igorstanmicrosoft com Phinean Woodwardunilever com Agenda Azure Synapse

igorstan@microsoft. com Phinean. Woodward@unilever. com

Agenda

Azure Synapse Analytics is Azure SQL Data Warehouse evolved, blending big data, data warehousing, and data integration into a single service for end-toend analytics at cloud scale

Azure Synapse Analytics limitless analytics service with unmatched time to insight Artificial Intelligence / Machine Learning / Internet of Things Intelligent Apps / Business Intelligence Synapse Analytics Studio Experience Platform MANAGEMENT SECURITY MONITORING METASTORE Azure Data Lake Storage Languages SQL Python . NET Java Scala R Form Factors PROVISIONED ON-DEMAND Analytics Runtimes DATA INTEGRATION Common Data Model Enterprise Security Optimized for Analytics

SQL Analytics new features available GA features: - Public preview features: Performance: Resultset caching Performance: Materialized Views Performance: Ordered columnstore Heterogeneous data: JSON support Trustworthy: Dynamic Data Masking Continuous integration & deployment: SSDT support Language: Read committed snapshot isolation - Workload management: Workload Isolation Data ingestion: Simple ingestion with COPY Data Sharing: Share DW data with Azure Data Share Trustworthy: Private LINK support Private preview features: - Data ingestion: Streaming ingestion & analytics in DW Built-in ML: Native Prediction/Scoring Data lake enabled: Fast query over Parquet files Language: Updateable distribution column Language: FROM clause with joins Language: Multi-column distribution support Note: private preview features require whitelisting

Control Node Result Best in class price performance Compute Node Interactive dashboarding with Resultset Caching - Millisecond responses with resultset caching Storage - Cache survives pause/resume/scale operations - Fully managed cache (1 TB in size) Alter Database <DBNAME> Set Result_Set_Caching ON

Best in class price performance Interactive dashboarding with Materialized Views - Automatic data refresh and maintenance - Automatic query rewrites to improve performance - Built-in advisor

CREATE WORKLOAD GROUP Sales WITH ( [ MIN_PERCENTAGE_RESOURCE = 60 ] [ CAP_PERCENTAGE_RESOURCE = 100 ] [ MAX_CONCURRENCY = 6 ] ) Workload aware query execution Intra Cluster Workload Isolation (Scale In) Sales Workload Isolation 60% - Multiple workloads share deployed resources - Reservation or shared resource configuration - Online changes to workload policies Marketing 100% 40% Compute 1000 c DWU

Heterogenous Data Preparation & Ingestion T-SQL Language SQL Analytics Event Hubs Streaming Ingestion Native SQL Streaming - High throughput ingestion (up to 200 MB/sec) - Delivery latencies in seconds - Ingestion throughput scales with compute scale - Analytics capabilities (SQL-based queries for joins, aggregations, filters) Io. T Hub Data Warehouse

T-SQL Language Heterogenous Data Preparation & Ingestion Event Hubs SQL Analytics Streaming Ingestion Data Warehouse COPY statement - Simplified permissions (no CONTROL required) Io. T Hub - No need for external tables - Standard CSV support (i. e. custom row terminators, escape delimiters, SQL dates) - User-driven file selection (wild card support) Azure Data Lake --Copy files in parallel directly into data warehouse table COPY INTO [dbo]. [weather. Table] FROM 'abfss: //<storageaccount>. blob. core. windows. net/<filepath>' WITH ( FILE_FORMAT = 'DELIMITEDTEXT’, SECRET = Credential. Object);

Create models Machine Learning enabled DW Upload models Score models SQL Analytics + = Data Model T-SQL Language Native PREDICT-ion - T-SQL based experience (interactive. /batch scoring) - Interoperability with other models built elsewhere Data Warehouse - Execute scoring where the data lives --T-SQL syntax for scoring data in SQL DW SELECT d. *, p. Score FROM PREDICT(MODEL = @onnx_model, DATA = dbo. mytable AS d) WITH (Score float) AS p; Predictions

SQL Analytics Data Lake Integration Parquet. Direct for interactive data lake exploration - >10 X performance improvement - Full columnar optimizations (optimizer, batch) - Built-in transparent caching (SSD, in-memory, resultset) 13 X

Azure Data Share Enterprise data sharing - Built-in flexibility • Share from DW to DW/DB/other systems • Choose data format to receive data in (CSV, Parquet) - One to many data sharing - Share a single or multiple datasets

Demo scenario Arcadia Event Hub data warehouse Data Lake Parquet files Taxi ride – pricing data

Unilever Story

WE MAKE MANY OF THE WORLD’S FAVOURITE BRANDS On any day, 2. 5 billion people use Unilever products to look good, feel good and get more out of life – giving us a unique opportunity to build a brighter future

