Verizon Centralizes Data into a Data Lake in Real Time for Analytics
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Verizon – Global Technology Services (GTS) was challenged by a multi-tier, labor-intensive process when trying to migrate data from disparate sources into a data lake to create financial reports and business insights. Join this session to learn more about how Verizon: • Easily accessed data from multiple sources including SAP data • Ingested data into major targets including Hadoop • Achieved real-time insights from data leveraging change data capture (CDC) technology • Reduced costs and labor
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Verizon Centralizes Data into a Data Lake in Real Time for Analytics
- 1. How we centralized data into a data lake for analytics
- 2. 2 © Hortonworks Inc. 2011 – 2017. All Rights Reserved Guest Speakers Arvind Rajagopalan Director – Global Technology Services – Verizon Jordan Martz Director of Technology Solutions - Attunity
- 3. © Verizon 2017 All Rights Reserved. Information contained herein is provided AS IS and subject to change without notice. All trademarks used herein are property of their respective owners. We are Verizon. Verizon delivers the promise of the digital world. • Fortune 500 rank: #14 • $29.8 billion in first-quarter revenue (2017) • 161,000 employees For first-quarter 2017: LTE covers 98% of U.S. population 113.9 M total retail connections LTE Advanced covers 466 markets Largest all-fiber Fios network 5.7 M Fios internet and 4.7 M Fios video connections 500 mbps upload and download speeds Global IP network 99% of Fortune 500 customers Products and solutions Innovating in entertainment, digital media, the Internet of Things and broadband service
- 4. © Verizon 2017 All Rights Reserved. Information contained herein is provided AS IS and subject to change without notice. All trademarks used herein are property of their respective owners. ‘Be Prepared’ – build architecture so you can: Analyze Everything Analyze Anywhere Analyze in Real-Time • 100’s to 1000’s of Data Sources • Business & Machine Data • On-premise or in the Cloud • In DB, DW, Hadoop, In-Memory, etc. • Capture new, changing data • Process/stream in motion
- 5. © Verizon 2017 All Rights Reserved. Information contained herein is provided AS IS and subject to change without notice. All trademarks used herein are property of their respective owners. Paradigm Shift: App-Centric Data-Centric DATA-CENTRIC Central Data Lake App1 App 2 App 3 App 4 App 5 App 6 APP-CENTRIC Limitations: • Multiple copies of data • Difficult cross-system integration • Limit on Data volumes Advantages: • One version of the data • No need for cross-app integration • System scales linearly
- 6. © Verizon 2017 All Rights Reserved. Information contained herein is provided AS IS and subject to change without notice. All trademarks used herein are property of their respective owners. Migrating to Hadoop – Types & Use Cases •Analyze data where it resides •Exploit Fault-tolerant, High-Performance Platforms for varying workloads •Push analytics to the front line ETL-Offload •Enable ELT Offload while reducing cost •Enable new forms and sources of data Self Service •Schema on Read •Transform and Model in place Data Reservoir Exploratory Lake Analytical Lake Active Archive Integrate & Converge Analytics & Data •Carry all History •Expand Depth and Breadth of DW •Expand Variety of Data
- 7. © Verizon 2017 All Rights Reserved. Information contained herein is provided AS IS and subject to change without notice. All trademarks used herein are property of their respective owners. Architecture in Motion Adaptable Architectures Data In Motion Enabling Real Time Scale Matters Reduce Impact, Increase Efficiency Breadth Matters Sources, targets, and in between Depth Matters When the going gets tough… Traceability Data Lineage Data Ingestion for Real-Time Analytics
- 8. © Verizon 2017 All Rights Reserved. Information contained herein is provided AS IS and subject to change without notice. All trademarks used herein are property of their respective owners. 8 Data Ingestion – Enhancement ✓ Data Ingestion with CDC ✓ Ingest Data directly to Hadoop ✓ Simplified Architecture (fewer hops, points of failure) ➢ Data consistency with time-based partitions ➢ Operational visibility with granular change tracking ➢ Automated data integration on Apache Hive ERP3 FINANCE DATA LAKE ERP2 ERP1ERP(SOURCE) Lambda Architecture Attunity Replicate
- 9. © 2017 Attunity Attunity Corporate Overview Data Integration & Big Data Management Software Accelerate data delivery and availability Automate data readiness for analytics Optimize data management with intelligence ▪Hadoop & Big Data ▪Databases & Data Warehouses ▪On premise & in the Cloud Solutions Global OfficesOverview ▪2000 customers in 65 countries ▪250 people and growing ▪NASDAQ traded (ATTU)
