Data Ninja Webinar Series: Realizing the Promise of Data Lakes
0 likes265 views
The document outlines a series of webinars focused on data virtualization solutions, emphasizing the benefits of integrating data lakes with enterprise data architectures. It discusses challenges with traditional data lakes, such as data silos and quality issues, and introduces the concept of a governed logical data lake that enhances data access and governance through virtualization. The goal is to provide organizations with improved data management, flexibility, and actionable insights while leveraging both new and legacy data sources.
Data Ninja Webinar Series: Realizing the Promise of Data Lakes
- 1. Realizing the Promise of Data Lakes webinars Data Ninja Webinar Series Sessions covering data virtualization solutions for driving business value
- 2. 2 Data Ninja Webinars Five webinars over the next few months…
- 3. Speakers Head of Product Marketing Lakshmi Randall
- 4. Agenda 1.Big Data Challenges 2.Limitations of Physical Data Lakes 3.The Governed Logical Data Lake 4.Benefits 5.Customer Case Studies 6.Q&A
- 6. 6 SOMEBODY BOUGHT SOMETHING “BACK IN THE DAY” • WE HAVE TO DEAL WITH LEGACY • HOMOGENEITY ISN’T REALISTIC • ALL DATA SOURCES ARE NO LONGER EQUAL
- 7. © 2016 Autodesk | Enterprise Information Services 7 The Data Lake alone is not a panacea Easy to put data in Wait! What about my old data warehouse? Harder to access and secure the data
- 8. 8 Duplicate sets of data in source system and data lake Creates data quality challenges because data must be updated in two places. Expensive since the data needs to be stored and maintained twice Governance of such large amounts of data can be challenging Restrictions on data access must be maintained as data is brought into the data lake as well as on new data created within the data lake. Data lakes themselves can become silos Often built for a specific department, data from the data lake must be integrated with other enterprise data to create a complete picture Limitations of Physical Data Lakes 8
- 9. 9 Overcoming the Limitations of Physical Data Lakes Integrate your Data Lake with other Enterprise Data Architectures Provides a way to access data from separate systems through an abstraction layer that makes it appear as if the data were in a single data lake Improves the enterprise func- tionality of data lakes by combining one or more physical data lakes with other enterprise data Improves an organization’s ability to govern and extract more value from its data lakes by extending them as logical data lakes 9 Implement a Single Logical Data Lake Using Data Virtualization
- 10. Marketing Data Lakes Research Logical Data Lake/Big Data Fabric Healthcare Self-Service Analytics Operational Apps A Single Governed Logical Data Lake Data Virtualization combines one or more physical data lakes with other enterprise data to create a “virtual” or “logical” data lake. Other Data Sources MDM Cloud Apps BI/Analytical Tools Excel Reports DATA VIRTUALIZATION Semantic Model Data Discovery Metadata Catalog Data Governance Denodo Platform Bridges Distinct Data Architectures 10 Discover Prepare Curate Orchestrate Integrate Publish “Rely on Data Integration Infrastructure to make the Data Lake Work.” Philip Russom, Analyst, TDWI
- 11. 11 Data Governance Data Lineage Structure and organization to your data lake Data Masking Data Quality Functions for validating, cleansing, enriching and standardizing data. SDK to integrate with external DQ tools and Big Data systems Enterprise Access Point Enterprise-level access controls – table, row, column Authentication/Authroization Roles Audit all access Encryption/Decryption Universal Semantic Model The Governed Data Lake
- 12. 12 Data Lineage Understand the “source of truth” and transformations of every piece of data in the model
- 13. Selective Data Masking (Full and Partial) 13
- 16. 16 Mature Data Virtualization Operational Data EDW SQL Integrated Security Other Sources Cache In-memory Fabric Big Data SaaS REST OData Catalog & Data Exploration Monitoring Auditing
- 17. 17 Performance Denodo’s unique query optimizer Denodo’s optimizer borrows many techniques from traditional RDBMs Query plans based on statistics and indexes Multiple JOIN methods Query rewriting to generate more optimal SQL However, given the distributed execution of a query in a processing fabric, Denodo has designed unique techniques to maximize performance in this environment Dynamic rewriting focused on maximizing execution at source and reduction of network traffic Cost estimates also factor-in: Processing power of the sources (e.g. number of nodes in a Hadoop cluster) Network and transfer rates
