Analyzing StackExchange data with Azure Data Lake
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The document outlines the significance of big data and how it can be analyzed using Azure Data Lake, which offers scalable and efficient data storage and analytics services. Key features include managed clusters, U-SQL for querying, and integration with various Azure services, making it suitable for processing vast datasets like those from Stack Exchange. It also compares Azure Data Lake with traditional data warehousing, emphasizing the flexibility and potential pitfalls of data lakes.
Analyzing StackExchange data with Azure Data Lake
- 1. Sponsored & Brought to you by Analyzing StackExchange data with Azure Data Lake Tom Kerkhove http://www.twitter.com/TomKerkhove https://be.linkedin.com/in/tomkerkhove
- 2. Analysing StackExchange data with Azure Data Lake Analysing StackExchange data with Azure Data Lake
- 3. Nice to meet you Tom KERKHOVE ➔ Integration Professional ➔ IoT Competency Lead ➔ Windows Development & Microsoft Azure MVP [email protected] +32 473 701 074 @TomKerkhove be.linkedin.com/in/tomkerkhove github.com/tomkerkhove
- 4. Agenda • Why should we care about Big Data? • Big Data in Azure • Azure Data Lake • Demo • Q & A 4
- 6. Integration of ThingsInternet of Things 6
- 7. Connect and scale with efficiency Analyze and act on new data Integrate and transform business processes
- 8. Event producers & gateways Ingestion & transformation Report, Act, Predict
- 9. Microsoft Patterns & Practices – IoT Journey
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- 13. Platform Services Infrastructure Services Web Apps Mobile Apps API Management API Apps Logic Apps Notification Hubs Content Delivery Network (CDN) Media Services BizTalk Services Hybrid Connections Service Bus Storage Queues Hybrid Operations Backup StorSimple Azure Site Recovery Import/Export SQL Database DocumentDB Redis Cache Azure Search Storage Tables Data Warehouse Azure AD Health Monitoring AD Privileged Identity Management Operational Analytics Cloud Services Batch RemoteApp Service Fabric Visual Studio App Insights Azure SDK VS Online Domain Services HDInsight Machine Learning Stream Analytics Data Factory Event Hubs Mobile Engagement Data Lake IoT Hub Data Catalog Security & Management Azure Active Directory Multi-Factor Authentication Automation Portal Key Vault Store/ Marketplace VM Image Gallery & VM Depot Azure AD B2C Scheduler
- 14. Overview in Azure 14 DocumentDB Data Factory Stream Analytics Data Lake HDInsight Data Lake (Store & Analytics) Virtual Machine IoT Hub SQL Data Warehouse SQL DatabaseStorageEvent Hubs Document Db Data Ingestion Data Storage Data Pipelines Machine Learning Data Analytics
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- 17. Analysing Big Data in Azure Azure Data Lake Family HDInsight Data Lake Store Data Lake Analytics • Unlimited storage • WebHDFS Store • Managed cluster service • Open-source technology • Runs on Windows or Linux • Managed job service • U-SQL batch-processing
- 18. Azure Data Lake Store ➔ WebHDFS compatible ➔ Any size ➔ Any format as-is ➔ Write-once-read-many ➔ Enterprise-grade security ➔ Thé big data store in Azure 18
- 19. Characteristics ➔ Data Warehousing ➔ Structured data ➔ Defined set of schemas ➔ Requires Extract-Transform- Load (ETL) before storing ➔ Known for some of us ➔ Exploratory analysis is hard because of transforming the data 19 Data Lake vs DataWarehousing ➔ Data Lake ➔ Raw data (unstructured/semi-structured/structured) ➔ “Dump” all your data in the lake ➔ Data scientists will interpret data from the lake ➔ Without metadata, turns in a data swamp pretty fast
- 20. 20Martin Fowler on Data Lake & Data Warehouses(link)
- 21. Azure Data Lake Analytics ➔ Run analytics jobs on managed clusters ➔ Don’t worry about scale ➔ Written in U-SQL ➔ SQL Syntax ➔ Extensibility in C# ➔ Easily scaled with Analytics Units ➔ Pay for processing time only 21
- 22. Writing U-SQL scripts 22 Extract from data source by using built-in or custom extractors. Transform / Analyse the data using SQL-syntax, in-line C# or C# method calls Output the result to a data source by using built-in or custom extractors
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- 24. Data Lake Analytics - Data Sources U-SQL Query Query Azure Storage Blobs Azure Data Lake Store Azure SQL Database Azure SQL Data Warehouse Azure SQL in VMs Azure Data Lake Analytics
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- 26. Meet StackExchange ➔ Over 280 subwebsites ➔ 150+ GB of open-source data ➔ Different kinds of data ➔ Posts ➔ Users ➔ Votes ➔ ... ➔ A big data sample data set
- 27. What AreWe GoingTo Do? • Downloading the original data set Acquiring The Data • Upload data set to Azure • Determine what service to use Moving The Data • Merging data from each site into one file • Conversion from XML to CSV Aggregating The Data • Run business logic on it • Attempt to gain knowledge from it Analyzing The Data • Visualize what we’ve learned Visualizing The Data 27
- 28. Azure Data Lake tools forVisual Studio ➔ Projects / Solutions / Source control ➔ Store Explorer ➔ Browse store ➔ Download complete / subset of file ➔ Preview ➔ JobVisualizer ➔ Determine bottlenecks by using heatmaps ➔ Playback jobs based on telemetry ➔ Query optimization ➔ Job Profiler ➔ Off-Line execution 28
- 29. Integration with Azure Services ➔ Integrate in your data pipelines in Azure Data Factory ➔ Move data from Azure Data Lake Store to other store ➔ Move data to Azure Data Lake Store ➔ Run U-SQL query within pipeline ➔ Integration with Azure Data Catalog ➔ Register your Azure Data Lake Store assets 29
- 30. Pricing ➔ Data Lake Store ➔ $0,08/GB stored per month ➔ $0,14 per 1M transactions • 1 transaction is block of up to 128 kB ➔ Egress will be billed but not know yet ➔ Data Lake Analytics ➔ $0,05 per job ➔ $0,05 per minute per Analytics Unit for processing time 30
- 31. Azure Data Lake Store vs Blob Storage 31 No Limitations Store whatever you want in any format Security Built-in Azure Active Directory support Pricing More expensive than Storage RA-GRS Redundancy It’s there but no control over it Built for Scale Optimized for high- scale reads Integration With Data Factory, Data Catalog & HDInsight
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- 33. Summary ➔ Big Data is not just a hype so get ready ➔ Azure Data Lake Store ➔ Analyse today & explore tomorrow ➔ Data Swamps ➔ Data Lake Analytics ➔ No cluster management ➔ Re-use existing skills ➔ Pay for what we use ➔ Big Data in Azure? Azure Data Lake family and it’s easy!
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