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Similar to Sql Bits 2020 - Designing Performant and Scalable Data Lakes using Azure Data Lake Storage (18)
Sql Bits 2020 - Designing Performant and Scalable Data Lakes using Azure Data Lake Storage
- 1. Designing performant and scalable data lakes using Azure Data Lake Storage Rukmani Gopalan @RukmaniGopalan
- 2. Agenda • Data Lake Concepts and Patterns • Designing your data lake • Set up • Organize data • Secure data • Manage cost • Optimizing your data lake • Achieve the best performance and scale
- 3. Traditional on-prem analytics pipeline Operational database Business/custom apps Operational database Operational database Enterprise data warehouse Data mart Data mart Data mart ETL ETL ETL ETL ETL ETL ETL Reporting Analytics Data mining
- 4. Modern data warehouse Logs (structured) Media (unstructured) Files (unstructured) Business/custom apps (structured) Ingest Prep & train Model & serve Store Azure Data Lake Storage Azure DatabricksAzure Data Factory Power BI Azure Synapse Analytics Azure Synapse Analytics
- 5. Advanced Analytics Logs (structured) Media (unstructured) Files (unstructured) Business/custom apps (structured) Ingest Prep & train Model & serve Store Azure Data Lake Storage Azure Data Factory Power BI Apps Azure Databricks Azure Synapse Analytics Azure Synapse Analytics Cosmos DB
- 6. Realtime Analytics Logs (structured) Media (unstructured) Files (unstructured) Business/custom apps (structured) Ingest Prep & train Model & serve Store Azure Data Lake Storage Azure DatabricksAzure Data Factory Power BI Apps Message Broker Azure Synapse Analytics Azure Synapse Analytics Cosmos DB Sensors and IoT (unstructured)
- 7. A “no-compromises” Data Lake: secure, performant, massively-scalable Data Lake storage that brings the cost and scale profile of object storage together with the performance and analytics feature set of data lake storage A z u r e D a t a L a k e S t o r a g e M A N A G E A B L ES C A L A B L E F A S T S E C U R E No limits on data store size Global footprint (50 regions) Optimized for Spark and Hadoop Analytic Engines Tightly integrated with Azure end to end analytics solutions Automated Lifecycle Policy Management Object Level tiering Support for fine- grained ACLs, protecting data at the file and folder level Multi-layered protection via at-rest Storage Service encryption and Azure Active Directory integration C O S T E F F E C T I V E I N T E G R AT I O N R E A D Y Atomic directory operations means jobs complete faster Object store pricing levels File system operations minimize transactions required for job completion
- 8. Azure Data Lake Storage Cloud Storage platform with first class file/folder semantics and support for multiple protocols and cost/performance tiers. Built on Object Storage. Common Blob Storage Foundation Blob API ADLS API Server Backups, Archive Storage, Semi-structured Data Object Data Hadoop File System, File and Folder Hierarchy, Granular ACLS Atomic File Transactions Analytics Data Object Tiering and Lifecycle Policy Management AAD Integration, RBAC, Storage Account Security HA/DR support through ZRS and RA-GRS NFS v3 (preview) HPC Data, Applications using NFS v3 against large sequentially read data sets File Data
- 9. Data Lake Architecture - Summary Store large volume of multi-structured data in its native format Defer work to ‘schematize’ after value & requirements are known (Schema-on-read) Extract high value insights from the multi-structured data Build intelligent business scenarios based on the insights
- 10. Designing Your Data Lake • How do I set up my data lake? • How do I organize my data? • How do I secure my data? • How do I manage cost?
