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Building a Single Logical Data Lake: For Advanced Analytics, Data Science, and Business Operations

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Denodo
Denodo

Ravi Shankar, CMO at Denodo's presentation from joint event with Deloitte in Sydney on 3rd September, 2018.

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Building a Single Logical Data Lake For Advanced Analytics, Data Science, and Business Operations Ravi Shankar, CMO September, 2018
2 • Competition from a low cost vendor • Lower the price, affecting margins? • Or, maintain high price, but differentiate in other ways?
3 Benefits Large Heavy Equipment Manufacturer Self-service / Predictive Analytics – IoT Integration Improved asset performance and proactive maintenance Increased revenue from sale of services and parts Reduced warranty costs of parts failure
4 “Big Data Challenges Impede Big Data Vision”
5 IT – Business Dilemma IT Architecture is Unmanageable & Brittle because: IT Focuses on Data Collection & Storage Business Focuses on Data Visualization & Analysis No One Focused on Data Delivery – So create 100’s to 1K’s of brittle direct connections and replicate large volumes of data Inventory System (MS SQL Server) Product Catalog (Web Service -SOAP) BI / Reporting JDBC, ODBC, ADO .NET Web / Mobile WS – REST JSON, XML, HTML, RSSLog files (.txt/.log files) CRM (MySQL) Billing System (Web Service - Rest) ETL Portals JSR168 / 286, Ms Web Parts SOA, Middleware, Enterprise Apps WS – SOAP Java API Customer Voice (Internet, Unstruc)
6 Big Data Fabric Architecture – Forrester Research, 2016
7 Big Data Fabric – Data Abstraction Layer Abstracts access to disparate data sources Acts as a single repository (virtual) Makes data available in real-time to consumers
8 Consume in business applications Combine related data into views 2 3 DATA CONSUMERS Enterprise Applications, Reporting, BI, Portals, ESB, Mobile, Web, Users, IoT/Streaming Data Connect to disparate data sources 1 DISPARATE DATA SOURCES Databases & Warehouses, Cloud/Saas Applications, Big Data, NoSQL, Web, XML, Excel, PDF, Word... Less StructuredMore Structured Multiple protocols, formats Linked data services query, search, browse Request/Reply, event driven Secure delivery Library of wrappers Web automation Any data or content Read & Write DATA VIRTUALIZATION DATA CONSUMERSAnalytical Operational CONNECT COMBINE CONSUME Share, Deliver, Publish, Govern, Collaborate Discover, Transform, Prepare, Improve Quality, Integrate Normalized views of disparate data Agile Development Performance Resource Management Lifecycle Management Data Services Data Catalog Governance & Metadata Security & Data Privacy
9 Logical Data Lake – Use Cases Data Warehouse OffloadingIoT Integration
10 Data Virtualization in the IoT Ecosystem Other RDBMS (apps, CRM, SAP) Other Sources (SaaS, SFDC, etc.) Ingestion Streaming analytics Big Data Storage Batch analytics, Machine learning Streaming data Traditional batch processing (ETL to EDW) Semantic Model Secure + Combine + Enrich
11 DV Layer @ Edge DV Layer @ Data Center/Cloud Lightweight DV Layer Beyond Edge Layer 1 Layer 2 Layer 3 Layer 4 Edge Computing Using Data Virtualization
12 Six Essential Capabilities of Data Virtualization 4. Self-service data services 4. Centralized metadata, security & governance 4. Location-agnostic architecture for multi-cloud, hybrid acceleration 1. Data abstraction 2. Zero replication, zero relocation 3. Real-time information
13 Big Data Queries Faster with Denodo Platform 1. Data Virtualization delivers better performance without needing to replicate data into Hadoop. 2. Data Virtualization leverages Data Source Architectures for what they are good at. Performance comparison of 5 different queries Impala Hadoop-only Runtime (s) Denodo Runtime (s) Denodo Runtime w/ Cache (s) Data Volumes Query 1 199 120 68 Queries 1,2,3,5 •Exadata Row Count: ~5M •Impala Row Count: ~500k Query 4 •Exadata Row Count: ~5M •Impala Row Count: ~2M Query 2 187 96 88 Query 3 120 212 115 Query 4 timeout 328 69 Query 5 46 91 56
