Big Data Adavnced Analytics on Microsoft Azure
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This presentation provides a survey of the advanced analytics strengths of Microsoft Azure from an enterprise perspective (with these organizations being the bulk of big data users) based on the Team Data Science Process. The talk also covers the range of analytics and advanced analytics solutions available for developers using data science and artificial intelligence from Microsoft Azure.
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Big Data Adavnced Analytics on Microsoft Azure
- 8. The opportunity and challenge of data science in enterprises Opportunity: 17% had a well-developed Predictive/Prescriptive Analytics program in place, while 80% planned on implementing such a program within five years – Dataversity 2015 Survey Challenge: Only 27% of the big data projects are regarded as successful – CapGenimi 2014 Tools & data platforms have matured - Still a major gap in executing on the potential
- 9. One reason: Process challenge in Data Science Organization Collaboration Quality Knowledge Accumulation Agility Global Teams • Geographic Locations Team Growth • Onboard New Members Rapidly Varied Use Cases • Industries and Use Cases Diverse DS Backgrounds • DS have diverse backgrounds, experiences with tools, languages
- 10. Data Science lifecycle Primary stages: Lifecycle
- 13. Time Science One Envisioning Scoping Science Two Science Three Science Four
- 19. Form 10-K 2016
- 20. Microsoft | Amplifying Human Ingenuity Enhance traditional line-of- business analytics solutions with Machine Learning Solve complex business problems with Deep Learning Engage customers with intelligent automated solutions
- 23. Azure AI Services Azure Infrastructure Tools
- 24. © Microsoft Corporation Fast, easy, and collaborative Apache Spark-based analytics platform Azure Databricks for deep learning modeling Tools InfrastructureFrameworks Leverage powerful GPU-enabled VMs pre-configured for deep neural network training Use HorovodEstimator via a native runtime to enable build deep learning models with a few lines of code Full Python and Scala support for transfer learning on images Automatically store metadata in Azure Database with geo-replication for fault tolerance Use built-in hyperparameter tuning via Spark MLLib to quickly drive model progress Simultaneously collaborate within notebooks environments to streamline model development Load images natively in Spark DataFrames to automatically decode them for manipulation at scale Improve performance 10x-100x over traditional Spark deployments with an optimized environment Seamlessly use TensorFlow, Microsoft Cognitive Toolkit, Caffe2, Keras, and more
- 25. © Microsoft Corporation Additional deep learning tools Automatically scale virtual machine clusters with GPUs or CPUs Develop your models with long-running batch jobs, iterative experimentation, and interactive training Support for any deep learning or machine learning framework Designed and pre-configured specifically for GPU- enabled instances Fully integrated with Azure AI training service to provide capacity for parallelized AI training at scale Get started in seconds with example scripts and sample data sets Batch AI Deep Learning Virtual Machine
- 26. © Microsoft Corporation Deploy AI models to devices on the edge IoT EdgeModel managementMachine learning Azure Databricks Azure ML Services Qualcomm QCS603 Vision AI dev. kit Pre trained Solutions IDEs AI Frameworks
- 27. © Microsoft Corporation Native TensorFlow Caffe Deploy AI models to devices on the edge flow Message passing Co-located Scoring file Service or app Trained/retrained model Additional images Original NW e.g. MobileNet
- 28. © Microsoft Corporation Machine learning and deep learning, when to use what? Code first (On-prem) ML Server On-prem Hadoop SQL Server (cloud) AML (Preview) SQL Server Hadoop Azure Batch DSVM Spark Visual tooling (cloud) AML Studio Notebooks Jobs Azure Databricks Spark What engine(s) do you want to use? Deployment target Which experience do you want? Build with Spark or other engines? Python TensorFlow, Keras, MS Cognitive Toolkit, ONNX, Caffe2 Spark ML, SparkR, SparklyR TensorFlow, Keras, MS Cognitive Toolkit, ONNX, Caffe2
- 34. S P A R K : A B R I E F H I S T O R Y
- 35. A P A C H E S P A R K An unified, open source, parallel, data processing framework for Big Data Analytics Spark Core Engine Spark SQL Interactive Queries Spark Structured Streaming Stream processing Spark MLlib Machine Learning Yarn Mesos Standalone Scheduler Spark MLlib Machine Learning Spark Streaming Stream processing GraphX Graph Computation
- 36. Azure Databricks Databricks Spark as a managed service on Azure
