Evolving From Monolithic to Distributed Architecture Patterns in the Cloud

DATA VIRTUALIZATION PACKED LUNCH WEBINAR SERIES Sessions Covering Key Data Integration Challenges Solved with Data Virtualization

Evolving from Monolithic to Distributed Architecture Patterns in the Cloud Paul Moxon VP Data Architectures & Chief Evangelist, Denodo Ruben Fernandez Sales Engineer, Denodo

Agenda 1. The Cloud Changes Everything 2. Accelerating Cloud Migration & Modernization 3. Customer Case Study 4. Demo – Denodo 7.0 5. Q&A 3

The Cloud Changes Everything 4

Data Integration – “The Way We Were…” 5

Data Integration – A Modern Data Ecosystem 6

Gartner: Predicts 2018: Data Management Strategies Continue to Shift Toward Distributed …..the collection of data as well as the need to connect to data are rapidly becoming the new normal, and that the days of a single data store with all the data of interest — the enterprise data warehouse — are long gone.”

Rightscale, 2017 State of the Cloud Report 85 percent of enterprises have a multi- cloud strategy, up from 82 percent in 2016”

A (Typical) Cloud Journey 11On-Premise On-Premise + SaaS On-Premise + SaaS + Private Cloud On-Premise + SaaS + Private Cloud + iPaaS Cloud Native

Distributed Cloud Data Architectures • Pure Cloud (“Cloud Native”) • Single Cloud provider • Private or public Cloud • Hybrid • Cloud and on-premise • Pure Cloud…but multiple Cloud providers • Data in multiple data stores, multiple locations • Applications (SaaS) storing data in Cloud 12

Challenges in Cloud Data Integration • Data is in many locations, data repositories, formats, etc. • Cloud, on-premise, SaaS, … • How do they know what data is available? • How to users find and access the data? • Simple tasks become more challenging as the data gets more dispersed 13

Accelerating Cloud Migration & Modernization 14

Application Modernization with the Cloud • Moving from legacy – typically monolithic – applications and application suites deployed on-premise to specialized SaaS applications in the Cloud • e.g. from Oracle E-Business Suite, PeopleSoft, Siebel, etc. • e.g. to Salesforce, NetSuite, Workday, Taleo, etc. • Cost savings can be substantial 15

Application Modernization (Cont’d) Data challenges for application modernization: • How do you access the data in the SaaS applications? • How do you get a holistic view of data in specialized applications? • How do you get data into the SaaS apps? • Are you going to give users access to each and every SaaS application? This is where Cloud Data Virtualization can play a significant role 16

Denodo’s Cloud Data Virtualization Architecture 17 Execution Agent Execution Agent Metadata Repository Execution Engine & Optimizer Data center A Data center B Corporate Security Monitoring & Auditing “The Cloud”

Application Modernization – Bio-Tech Company 18 Data Virtualization Web, Cloud and SaaS • HR Performance • Compliance • Recruitment • Workforce Mang. • Compensation H R M S • Core HR Functions H R M S Internal Enterprise Systems • Recruitment • Workforce Management • Compensation Moving to the Cloud …

Data Migration to Cloud Moving data sources from on premise to Cloud – or even from Cloud to Cloud • Using Data Virtualization as an abstraction layer to isolate the business from the effects of the change • Using Data Virtualization as a hybrid data access layer to access data, whether on- premise or in the Cloud 19

Data Migration to Cloud - Asurion 20 • Growing internationally, moving into different privacy and data protection jurisdictions • New products – need for different data types and sources • Mixing structured, multi-structured, streaming, text, video, voice, geo-location, etc. • Moving to Cloud for increased speed and agility • Easier to spin up new virtual servers for new data sets • Competing pressures for securing data and providing access to data sets

Data Migration to Cloud - Asurion 21 Security Constraints Geographical Constraints Contractual Client Obligations PII Protection Departmental Restrictions Fast Changing Hadoop & Cloud Technologies Hive, Spark, Redshift Maintaining different code base Discover, Co-relate, Enable Predictive Analytics Text, CSV, Voice, JSON, Streaming, 3rd Party Data 60TB+ structured, 200TB+ telemetry & unstructured data

Data Migration to Cloud - Asurion 22

Cloud Data Integration - Logitech 24 • Logitech struggling with scalability of Exadata data warehouse • Too expensive to scale up with more data and higher workloads • Needed to move to Cloud for increased speed and agility • Easier to dynamically scale for changing workloads • Wanted analytical engines running on AWS for speed and agility • Redshift, AWS EMR, Spark, etc. • But some data staying on-premises • Needed platform to bridge Cloud and on-premise and to enable the migration with minimal impact on business

Cloud Data Integration - Logitech 25

Logitech Solution Architecture 26

Logitech - Benefits 27 Data Fest Presentation by Tekin Mentes, Enterprise Data Architect & Data Evangelist, Logitech

Product Demonstration Evolving From Monolithic to Distributed Architecture Patterns in the Cloud Ruben Fernandez Sales Engineer, Denodo

Combine Cloud EDW with other Cloud sources (Cloud SaaS, Hadoop, etc.) for agile reporting and analytics Benefits • Fresh data coming straight from Cloud systems • Avoid local replication of cloud systems Example Report: Sales volume per lead source in last 30 days (EDW + CRM) Scenario: Cloud EDW + SaaS CRM + Cloud Hadoop 29 Event (dim) User (dim) Customer feedback Semantic Model Original Model Sales (fact)Leads Amazon Redshift Amazon EMRCRM

Performance & Optimizations SELECT u.state AS state, SUM(s.pricepaid) AS sales_total FROM sales s JOIN users u ON s.buyerid = u.userid JOIN salesforce_lead l ON u.email = l.email WHERE l.leadsource= 'Web' GROUP BY u.state; System Execution Time Data Transferred Denodo 3 sec. 34k Non-optimized 8 min 100 M 100 M 2k join group by 32k join group by email Group by state join join optimizer 2k

Summary • Moving to Cloud can be disruptive • Data Virtualization can help minimize the impact on business by isolating the changes • Without proper hybrid integration layer, Cloud apps and databases can become yet more silos • Cloud Data Virtualization can open up these silos and allow users to access all data, anywhere • If you’re struggling with integration of Cloud data, you might lose the Cloud benefits of lower TCO and agility • Cloud Data Virtualization can improve agility, lower TCO and help ensure the benefits of Cloud Modernization 32

Summary • Benefits of using Denodo Platform include: • Isolate business from changes in underlying infrastructure • e.g. moving from Teradata to Snowflake • Provides hybrid data access layer • Access all data from anywhere - Cloud-to-Ground, Cloud-to-Cloud • Common and consistent governance and security across all data • Enable speed and agility in new environment • Avoid expensive Cloud ‘data egress’ charges • Move processing to the data and not the data to the processor 33

Next steps Access Denodo Platform for Azure: https://www.denodo.com/en/denodo- platform/denodo-platform-for-azure Access Denodo Platform on AWS: www.denodo.com/en/denodo-platform/denodo- platform-for-aws

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Evolving From Monolithic to Distributed Architecture Patterns in the Cloud

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Evolving From Monolithic to Distributed Architecture Patterns in the Cloud