Modern Data Warehouse Logical Representation • Each product will have its own resource group for cataloguing and cross-charging purposes Generally implemented using SQL DW, AAS, PBI, but flexibility based on requirements • Implemented using ADLS • Implemented using Azure Databricks • Implemented using ADLS • Business Data Lake (BDL) Product ADF Product Business Data Lake (BDL) ADF Product Universal Data Lake (UDL) Orchestration

Azure Synapse Analytics (formerly SQL DW)

Unilever and Azure Synapse Analytics (Preview) In preview for ~3 months Brings together data integration, data warehousing and big data processing capabilities at scale Accelerates the delivery of BI, AI and Intelligent Applications Build Analytics Solution from ingestion to BI reporting in one place in a single workspace Easy Management Integrated Dev Ops environment Easy deployment Secure Environment Centralized Monitoring & Alerting Additional features

Q/A

Backup

Want to learn more about Azure Synapse Analytics? Check out these sessions for more information BRK 2187 NEW! Introducing Azure Synapse Analytics: the Next Evolution of SQL Data Warehouse for Every Data Professional Monday, 3: 15 PM BRK 3044 Migrating Your Mission-Critical Data Warehouse to Azure Synapse Analytics Monday, 4: 30 PM BRK 3330 Unifying AI-to-BI with Azure Synapse Analytics Tuesday, 9: 15 AM BRK 3224 Modernizing your Data Warehouse with Data Ingestion, Preparation, and Serving using Azure Synapse Analytics Tuesday, 10: 30 AM BRK 3229 Securing Your Data Warehouse with Azure Synapse Analytics Tuesday, 11: 45 AM BRK 3050 Democratizing the Data Lake with On-Demand Capabilities in Azure Synapse Analytics BRK 3051 Cloud Data Warehousing with Azure Synapse Analytics Tuesday, 3: 30 PM Wednesday, 10: 30 AM

Want to learn more about analytics on Azure? Check out these sessions for more information BRK 3045 Code-free ETL using Azure Data Factory & Data Share Wednesday, 2: 15 PM BRK 3094 Modern Data Integration Scenarios & B 2 B data sharing using Azure Data Share Wednesday, 3: 30 PM BRK 3046 Achieving Petabyte-Scale Data Ingestion with Azure Data Factory BRK 3043 Maximizing your Azure Databricks Deployment BRK 3042 Gaining Business Insights with Open Source Analytics on Azure HDInsight: Patterns and Best Practices BRK 3047 Prepare for the Next Era of Insights Using Azure Data Lake Storage BRK 2066 Enabling Real-Time Analytics Patterns from the Cloud to the Intelligent Edge with Azure Stream Analytics BRK 3048 Build High Performance Time Series and Log Data Analysis Solutions with Azure Data Explorer Thursday, 9: 15 AM Thursday, 10: 30 AM Friday, 9: 15 AM Friday, 11: 45 AM Tuesday, 2: 15 PM Friday, 10: 30 AM

Want to get hands-on? Check out these labs to get hands-on with the latest in Azure Analytics. WRK 4002 Data Integration using Azure Data Factory and Azure Data Share WRK 4000 Build Solutions Powered by Real-time Analytics using Azure Stream Analytics and Azure Data Explorer WRK 4001 Building an End-to-end Analytics Pipeline with Azure Synapse Analytics Tuesday, 4: 00 PM Wednesday, 12: 30 PM Thursday, 4: 00 PM Stopping by our booth? Check out these theater sessions in the Hub for even more information THR 3110 Big Data Processing with Spark and. NET THR 3113 Maximizing ROI in SQL Data Warehouse with Enhanced Workload Management THR 3116 Streaming Data with Azure Event. Hubs and Kafka THR 3128 Time Series and Machine Learning with Azure Data Explorer THR 3133 Staying Productive with Azure Data's Developer Tooling THR 3119 What's New with Azure Data Lake Storage? Tuesday, 12: 40 PM Wednesday, 3: 05 PM Thursday, 1: 15 PM Thursday, 10: 20 AM Thursday, 2: 30 PM

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CREATE WORKLOAD CLASSIFIER classifier_name WITH ( WORKLOAD_GROUP = 'name’ , MEMBERNAME = 'security_account' [ [ , ] IMPORTANCE = { LOW | BELOW_NORMAL | NORMAL (default) | ABOVE_NORMAL | HIGH }]) Scheduler With Importance Turned On Workload aware query execution CEO 1 2 3 12 CEO 4 5 Workload Importance Running 6 7 8 9 10 11 12 Normal Low Normal High Queued