14 Denodo Dynamic Query Optimizer System Execution Time Data Transferred Optimization Technique Denodo 9 sec. 4 M Aggregation push-down Without Denodo 125 sec. 292 M None: full scan SELECT c.id, SUM(s.amount) as total FROM customer c JOIN sales s ON c.id = s.customer_id GROUP BY c.id 290 M 2 M Sales Customer 2 M 2 M Sales Customer join group by join group by
15 join Group by ZIP join Group by ZIP Data Virtualization with Massively Parallel Processing 2M rows (sales by customer) Customer (2M rows) System Execution Time Optimization Techniques Others ~ 10 min Traditional Federation Tools No MPP 43 sec Aggregation push-down With MPP 11 sec Aggregation push-down + MPP integration (Impala 8 nodes) Sales (300 million rows) join Group by ZIP 1. Partial Aggregation Push-down Maximizes source processing Reduces network traffic 3. On-demand data transfer For SQL-on-Hadoop systems, Denodo automatically generates and uploads Parquet files 4. Integration with local and pre-cached data The optimization engine detects when data is cached, or when it is a native table in the MPP 2. Integrated with Cost-based Optimizer Based on data volume estimation and the cost of these particular operations, CBO can decide to move all or part of the execution tree to the MPP 5. Fast parallel execution Support for Spark, Presto, and Impala for fast analytical processing using inexpensive Hadoop-based solutions With MPP Integration group by customer ID
16 Customer-reported projected savings by percentage ROI and TCO of Data Virtualization Data Integration Cost reduction ▪ 60-80% savings Traditional Call Centres, Portals ▪ 30-70% savings BI and Reporting ▪ 40-60% savings ETL and Data Warehousing ▪ Project timelines of 6-12 months reduced to 3-6 months ▪ Up to 85% reduction in time • New sources can be configured in minutes, and fully integrated within days. • 100’s of application entities can be integrated within weeks. • New business functionality can be added within days. • Existing functionality can be enhanced with new data within days. • Data proliferation can be significantly reduced. • Common, consistent and timely access to all data via preferred visualization tools.
17 Customer Centricity / MDM ✓ Complete View of Customer Data Services ✓ Data as a Service ✓ Data Marketplace ✓ Data Services ✓ Application and Data Migration Cloud Solutions Data Governance ✓ GRC ✓ GDPR ✓ Data Privacy / Masking BI and Analytics ✓ Self-Service Analytics ✓ Logical Data Warehouse ✓ Enterprise Data Fabric Big Data ✓ Logical Data Lake ✓ Data Warehouse Offloading ✓ IoT Analytics ✓ Cloud Modernization ✓ Cloud Analytics ✓ Hybrid Data Fabric Denodo ‘Horizontal Solution’ Categories
18 Autodesk – Integration of Streaming Data AUTODESK 2017 CIO 100 Award AUTODESK FINALIST in 2017 Excellence Awards
19 Asurion: Data Virtualization On-Premises and Cloud ASURION 2017 Best Practices Award
20 Denodo The Leader in Data Virtualization DENODO OFFICES, CUSTOMERS, PARTNERS Palo Alto, CA. Global presence throughout North America, EMEA, APAC, and Latin America. LEADERSHIP ▪ Longest continuous focus on data virtualization – since 1999 ▪ Leader in 2018 Forrester Wave – Big Data Fabric ▪ Winner of numerous awards CUSTOMERS ~500 customers, including many F500 and G2000 companies across every major industry have gained significant business agility and ROI. FINANCIALS Backed by $4B+ private equity firm. 50+% annual growth; Profitable.
www.denodo.com info@denodo.com © Copyright Denodo Technologies. All rights reserved Unless otherwise specified, no part of this PDF file may be reproduced or utilized in any for or by any means, electronic or mechanical, including photocopying and microfilm, without prior the written authorization from Denodo Technologies.