- 11. How do I set up my data lake? • Centralized vs Federated implementation • Data management and administration – done by a central team vs business units/domains • Blueprint approach to federated data lakes with centralized governance Flexible – single or multiple storage accounts Blueprint
- 12. Recommendations Isolate development vs pre-production and production data lakes Identify logical datasets, resources and management needs – this drives the centralized vs federated approach • Business unit boundaries • Regional boundaries Promote sharing data/insights across business units – beware of data silos
- 13. How do I organize my data? • Azure Data Lake Storage hierarchy • Storage account Azure resource that contains data objects • Container Organize within storage account - contains a set of files/folders • Folder/directory Organize within container - contains a set of files/folders, Hadoop file system friendly • File Holds data that can be read or written
- 14. Recommendations Organize data based on semantic structure as well as desired access control Separate the different zones into different accounts, containers or folders depending on business need
- 15. How do I secure my data? PERIMETER/NETWORK Service Endpoints Private Endpoints AUTHENTICATION Azure Active Directory (recommended) Shared Keys SAS tokens Shared Key AUTHORIZATION RBACs (coarse grained) POSIX ACLs (fine grained) Shared Key DATA PROTECTION Encryption on-the-wire with HTTPS Encryption at Rest • Service and Customer Managed Keys Diagnostic Logs
- 16. A Little More on Authorization RBACs and ACLs integrated with AAD • RBACs – Storage account and container • ACLs – File and folders Other access mechanisms (not recommended) • Shared Keys – Disable if not needed (preview) • SAS Tokens – short lived access
- 17. Recommendations Service or Private endpoints for network security Use Azure Active Directory authentication to manage access Use RBACs for coarse grained access (at storage account or container level) and ACLs for fine grained access control (at file or folder level) AAD groups largely simplify your access management issues
- 18. How do I manage cost? • Choose the right set of features for your business – cost vs benefit • E.g. Redundancy option – criticality of geo-redundancy for production vs dev environments LRS ZRS GZRS(RA-)GRS Single Region Dual RegionGRS
- 19. How do I manage cost? (Continued…) • Control data growth – minimize risk of data swamp • Workspace data management • Leverage lifecycle management policies • Tiering • Retention
- 20. Recommendations Choose the features of data lake storage based on business need Pre-prod and development environment needs might vary from production environment needs Leverage lifecycle management policies for better data management Move data to a cooler tier if not actively used – be aware of higher transaction costs and minimum retention policies Use retention policies to delete data that is not needed
- 21. How do I optimize my data lake? Goal • Optimize for performance AND scale as the data and applications continue to grow on the data lake The basic considerations are… • Optimize for high throughput • Target getting at least a few MBs (higher the better) per transaction. • Optimize data access patterns • Reduce unnecessary scanning of files • Read only the data you need to read. • Write efficiently so downstream applications that read the data benefit
- 22. File size and format • Too many small files adversely impact performance • Choosing the right format – better performance AND lower cost • Parquet – integrated optimizations with Azure Synapse Analytics and Azure Databricks • Recommendations Modify source to ingest larger files into the data lake Coalesce and convert to right format (E.g. Parquet) in curation phase of your analytics pipelines Realtime analytics pipelines (E.g. sensor data in IoT application) – microbatch for larger writes
- 23. Partition your data for optimized access Partition based on consumption patterns for optimized performance Sensor ID Year Temperature Humidity Pressure
- 24. Microsoft Confidential Query Acceleration (Preview) Optimize access to structured data by filtering data directly in the storage service Single file predicate evaluation and column projection to optimize analytics engines Eg: SELECT _1, _3 FROM BlobStorage WHERE _14 < 250 AND _16 > '2019-07-01'
- 25. Guidance from experts Microsoft Docs Explore overviews, tutorials, code samples, and more. Azure Data Lake Storage: https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-introduction Azure Data Lake Storage Guidance Document: https://aka.ms/adls/guidancedoc Azure Synapse Analytics: https://docs.microsoft.com/azure/synapse-analytics
- 26. © Copyright Microsoft Corporation. All rights reserved.
Editor's Notes
- #16: An Azure Virtual Network (VNet) is a representation of your own network in the cloud. It is a logical isolation of the Azure cloud dedicated to your subscription. ... When you create a VNet, your services and VMs within your VNet can communicate directly and securely with each other in the cloud.
- #23: Symptom: Job latencies Investigation Storage request throttling Root cause Too many read operations to storage. Large number of row groups in databrick delta parquet file resulted in lots of reads operations. Solution Adjusted parquet.block.size config value to reduce number of row groups per parquet file Job runtimes reduced by 3x
- #24: Symptom: Job timeouts Investigation Transaction and throughput peaks, bursty pattern of load Storage request throttling Root cause Data cleanup during SLA job execution Large number of partitions 10’s of thousands of partitions Solution Reduced number of partitions to 250 Best practice: Partitioning strategy must align with your query pattern Reduced the number of delete operations while SLA job is running