Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)
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The document discusses the concept of logical data lakes and their evolution to address data integration challenges, emphasizing the shift from single-purpose to multi-purpose data lakes. It highlights the role of data virtualization in improving accessibility, governance, and user experience within data lakes, while also noting the challenges of traditional physical data lakes. Key takeaways include the benefits of logical data architectures for diverse user needs and how data virtualization enhances the functionality and ROI of data lakes.
Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)
- 1. DATA VIRTUALIZATION APAC WEBINAR SERIES Sessions Covering Key Data Integration Challenges Solved with Data Virtualization
- 2. Logical data lakes: From single purpose to multipurpose data lakes Chris Day Director, APAC Sales Engineering, Denodo Sushant Kumar Product Marketing Manager, Denodo
- 3. Agenda Logical data lakes: From single purpose to multipurpose data lakes Product Demo Q&A Next Steps
- 4. Logical data lakes: From single purpose to multipurpose data lakes 4 Product Marketing Manager, Denodo Sushant Kumar
- 5. 5 A data lake is a storage repository that holds a vast amount of raw data in its native format. The data structure and requirements are not defined until the data is needed The current needs for sophisticated data- driven intelligence and data science favored this concept for its simplicity and power Hadoop and its ecosystem provided the foundation that data lakes required: vast storage and processing muscle It also favored the concept of ELT vs ETL: load data first, (maybe) Data Lakes
- 6. 6 The early data scientists saw Hadoop as their personal supercomputer. Hadoop-based Data Lakes helped democratize access to state of the art supercomputing with off-the- shelf HW (and later cloud) The industry push for BI made Hadoop–based solutions the standard to bring modern analytics to any corporation Data Lakes – A Data Scientist’s Playground
- 7. 7 Data Lakes – Not a Perfect World Physical Nature • Based on Replication. Data Lakes require data to be copied to its physical storage • Replication extends development cycles and costs • Not all data is suitable for replication • Real time needs: Cloud and SaaS APIs • Large volumes: existing EDW • Laws and restrictions Single Purpose • Usage of the data lake is often monopolize by data scientists • New data silo. No clear path to share insights with business users • Lacks the governance, security and quality that business users are used to (e.g. in the EDW)
- 8. 8 The Rise of Logical Architectures The Evolution of AnalyticalArchitectures Source: Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs Gartner April 2018
- 9. Multi‐purpose data lakes are data delivery environments developed to support a broad range of users, from traditional self‐service BI users (e.g. finance, marketing, human resource, transport) to sophisticated data scientists. Multi‐purpose data lakes allow a broader and deeper use of the data lake investment without minimizing the potential value for data science and without making it an inflexible environment. Rick Van der Lans, R20 Consultancy
- 10. 10 The Multipurpose Data Lake with Data Virtualization Logical Nature • Replication is an option, not a necessity • Broaden data access, shorten development times, better insights • Tight integration with big data systems. Fast execution with large data volumes Multi-purpose • Curated access for non-technical users • Better governance and access control • Better ROI for the investment of the lake
- 11. 11 The Multipurpose Data Lake with Data Virtualization “Amulti-purpose data lake can become an organization’s universal data delivery system” Architecting the Multi-Purpose Data Lake with Data Virtualization , Rick Van der Lans, April 2018
- 12. 12 Single access to all data assets, internal and external: ▪ Physical Data Lake (usually based on SQL-on-Hadoop systems) ▪ Other databases (EDW,ODS, applications, etc.) ▪ SaaS APIs (Salesforce, Google, social media, etc.) ▪ Files (local, S3, Azure, etc.) The Virtual Data Lake – Access to all Data Sources
- 13. 13 The physical Data Lake can also be used as Denodo’s cache This allows to quickly load any data accessible by Denodo to the Hadoop cluster Caching becomes an alternative to ingestion ELT processes that preserves lineage and governance Load process based on direct load to HDFS: 1. Creation of the target table in Cache system 2. Generation of Parquet files (in chunks) with Snappy compression in the local machine 3. Upload in parallel of Parquet files to HDFS The Virtual Data Lake – Ingesting and Caching
- 14. 14 Denodo optimizer provides native integration with MPP systems to provide one extra key capability: Query Acceleration Denodo can move, on demand, processing to the MPP during execution of a query • Parallel power for calculations in the virtual layer • Avoids slow processing in-disk when processing buffers don’t fit into Denodo’s memory (swapped data) The Virtual Data Lake – Using the Lake Processing Engine
- 15. 15 The Virtual Data Lake – Putting the Pieces Together 2Mrows (sales by customer) CurrentSales 68 M rows 1. Partial Aggregation push down Maximizes source processing dramatically Reducesnetwork traffic 3. On-demand data transfer Denodo automatically generates and upload Parquet files 4. Integration with local data The engine detects when data is cached or comes from a local table already in the MPP 2. Integrated with Cost Based Optimizer Based on data volume estimation and the cost of these particularoperations, the CBO can decide to move all orpart of the execution tree to theMPP 5. Fast parallel execution Support for Spark, Presto and Impala for fast analytical processing in inexpensive Hadoop-based solutions Hist.Sales 220 M rows Customer 2 M rows (Cached) join group by ZIP System Execution Time Optimization Techniques Others ~ 10 min Simple federation No MPP 43 sec Aggregation push-down With MPP 11 sec Aggregation push-down + MPP integration (Impala 8 nodes) group by Customer ID
- 16. 16 ▪ A Virtual Data Lake improves decision making and shortens development cycles • Surfaces all company data from multiple repositories without the need to replicate all data into the lake • Eliminates data silos: allows for on-demand combination of data from multiple sources ▪ A Virtual Data Lake broadens adoption of the lake and improves its ROI • Improves governance and metadata management to avoid “data swamps” • Allows controlled access to the lake to non-technical users ▪ A Virtual Data Lake offer performance for the Big Data World • Leverages the processing power of the existing cluster controlled by Denodo’s optimizer The Virtual Data Lake - Conclusions
- 17. 17 Challenges • Competition from a low cost vendor • Lower the price, affecting margins? • Or, maintain high price, but differentiate in other ways? Customer story – Large Heavy Equipment Manufacturer
- 18. 18 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
- 19. 19 Gartner, Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs, May 2018 When designed properly, DV can speed data integration, lower data latency, offer flexibility and reuse, and reduce data sprawl across dispersed data sources. Due to its many benefits, DV is often the first step for organizations evolving a traditional, repository- style data warehouse into a Logical Architecture”
- 20. Product Demonstration Director, APAC Sales Engineering, Denodo Chris Day
- 21. 21 Key Takeaways FIRST Takeaway Hadoop-based Data Lakes are the standard approach to modern analytics within most organizations SECOND Takeaway Physical Data Lakes introduce many complexities (replication, synchronization, governance, etc.) that restrict their use THIRD Takeaway Logical Data Lakes allow users to access data from all sources – internal and external – to grow value of Data Lake approach FOURTH Takeaway Data Virtualization creates ‘multipurpose’ Data Lakes for all kinds of users – data scientists and business users FIFTH Takeaway Data Virtualization introduces governance and access controls to the Data Lake without impeding the ‘power users' 21
- 22. Q&A
- 23. 23 Next Steps Access Denodo Platform in the Cloud! Take a Test Drive today! https://bit.ly/2AouQLQ GET STARTED TODAY
- 24. Next session Data Virtualization enabled Data Fabric: Operationalize the data lake Sushant Kumar Product Marketing Manager, Denodo Chris Day Director, APAC Sales Engineering, Denodo
- 25. 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.