In Memory Parallel Processing for Big Data Scenarios
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Watch the full webinar on demand here: https://goo.gl/5VyGns Denodo Platform offers one of the most sought after data fabric capabilities through data discovery, preparation, curation and integration across the broadest range of data sources. As data volume and variety grows exponentially, Denodo Platform 7.0 will offer in-memory massive parallel processing (MPP) capability for the most advanced query optimization in the market. Attend this session to learn: • How Denodo Platform 7.0’s native built-in integration with MPP systems will provide query acceleration and MPP caching • How to successfully approach highly complex big data scenarios, leveraging inexpensive MPP solutions • With the MPP capability in place, how data driven insights can be generated in real-time with Denodo Platform Agenda: • Challenges with traditional architectures • Denodo Platform MPP capabilities and applications • Product demonstration • Q&A
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In Memory Parallel Processing for Big Data Scenarios
- 1. DATA VIRTUALIZATION PACKED LUNCH WEBINAR SERIES Sessions Covering Key Data Integration Challenges Solved with Data Virtualization
- 2. In Memory Parallel Processing for Big Data Scenarios Pablo Alvarez-Yanez Principal Solution Architect, Denodo Paul Moxon VP Data Architectures & Chief Evangelist, Denodo
- 3. Agenda 1. Modern Data Architecture 2. Denodo Platform – Big Data Integrations 3. Big Data Performance 4. Putting This All Together 5. Q&A 6. Next Steps
- 4. The Modern Data Architecture 4
- 5. Data Integration – A Modern Data Ecosystem 5
- 6. Organizations are Storing More and More Data… 6
- 7. Data Lake – The Challenges 7
- 8. 8 Big Data & Analytics Reference Architecture
- 9. Denodo Platform and Big Data Integrations 9
- 10. Hadoop as a Data Source 10 Denodo offers native connectors for all the major SQL-on-Hadoop engines: Hive Impala SparkSQL Presto In addition, Denodo also offers connectivity for HBase and direct HDFS access to different file formats
- 11. Hadoop as a Cache 11 Denodo uses an external RDBMS of your choice to persist copies of the result sets to improve execution times • Since data is persisted in an RDBMS, Denodo can push down relational operations, like JOINS with other tables, to the database used for cache SQL-on-Hadoop systems can also be used as Denodo’s cache Cache 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
- 12. Hadoop as a Processing Engine 12 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)
- 13. Demo 13
- 14. 14 Combining Denodo’s Optimizer with a Hadoop MPP Denodo provides the most advanced optimizer in the market, with techniques focused on data virtualization scenarios with large data volumes In addition to traditional Cost Based Optimizations (CBO), Denodo’s optimizer applies innovative optimization strategies, designed specifically for virtualized scenarios, beyond traditional RDBMS optimizatios. Combined with the tight integration with SQL-on-Hadoop MPP databases, it creates a very powerful combo
- 15. 15 Example Scenario Trend of sales by zip code over the previous years. Scenario: • Current data (last 12 months) in EDW • Historical data offloaded to Hadoop cluster for cheaper storage • Customer master data is used often, so it is cached in the Hadoop cluster Very large data volumes: • Sales tables have hundreds of millions of rows join group by ZIP union Current Sales 100 million rows Historical Sales 300 million rows Customer 2 million rows (cached)
- 16. 16 Example: What are the options? 1. Option A: Simple Federation in Virtual Layer • Move hundreds of millions of rows for processing in the virtual layer 2. Option B: Data Shipping • Move “Current sales” to Hadoop and process content in the cluster • Moves 100 million rows 3. Option C: Partial Aggregation Pushdown (Denodo 6) • Modifies the execution tree to split the aggregation in two steps: 1. First by Customer ID for the JOIN (pushed down to source) 2. Second by zip code for the final results (in virtual layer) • Reduces significantly network traffic but processing of large amount of data in the virtual layer (aggregation by zip code) becomes the bottleneck 4. Denodo’s MPP Integration (Denodo 7) Simple Federation Shipping join group by ID group by ZIP group by ZIP join
- 17. 17 Example: Denodo’s Integration with the Hadoop Ecosystem 2M rows (sales by customer) 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) Current Sales 100 M rows group by customer ID1. Partial Aggregation push down Maximizes source processing dramatically Reduces network traffic 3. On-demand data transfer Denodo automatically generates and upload Parquet files 4. Integration with local and pre-cached 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 particular operations, the 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 in inexpensive Hadoop-based solutions Hist. Sales 300 M rows Customer 2 M rows (cached) join group by ZIP
- 18. Demo 18
- 19. Putting It All Together 19
- 20. Putting all the pieces together 20 These three techniques (Hadoop as a data source, cache and processing engine) can be combined to successfully approach complex scenarios with big data volumes in an efficient way: Surfaces all the company data without the need to replicate all data to the Hadoop lake Improves governance and metadata management to avoid “data swamps” Allows for on-demand combination of real-time (from the original sources) with historical data (in the cluster) Leverages the processing power of the existing cluster controlled by Denodo’s optimizer
- 21. Architecture 21 To benefit from this architecture, Denodo servers should run in edge nodes of the Hadoop cluster This will ensure: Faster uploads to HDFS Faster data retrieval from the MPP Better compatibility with the Hadoop configuration and versions of the libraries Denodo Cluster Multiple nodes behind a load balancer for HA Running on Hadoop Edge nodes Hadoop Cluster Processing and Storage nodes Same subnet as Denodo cluster
- 22. Q&A
- 23. Next steps Download Denodo Express: www.denodoexpress.com Access Denodo Platform in the Cloud! 30 day FREE trial available! Denodo for Azure: www.denodo.com/TrialAzure/PackedLunch Denodo for AWS: www.denodo.com/TrialAWS/PackedLunch
- 24. Next session Self-Service Information Consumption Using Data Catalog Thursday, March 15th, 2018 at 11:00am (PST) Paul Moxon VP Data Architectures and Chief Evangelist
- 25. Thank you! © 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.