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Building a Single Logical Data Lake: For Advanced Analytics, Data Science, and Business Operations

  • 1. Building a Single Logical Data Lake For Advanced Analytics, Data Science, and Business Operations Ravi Shankar, CMO September, 2018
  • 2. 2 • Competition from a low cost vendor • Lower the price, affecting margins? • Or, maintain high price, but differentiate in other ways?
  • 3. 3 Benefits Large Heavy Equipment Manufacturer Self-service / Predictive Analytics – IoT Integration Improved asset performance and proactive maintenance Increased revenue from sale of services and parts Reduced warranty costs of parts failure
  • 4. 4 “Big Data Challenges Impede Big Data Vision”
  • 5. 5 IT – Business Dilemma IT Architecture is Unmanageable & Brittle because: IT Focuses on Data Collection & Storage Business Focuses on Data Visualization & Analysis No One Focused on Data Delivery – So create 100’s to 1K’s of brittle direct connections and replicate large volumes of data Inventory System (MS SQL Server) Product Catalog (Web Service -SOAP) BI / Reporting JDBC, ODBC, ADO .NET Web / Mobile WS – REST JSON, XML, HTML, RSSLog files (.txt/.log files) CRM (MySQL) Billing System (Web Service - Rest) ETL Portals JSR168 / 286, Ms Web Parts SOA, Middleware, Enterprise Apps WS – SOAP Java API Customer Voice (Internet, Unstruc)
  • 6. 6 Big Data Fabric Architecture – Forrester Research, 2016
  • 7. 7 Big Data Fabric – Data Abstraction Layer Abstracts access to disparate data sources Acts as a single repository (virtual) Makes data available in real-time to consumers
  • 8. 8 Consume in business applications Combine related data into views 2 3 DATA CONSUMERS Enterprise Applications, Reporting, BI, Portals, ESB, Mobile, Web, Users, IoT/Streaming Data Connect to disparate data sources 1 DISPARATE DATA SOURCES Databases & Warehouses, Cloud/Saas Applications, Big Data, NoSQL, Web, XML, Excel, PDF, Word... Less StructuredMore Structured Multiple protocols, formats Linked data services query, search, browse Request/Reply, event driven Secure delivery Library of wrappers Web automation Any data or content Read & Write DATA VIRTUALIZATION DATA CONSUMERSAnalytical Operational CONNECT COMBINE CONSUME Share, Deliver, Publish, Govern, Collaborate Discover, Transform, Prepare, Improve Quality, Integrate Normalized views of disparate data Agile Development Performance Resource Management Lifecycle Management Data Services Data Catalog Governance & Metadata Security & Data Privacy
  • 9. 9 Logical Data Lake – Use Cases Data Warehouse OffloadingIoT Integration
  • 10. 10 Data Virtualization in the IoT Ecosystem Other RDBMS (apps, CRM, SAP) Other Sources (SaaS, SFDC, etc.) Ingestion Streaming analytics Big Data Storage Batch analytics, Machine learning Streaming data Traditional batch processing (ETL to EDW) Semantic Model Secure + Combine + Enrich
  • 11. 11 DV Layer @ Edge DV Layer @ Data Center/Cloud Lightweight DV Layer Beyond Edge Layer 1 Layer 2 Layer 3 Layer 4 Edge Computing Using Data Virtualization
  • 12. 12 Six Essential Capabilities of Data Virtualization 4. Self-service data services 4. Centralized metadata, security & governance 4. Location-agnostic architecture for multi-cloud, hybrid acceleration 1. Data abstraction 2. Zero replication, zero relocation 3. Real-time information
  • 13. 13 Big Data Queries Faster with Denodo Platform 1. Data Virtualization delivers better performance without needing to replicate data into Hadoop. 2. Data Virtualization leverages Data Source Architectures for what they are good at. Performance comparison of 5 different queries Impala Hadoop-only Runtime (s) Denodo Runtime (s) Denodo Runtime w/ Cache (s) Data Volumes Query 1 199 120 68 Queries 1,2,3,5 •Exadata Row Count: ~5M •Impala Row Count: ~500k Query 4 •Exadata Row Count: ~5M •Impala Row Count: ~2M Query 2 187 96 88 Query 3 120 212 115 Query 4 timeout 328 69 Query 5 46 91 56