- 18. 18 1. A logical data lake prevents the data lake from becoming a silo and provides access to all the information an organization needs to power its analytics. 2. Data virtualization improves agility in big data activities. Users can quickly combine sources of information without spending time installing and configuring new databases or clusters to store the consolidated information. 3. This ease of use encourages exploration of data since the cost or effort to access the information is lower. 4. Data virtualization eliminates the cost of storing information twice and the need to update information in multiple places since information is not duplicated. Managing Data Lakes 18 Data virtualization is a practical strategy for managing data lakes
- 19. 19 Why do Enterprises need Denodo’s Big Data Fabric to succeed? New actionable insights with minimal effort Information Self-service for business users Secures big data end-to-end Real-time integrated data across the business Ability to aggregate, transform, cleanse, and integrate data from multiple big data sources, which can then be presented in dashboards, reporting tools, and web applications. Allows any application, process, dashboard, tool, or user to access any integrated data, regardless of where the data is physically or logically located and regardless of the data format. Offers consistent, timely, and trusted data for internal and external users. Enables centralized data access and control, and supports data-at- rest and data-in-motion security measures. Remediates security risks with masking, auditing, and encryption across the fabric. Provides self-service data discovery and search capabilities. Virtual Sandboxing for Citizen Users
- 20. Benefits
- 21. 21 -Source: “Predicts 2017: Data Distribution and Complexity Drive Information Infrastructure Modernization” By 2018, organizations with data virtualization capabilities will spend 40% less on building and managing data integration processes for connecting distributed data assets.”
- 22. Data Virtualization bridges the skills gap
- 23. Developers and users don’t need to learn special languages. They can leverage Denodo graphical user interface to model, unify and deliver the data to multiple consumers. Data Virtualization combines new and legacy data sources
- 24. 24 How the data goes in… How it gets back out… Denodo’s Big data fabric provides easy access to big data without having to decipher various data formats. Data Virtualization provides ease of use
- 25. 25 -Source: “Forrester Wave™: Big Data Fabric Q4 2016” Denodo’s key strength is delivering a unified and centralized data services fabric with security and real-time integration across multiple traditional and big data sources, including Hadoop, NoSQL, cloud, and software-as-a-service (SaaS).”
- 26. 26 -Source: “Forrester Wave™: Big Data Fabric Q4 2016” Today, several enterprises are leveraging Denodo to support big data fabric deployments — such as virtual big data marts, big data analytics, realtime analytics, and iot data processing — in various vertical industries. Customers like its easy-to-use, simple yet sophisticated data modeling capabilities, search, and support for various big data sources.
- 28. © 2016 Autodesk | Enterprise Information Services 28 Why Build the Logical Data Lake Data virtualization can be used throughout your data pipeline!
- 29. 29 Leading Construction Manufacturer - Telematics & Predictive Maintenance Dealer Maintenance Parts Inventory OSI PI Hadoop Cluster Tableau: Dealer / Customer Dashboard
- 30. 30 Enrich Machine Data and Combine with Other Data Ingest, Integrate & Deliver Persisted (In-memory, Hadoop) Streams (specific time window) Message Queue Machine-generated/Event data Alerts Workflows Operational Processes Analytical Processes Consumers Visualization Data Virtualization Enrich and Combine IoT Data with Other Data Historians Streams ERP/SCM DW Analytical DB MDM Apps Data Marts Hadoop NoSQL
- 31. 31 Business Benefits Improved asset performance and proactive maintenance. Reduced warranty costs due to proactive maintenance of parts preventing parts failure. Optimized pricing for services and parts among global service providers. New Business Model opportunities based on real-time analysis of detailed sensor data.
- 32. Q&A
- 33. Next Steps Forrester Wave™: Big Data Fabric Q4 2016 http://www.denodo.com/en/page/forrester-wave-big-data-fabric- q4-2016 Get Started! Download Denodo Express: www.denodoexpress.com Access Denodo Platform on AWS: www.denodo.com/en/denodo- platform/denodo-platform-for-aws
- 34. Data Ninja Webinar Series Sessions covering data virtualization solutions for driving business value Next Series: Packed Lunch Series January - 2017
- 35. Thanks! 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.