- 37. CONTROL EASE OF USE Azure Data Lake Analytics Azure Data Lake Store Azure Storage Any Hadoop technology, any distribution Workload optimized, managed clusters Data Engineering in a Job-as-a-service model Azure Marketplace HDP | CDH | MapR Azure Data Lake Analytics IaaS Clusters Managed Clusters Big Data as-a-service Azure HDInsight Frictionless & Optimized Spark clusters Azure Databricks BIGDATA STORAGE BIGDATA ANALYTICS ReducedAdministration K N O W I N G T H E V A R I O U S B I G D A T A S O L U T I O N S
- 38. Azure HDInsight What It Is • Hortonworks distribution as a first party service on Azure • Big Data engines support – Hadoop Projects, Hive on Tez, Hive LLAP, Spark, HBase, Storm, Kafka, R Server • Best-in-class developer tooling and Monitoring capabilities • Enterprise Features • VNET support (join existing VNETs) • Ranger support (Kerberos based Security) • Log Analytics via OMS • Orchestration via Azure Data Factory • Available in most Azure Regions (27) including Gov Cloud and Federal Clouds Guidance • Customer needs Hadoop technologies other than, or in addition to Spark • Customer prefers Hortonworks Spark distribution to stay closer to OSS codebase and/or ‘Lift and Shift’ from on-premises deployments • Customer has specific project requirements that are only available on HDInsight Azure Databricks What It Is • Databricks’ Spark service as a first party service on Azure • Single engine for Batch, Streaming, ML and Graph • Best-in-class notebooks experience for optimal productivity and collaboration • Enterprise Features • Native Integration with Azure for Security via AAD (OAuth) • Optimized engine for better performance and scalability • RBAC for Notebooks and APIs • Auto-scaling and cluster termination capabilities • Native integration with SQL DW and other Azure services • Serverless pools for easier management of resources Guidance • Customer needs the best option for Spark on Azure • Customer teams are comfortable with notebooks and Spark • Customers need Auto-scaling and • Customer needs to build integrated and performant data pipelines • Customer is comfortable with limited regional availability (3 in preview, 8 by GA) Azure ML What It Is • Azure first party service for Machine Learning • Leverage existing ML libraries or extend with Python and R • Targets emerging data scientists with drag & drop offering • Targets professional data scientists with – Experimentation service – Model management service – Works with customers IDE of choice Guidance • Azure Machine Learning Studio is a GUI based ML tool for emerging Data Scientists to experiment and operationalize with least friction • Azure Machine Learning Workbench is not a compute engine & uses external engines for Compute, including SQL Server and Spark • AML deploys models to HDI Spark currently • AML should be able to deploy Azure Databricks in the near future L O O K I N G A C R O S S T H E O F F E R I N G S
- 40. P R O V I S I O N I N G A Z U R E D A T A B R I C K S W O R K S P A C E
- 41. G E N E R A L S P A R K C L U S T E R A R C H I T E C T U R E Data Sources (HDFS, SQL, NoSQL, …) Cluster Manager Worker Node Worker Node Worker Node Driver Program SparkContext
- 42. A Z U R E D A T A B R I C K S C L U S T E R A R C H I T E C T U R E Azure DB for PostgreSQL Webapp Azure Compute Cluster Manager Databricks’ Azure Account User’s Azure Account Azure Compute Spark Driver Azure Compute Spark Worker Azure Compute Spark Worker Jobs FileSystem Service Spark History Server Log Daemon Log Daemon
- 43. C L U S T E R M A N A G E R A R C H I T E C T U R E JobsWebapp Cluster Manager Cluster Azure Compute Spark Driver Azure Compute Spark Worker Azure Compute Spark Worker Database Instances, Clusters, Libraries, Hive Metastore, … Cluster Azure Compute Spark Driver Azure Compute Spark Worker Azure Compute Spark Worker Instance Manager Container Management Library Manager
- 45. D A T A B R I C K S A C C E S S C O N T R O L Access control can be defined at the user level via the Admin Console Workspace Access Control Defines who can who can view, edit, and run notebooks in their workspace Cluster Access Control Allows users to who can attach to, restart, and manage (resize/delete) clusters. Allows Admins to specify which users have permissions to create clusters Jobs Access Control Allows owners of a job to control who can view job results or manage runs of a job (run now/cancel) REST API Tokens Allows users to use personal access tokens instead of passwords to access the Databricks REST API Databricks Access Control
- 46. Clusters
- 47. C L U S T E R S ▪ Azure Databricks clusters are the set of Azure Linux VMs that host the Spark Worker and Driver Nodes ▪ Your Spark application code (i.e. Jobs) runs on the provisioned clusters. ▪ Azure Databricks clusters are launched in your subscription—but are managed through the Azure Databricks portal. ▪ Azure Databricks provides a comprehensive set of graphical wizards to manage the complete lifecycle of clusters—from creation to termination.