  • 14. 14 Denodo Dynamic Query Optimizer System Execution Time Data Transferred Optimization Technique Denodo 9 sec. 4 M Aggregation push-down Without Denodo 125 sec. 292 M None: full scan SELECT c.id, SUM(s.amount) as total FROM customer c JOIN sales s ON c.id = s.customer_id GROUP BY c.id 290 M 2 M Sales Customer 2 M 2 M Sales Customer join group by join group by
  • 15. 15 join Group by ZIP join Group by ZIP Data Virtualization with Massively Parallel Processing 2M rows (sales by customer) Customer (2M rows) System Execution Time Optimization Techniques Others ~ 10 min Traditional Federation Tools No MPP 43 sec Aggregation push-down With MPP 11 sec Aggregation push-down + MPP integration (Impala 8 nodes) Sales (300 million rows) join Group by ZIP 1. Partial Aggregation Push-down Maximizes source processing Reduces network traffic 3. On-demand data transfer For SQL-on-Hadoop systems, Denodo automatically generates and uploads Parquet files 4. Integration with local and pre-cached data The optimization engine detects when data is cached, or when it is a native table in the MPP 2. Integrated with Cost-based Optimizer Based on data volume estimation and the cost of these particular operations, CBO can decide to move all or part of the execution tree to the MPP 5. Fast parallel execution Support for Spark, Presto, and Impala for fast analytical processing using inexpensive Hadoop-based solutions With MPP Integration group by customer ID
  • 16. 16 Customer-reported projected savings by percentage ROI and TCO of Data Virtualization Data Integration Cost reduction ▪ 60-80% savings Traditional Call Centres, Portals ▪ 30-70% savings BI and Reporting ▪ 40-60% savings ETL and Data Warehousing ▪ Project timelines of 6-12 months reduced to 3-6 months ▪ Up to 85% reduction in time • New sources can be configured in minutes, and fully integrated within days. • 100’s of application entities can be integrated within weeks. • New business functionality can be added within days. • Existing functionality can be enhanced with new data within days. • Data proliferation can be significantly reduced. • Common, consistent and timely access to all data via preferred visualization tools.
  • 17. 17 Customer Centricity / MDM ✓ Complete View of Customer Data Services ✓ Data as a Service ✓ Data Marketplace ✓ Data Services ✓ Application and Data Migration Cloud Solutions Data Governance ✓ GRC ✓ GDPR ✓ Data Privacy / Masking BI and Analytics ✓ Self-Service Analytics ✓ Logical Data Warehouse ✓ Enterprise Data Fabric Big Data ✓ Logical Data Lake ✓ Data Warehouse Offloading ✓ IoT Analytics ✓ Cloud Modernization ✓ Cloud Analytics ✓ Hybrid Data Fabric Denodo ‘Horizontal Solution’ Categories
  • 18. 18 Autodesk – Integration of Streaming Data AUTODESK 2017 CIO 100 Award AUTODESK FINALIST in 2017 Excellence Awards
  • 19. 19 Asurion: Data Virtualization On-Premises and Cloud ASURION 2017 Best Practices Award
  • 20. 20 Denodo The Leader in Data Virtualization DENODO OFFICES, CUSTOMERS, PARTNERS Palo Alto, CA. Global presence throughout North America, EMEA, APAC, and Latin America. LEADERSHIP ▪ Longest continuous focus on data virtualization – since 1999 ▪ Leader in 2018 Forrester Wave – Big Data Fabric ▪ Winner of numerous awards CUSTOMERS ~500 customers, including many F500 and G2000 companies across every major industry have gained significant business agility and ROI. FINANCIALS Backed by $4B+ private equity firm. 50+% annual growth; Profitable.
  • 21. www.denodo.com info@denodo.com © Copyright Denodo Technologies. All rights reserved Unless otherwise specified, no part of this PDF file may be reproduced or utilized in any for or by any means, electronic or mechanical, including photocopying and microfilm, without prior the written authorization from Denodo Technologies.