- 10. © 2017 Attunity Seamless integration with Hortonworks Connected Data platforms and solutions Hortonworks Connection Hortonworks Solutions Enterprise Data Warehouse Optimization Cyber Security and Threat Management Internet of Things and Streaming Analytics Hortonworks Connection Subscription Support SmartSense Premier Support Educational Services Professional Services Community Connection Cloud Hortonworks Data Cloud AWS HDInsight Data Center Hortonworks Data Suite HDFHDP
- 11. © 2017 Attunity Real-time Data Ingest with Attunity Replicate SOURCES OLTP, ERP, CRM Systems Documents, Emails Web Logs, Click Streams Social Networks Machine Generated Sensor Data Geolocation Data Attunity Replicate for HDP & HDF Accelerate time-to-insights by delivering solutions faster, with fresh data, from many sources - Automated data ingest - Incremental data ingest (CDC) - Support for multiple sources
- 12. © 2017 Attunity Attunity Replicate architecture Transfer TransformFilter Batch CDC Incremental In-Memory File Channel Batch Hadoop Files RDBMS Data Warehouse Mainframe Cloud On-prem Cloud On-prem Hadoop Files RDBMS Data Warehouse Kafka Persistent Store
- 13. © 2017 Attunity*Supported under early access program Attunity Replicate sources and targets RDBMS Oracle SQL Server DB2 iSeries DB2 z/OS DB2 LUW MySQL PostgreSQL Sybase ASE Informix DW Exadata Teradata Netezza Vertica Hortonworks Cloudera MapR Hadoop DB2 for z/OS IMS/DB VSAM SQL M/P Enscribe RMS HP NonStop Mainframe AWS RDS Salesforce Cloud RDBMS Oracle SQL Server DB2 LUW MySQL PostgreSQL Sybase ASE Informix DW Microsoft PDW Exadata Teradata Netezza Vertica Sybase IQ Amazon Redshift Actian Vector SAP HANA Hortonworks Cloudera MapR Pivotal Amazon EMR Hadoop MongoDB NoSQL Amazon RDS Amazon Redshift Amazon EMR Google Cloud SQL Google Cloud Dataproc Azure SQL Data Warehouse Azure SQL Database Cloud Azure Event Hubs* Kafka Messaging Targets Sources SAP ECC on Oracle ECC on SQL ECC on DB2* SAP HANA
- 14. © 2017 Attunity In Memory and File Optimized Data Transport CDC for data-at-rest and data-in-motion R1 R1 R2 R1 R2 R 1 R 2 Batch CDC Data Warehouse Ingest-Merge SQL n 2 1 SQL SQL Transactional CDC Message Encoded CDC Data Sources Attunity Replicate – Change Processing CDC Many Databases and Data Warehouses ....
- 15. © 2017 Attunity CDC Data Streaming into Kafka HDF HDP MSG n 2 1 MSG MSG Data Streaming Transaction logs In memory optimized metadata management and data transport Bulk Load MSG n 2 1 MSG MSG Data Streaming Message broker Message broker
- 16. © 2017 Attunity Attunity Replicate for SAP Universal, Real-Time and Simplified Data Integration • Replicate your SAP application data in bulk or real-time for data analytics ▪ Documents, transactions and business data ▪ All core and industry-specific SAP modules • Integrate real-time with all major targets ▪ DBs, data warehouses, Hadoop – cloud or on premises ▪ Decode SAP data from complex source structures ▪ Enable business usage on common data model • Move external data into SAP HANA Attunity Replicate Bulk Load CDC Core and Industry-Specific SAP Modules RDBMS | EDW | Hadoop On Premises or Cloud Hadoop Data Lake
- 17. © 2017 Attunity Attunity Replicate Server TransformFilter Batch CDC Incremental In-Memory File Channel Batch Attunity Replicate Persistent Store Extract relationships for Pool and Cluster Tables RDBMS (Oracle, DB2, etc.) Redo/ Archive logs or Journal File --------------- - Transparent Tables On Premises Hadoop RDBMS Data WarehouseKafka Cloud Attunity Replicate Agent for SAP SAP ECC (Enterprise Central Component) Data Model Mapping Pool/Cluster table RFC
- 18. © Verizon 2017 All Rights Reserved. Information contained herein is provided AS IS and subject to change without notice. All trademarks used herein are property of their respective owners. Use Cases 18 • Working Capital Analytics • Spend Analytics • Labor Reporting • Audit & Compliance • Capital Reporting & Analytics • Active Archival of legacy data
- 19. © Verizon 2017 All Rights Reserved. Information contained herein is provided AS IS and subject to change without notice. All trademarks used herein are property of their respective owners. Data Governance Considerations for Migration MDM Integration Bidirectional, tagging & Linking tools, which highlight the Relationships in Data Data Quality Incoming data needs to discover contradictions, inconsistencies, & redundancies Security Policy Process authentication, authorization, encryption, & monitoring Data Masking Access to sensitive Data has regulatory & additional auditing
- 20. © Verizon 2017 All Rights Reserved. Information contained herein is provided AS IS and subject to change without notice. All trademarks used herein are property of their respective owners. What’s Next? Delivering real-time insights & analytics opening up new use cases: • TCO Analysis • Reducing Close Cycles • Revenue Analysis • EDW Offload