- 51. A Z U R E D A T A B R I C K S N O T E B O O K S O V E R V I E W Notebooks are a popular way to develop, and run, Spark Applications ▪ Notebooks are not only for authoring Spark applications but can be run/executed directly on clusters • Shift+Enter • • ▪ Notebooks support fine grained permissions—so they can be securely shared with colleagues for collaboration (see following slide for details on permissions and abilities) ▪ Notebooks are well-suited for prototyping, rapid development, exploration, discovery and iterative development Notebooks typically consist of code, data, visualization, comments and notes
- 52. M I X I N G L A N G U A G E S I N N O T E B O O K S You can mix multiple languages in the same notebook Normally a notebook is associated with a specific language. However, with Azure Databricks notebooks, you can mix multiple languages in the same notebook. This is done using the language magic command: • %python Allows you to execute python code in a notebook (even if that notebook is not python) • %sql Allows you to execute sql code in a notebook (even if that notebook is not sql). • %r Allows you to execute r code in a notebook (even if that notebook is not r). • %scala Allows you to execute scala code in a notebook (even if that notebook is not scala). • %sh Allows you to execute shell code in your notebook. • %fs Allows you to use Databricks Utilities - dbutils filesystem commands. • %md To include rendered markdown
- 53. L I B R A R I E S O V E R V I E W Enables external code to be imported and stored into a Workspace
- 55. D A T A B R I C K S S P A R K I S F A S T Benchmarks have shown Databricks to often have better performance than alternatives SOURCE: Benchmarking Big Data SQL Platforms in the Cloud
- 57. S P A R K S Q L O V E R V I E W Spark SQL is a distributed SQL query engine for processing structured data
- 58. L O C A L A N D G L O B A L T A B L E S Databricks registers global tables to the Hive metastore and makes them available across all clusters. Only global tables are visible in the Tables pane Azure Databricks Tables Databricks does not registers local tables in the Hive metastore and are only available within one cluster. Also known as temporary tables
- 59. A Z U R E S Q L D W I N T E G R A T I O N Integration enables structured data from SQL DW to be included in Spark Analytics Azure SQL Data Warehouse is a SQL-based fully managed, petabyte-scale cloud solution for data warehousing Azure Databricks Azure SQL DW ▪ You can bring in data from Azure SQL DW to perform advanced analytics that require both structured and unstructured data. ▪ Currently you can access data in Azure SQL DW via the JDBC driver. From within your spark code you can access just like any other JDBC data source. ▪ If Azure SQL DW is authenticated via AAD then Azure Databricks user can seamlessly access Azure SQL DW.
- 60. P O W E R B I I N T E G R A T I O N Enables powerful visualization of data in Spark with Power BI Power BI Desktop can connect to Azure Databricks clusters to query data using JDBC/ODBC server that runs on the driver node. • This server listens on port 10000 and it is not accessible outside the subnet where the cluster is running. • Azure Databricks uses a public HTTPS gateway • The JDBC/ODBC connection information can be obtained from the Cluster UI directly as shown in the figure. • When establishing the connection, you can use a Personal Access Token to authenticate to the cluster gateway. Only users who have attach permissions can access the cluster via the JDBC/ ODBC endpoint. • In Power BI desktop you can setup the connection by choosing the ODBC data source in the “Get Data” option.
