Download as PDF, PPTX
Uploaded byAlluxio, Inc.
Enabling big data & AI workloads on the object store at DBS
DBS Bank is headquartered in Singapore and has evolved its data platforms over three generations from proprietary systems to a hybrid cloud-native platform using open source technologies. It is using Alluxio to unify access to data stored in its on-premises object store and HDFS as well as enable analytics workloads to burst into AWS. Alluxio provides data caching to speed up analytics jobs, intelligent data tiering for efficiency, and policy-driven data migration to the cloud over time. DBS is exploring further using cloud services for speech processing and moving more workloads to the cloud while keeping data on-premises for compliance.
More Related Content
Similar to Enabling big data & AI workloads on the object store at DBS
Enabling big data & AI workloads on the object store at DBS
- 1. Enabling big data& AI workloads on the object store at DBS Vitaliy Baklikov (DBS Bank) Dipti Borkar (Alluxio) Strata Data New York 2019
- 2. About DBS • Headquarteredin Singapore • Largest bank in South East Asia • Present in 18 markets globally, including 6 priority markets • Singapore, Hong Kong, China, India, Indonesia and Taiwan • We have a very cool digiBank app • And lots lots lots of data systems
- 3. DBS – Top20 Business Transformations of the Last Decade
- 4. AWS EnginesOnprem Engines HDFS ObjectStore Evolution of Data Platforms at DBS Generation 1 • Boxed data • Monolithic/Closed Systems • Proprietary HW/SW • Data for Targeted Use Cases Generation 2 • Big Data Explosion • Hadoop Data Lakes • Commodity HW and Hadoop Ecosystem • Compute tied to Storage Generation 3 • Data Democratization • Cloud Native platform… Hybrid! Multi! • Open Source Engines • Burst compute in the cloud with data on-prem for compliance • AI/ML Centric Teradata Informatica SAS HadoopTeradata Informatica SAS Teradata Informatica SAS Hadoop
- 5. Challenges 1. Data Lakebuilt on local Object Store – Expensive rename operation – Object listing is slow – Variable performance – Data locality is gone 2. Multiple Data Silos 3. Limited on-premise compute capacity – Legacy ITIL processes for Infra provisioning – No dynamic scale out/in
- 6. Alluxio at DBS MountHDFS from other platforms into common Alluxio cluster Unified Namespace Object store Analytics Hybrid cloud bursting Caching layer for hot data to speed up Presto and Spark jobs Extend Alluxio cluster into AWS VPC Run EMR for model training and bring the results back to on-prem
- 7. Advanced Analytics onObject Storage The Use Case • Cash-in-Transit use case • Forecast cash replenishment schedule for each ATM • Produce a delivery graph by 4am each morning The Challenges • Strict fixed SLA • Need to load all history data for the forecasting model… daily!
- 8. Alluxio at DBS MountHDFS from other platforms into common Alluxio cluster Unified Namespace Object store Analytics Hybrid cloud bursting Caching layer for hot data to speed up Presto and Spark jobs Extend Alluxio cluster into AWS VPC Run EMR for model training and bring the results back to on-prem
- 9. Burst processing intothe Cloud The Use Case – Call Center project – Millions of calls annually – Why do our customers call us? – What do they do before picking up the phone? • Reconstruct customer journey • Predict the reason for the call The Challenges – Transcript quality – Need lots of compute • >30TB of clickstream, transaction, customer, and product data • >20TB of audio files – Need dynamic compute for training and analysis – Data needs to reside on-prem for compliance
- 10. High Level Architecture DataOrchestration Data Orchestration
- 11. Next Steps 1. UseCloud offerings for Speech-to-Text 2. Move more workloads to the cloud 3. Extend to other clouds
- 12. Data Orchestration forthe Cloud
- 13. Data Orchestration forthe Cloud Java File API HDFS Interface S3 Interface REST APIPOSIX Interface HDFS Driver Swift Driver S3 Driver NFS Driver Enable innovation with any frameworks running on data stored anywhere Data Analyst Data Engineer Storage Ops Data Scientist Lines of Business
- 14. Data Orchestration forthe Cloud Java File API HDFS Interface S3 Interface REST APIPOSIX Interface HDFS Driver Swift Driver S3 Driver NFS Driver Enable innovation with any frameworks running on data stored anywhere
- 15. Problem: HDFS clusteris compute- bound & complex to maintain AWS Public Cloud IaaS Spark Presto Hive TensorFlow Alluxio Data Orchestration and Control Service On Premises Connectivity Datacenter Spark Presto Hive Tensor Flow Alluxio Data Orchestration and Control Service Barrier 1: Prohibitive network latency and bandwidth limits • Makes hybrid analytics unfeasible Barrier 2: Copying data to cloud • Difficult to maintain copies • Data security and governance • Costs of another silo Step 1: Hybrid Cloud for Burst Compute Capacity • Orchestrates compute access to on-prem data • Working set of data, not FULL set of data • Local performance • Scales elastically • On-Prem Cluster Offload (both Compute & I/O) Step 2: Online Migration of Data Per Policy • Flexible timing to migrate, with less dependencies • Instead of hard switch over, migrate at own pace • Moves the data per policy – e.g. last 7 days “Zero-copy” bursting to the cloud
- 16. Using Alluxio withAWS EMR Presto Hive Instances Metadata & Data cache Presto Hive Metadata & Data cache HDFS HDFSEMRF S EMRF S Compute-driven Continuous sync Compute-driven Continuous sync
- 17. Spark Presto HiveTensorFlow RAM Framework Read file /trades/us Bucket Trades Bucket Customers Data requests Feature Highlight: Data Caching for faster compute Read file /trades/us again Read file /trades/top Read file /trades/top Variable latency with throttling Read file /trades/us again Read file /trades/top Read file /trades/top Read file /trades/us again Read file /trades/top Read file /trades/top Read file /trades/us again Read file /trades/top Read file /trades/top Read file /trades/us again
- 18. Spark Presto HiveTensorFlow RAM SSD Disk Framework Bucket Trades Bucket Customers Data requests Feature Highlight - Intelligent Tiering for resource efficiency Read file /customers/145 Out of memory Variable latency with throttling Data moved to another tier
- 19. Spark Presto HiveTensorFlow RAM SSD Disk Framework New Trades Policy Defined Move data > 90 days old to Feature Highlight – Policy-driven Data Management S3 Standard Policy interval : Every day Policy applied everyday HDFS
- 20. Alluxio MasterZookeeper / RAFT Standby Master WAN Alluxio Client Alluxio Client Alluxio Worker RAM /SSD / HDD Alluxio Worker RAM / SSD / HDD Alluxio Reference Architecture … … Application Application Under Store 1 Under Store 2
- 21.