING STREAM PROCESSING 3RD PARTY APPS WORKLOAD MANAGEMENT STORAGE FOR ANY TYPE OF DATA UNIFIED, ELASTIC, RESILIENT, SECURE DATA MANAGEMENT SYSTEM MANAGEMENT CLOUDERA’S ENTERPRISE DATA HUB FILESYSTEM ONLINE NOSQL MAPREDUCE IMPALA SOLR SPARK SPARK STREAMING YARN HDFS HBASE CLOUDERANAVIGATORCLOUDERAMANAGER SENTRY ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

15 Partners Proactive & Predictive Support Professional Services Training Cloudera: Your Trusted Advisor for Big Data 15 Advance from Strategy to ROI with Best Practices and Peak Performance ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

16 ©2014Cloudera, Inc. All rights reserved.16 ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

17 The Impact of ELT & Dormant Data on the EDW 17 ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.  ELT drives up to 80% of database capacity  Dormant – rarely used data – waste premium storage  ETL/ELT processes on dormant data waste premium CPU cycles Hot Warm Cold Data Transformations (ELT) of unused data

1818 ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

19 Where to Start? 19 How to identify dormant data? What workloads will deliver the biggest impact? How will you access & move all your data? Can you secure the new environment? How do you optimize it? How do you manage it? How do you make it business-class? What tools do you need? How will you leverage all your data, including mainframes? ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

2020 Offload Legacy Data & Workloads to The Enterprise Data Hub Phase III: Optimize & Secure Phase II: Offload Phase I: Identify One Framework. Blazing Performance, Iron-Clad Security, Disruptive Economics • Identify data & workloads most suitable for offload • Focus on those that will deliver maximum savings & performance • Access and move virtually any data e.g. mainframe to Enterprise Data Hub with one tool • Easily replicate existing staging workloads in Hadoop using a graphical user interface • Deploy on premises and in Cloud • Optimize the new environment • Manage & secure all your data with business class tools • Deliver self-service reporting ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

21

22 The Problem: Volume of DataBusinesses are struggling to unlock exploding data ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

23 The Problem: Diverse DataBusinesses and their people are struggling to unlock diverse data ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

24 The Problem: Old School Software Traditional technologies are complicated, inflexible and slow moving ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

25 The Tableau RevolutionFast and easy analytics for everyone ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

26 FlexibleTransform all types of data into self-service analytics ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

27 For EveryoneEase of use leads to adoption across all departments and use cases ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

28 •LIVE DEMO ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

29 Case Study: Optimize EDW Leading Financial Org 29 0 50 100 150 200 250 ElapsedTime(m) HiveQL 217 min Syncsort DMX-h 9 min HiveQL 217 min Mainframe Offload (74-page COBOL copybook) Development Effort Syncsort DMX-h: 4 hrs. Manual Coding: Weeks! Benefits:  Cut development time from weeks to hours  Reduced complexity 47 HiveQL scripts to 4 DMX-h graphical jobs  Easily validate COBOL copybooks and find errors  Mainframe Data available to business for analytics  Staging & ELT moved out of RDBMS – Queries run faster ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

3030 Final Thoughts.. Rusty Sears ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved. Vice President of Enterprise Data Services and Big Data at Regions Financial Corporation

31 ©2014Cloudera, Inc. All rights reserved.31 QUESTIONS? ©2014Cloudera, Syncsort, Tableau Inc. All rights reserved.

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