- 61. C O S M O S D B I N T E G R A T I O N The Spark connector enables real-time analytics over globally distributed data in Azure Cosmos DB ▪ With Spark connector for Azure Cosmos DB, Apache Spark can now interact with all Azure Cosmos DB data models: Documents, Tables, and Graphs. • efficiently exploits the native Azure Cosmos DB managed indexes and enables updateable columns when performing analytics. • utilizes push-down predicate filtering against fast-changing globally-distributed data ▪ Some use-cases for Azure Cosmos DB + Spark include: • Streaming Extract, Transformation, and Loading of data (ETL) • Data enrichment • Trigger event detection • Complex session analysis and personalization • Visual data exploration and interactive analysis • Notebook experience for data exploration, information sharing, and collaboration Azure Cosmos DB is Microsoft's globally distributed, multi-model database service for mission-critical applications The connector uses the Azure DocumentDB Java SDK and moves data directly between Spark worker nodes and Cosmos DB data nodes
- 62. A Z U R E B L O B S T O R A G E I N T E G R A T I O N Data can be read from Azure Blob Storage using the Hadoop FileSystem interface. Data can be read from public storage accounts without any additional settings. To read data from a private storage account, you need to set an account key or a Shared Access Signature (SAS) in your notebook spark.conf.set ( "fs.azure.account.key.{Your Storage Account Name}.blob.core.windows.net", "{Your Storage Account Access Key}") Setting up an account key Setting up a SAS for a given container: spark.conf.set( "fs.azure.sas.{Your Container Name}.{Your Storage Account Name}.blob.core.windows.net", "{Your SAS For The Given Container}") Once an account key or a SAS is setup, you can use standard Spark and Databricks APIs to read from the storage account: val df = spark.read.parquet("wasbs://{Your Container Name}@m{Your Storage Account name}.blob.core.windows.net/{Your Directory Name}") dbutils.fs.ls("wasbs://{Your ntainer Name}@{Your Storage Account Name}.blob.core.windows.net/{Your Directory Name}")
- 63. A Z U R E D A T A L A K E I N T E G R A T I O N To read from your Data Lake Store account, you can configure Spark to use service credentials with the following snippet in your notebook spark.conf.set("dfs.adls.oauth2.access.token.provider.type", "ClientCredential") spark.conf.set("dfs.adls.oauth2.client.id", "{YOUR SERVICE CLIENT ID}") spark.conf.set("dfs.adls.oauth2.credential", "{YOUR SERVICE CREDENTIALS}") spark.conf.set("dfs.adls.oauth2.refresh.url", "https://login.windows.net/{YOUR DIRECTORY ID}/oauth2/token") After providing credentials, you can read from Data Lake Store using standard APIs: val df = spark.read.parquet("adl://{YOUR DATA LAKE STORE ACCOUNT NAME}.azuredatalakestore.net/{YOUR DIRECTORY NAME}") dbutils.fs.list("adl://{YOUR DATA LAKE STORE ACCOUNT NAME}.azuredatalakestore.net/{YOUR DIRECTORY NAME}")
- 65. S P A R K M L A L G O R I T H M S Spark ML Algorithms
- 67. S P A R K S T R U C T U R E D S T R E A M I N G O V E R V I E W ▪ Unifies streaming, interactive and batch queries—a single API for both static bounded data and streaming unbounded data. ▪ Runs on Spark SQL. Uses the Spark SQL Dataset/DataFrame API used for batch processing of static data. ▪ Runs incrementally and continuously and updates the results as data streams in. ▪ Supports app development in Scala, Java, Python and R. ▪ Supports streaming aggregations, event-time windows, windowed grouped aggregation, stream-to-batch joins. ▪ Features streaming deduplication, multiple output modes and APIs for managing/monitoring streaming queries. ▪ Built-in sources: Kafka, File source (json, csv, text, parquet) A unified system for end-to-end fault-tolerant, exactly-once stateful stream processing
- 69. D A T A B R I C K S R E S T A P I Cluster API Create/edit/delete clusters DBFS API Interact with the Databricks File System Groups API Manage groups of users Instance Profile API Allows admins to add, list, and remove instances profiles that users can launch clusters with Job API Create/edit/delete jobs Library API Create/edit/delete libraries Workspace API List/import/export/delete notebooks/folders Databricks REST API
- 70. D A T A B R I C K S A P I - A U T H E N T I C A T I O N Personal access tokens or passwords can be used to authenticate and access Databricks REST APIs -H "Authorization: Bearer TOKEN_VALUE"
- 71. © Microsoft Corporation A side-by-side comparison of capabilities and features Model deployment options Scoring interface provided Deployment environments Scalability of scoring interface Scoring requirements Model packaging SQL Server or SQL Database T-SQL stored procedure SQL Server 2017 database instance on-premises or in Azure VM Need to author Python or R code within a T-SQL stored procedure that loads the trained model from a table where it is stored and applies it in scoring. Serialized to table Limited to capacity of single server Azure Databricks Notebook or Job Load the trained model from storage and apply to scoring in notebook in Python, Scala, R, or SQL. Serialized to storage Azure Databricks cluster, model export Can scale across cluster resources Web service Create a Docker image that contains scoring service, model, and dependencies Docker image SQL Server, Hadoop Scales by deploying more instances in Azure Container Services Azure Machine Learning AKS, ACI edge via AML IoT, IoT edge via AML AKS, ACI IoT, IoT edge Spark and Batch AI