BRK 3259 70 473 Designing and implementing Cloud

BRK 3259

70 -473 Designing and implementing Cloud Data Platform Solutions 70 -475 Designing and Implementing Big Data Analytics Solutions Design & implement DB solutions Big data batch and interactive processing Manage DBMS security Big data real-time processing Design for HA, DR and Scale Implement Machine Learning Monitor and Manage implementations Operationalize these end-to-end

50 -60 questions ~2 - 3 hours to complete exam Can review questions Exam TIP: Mark what you want to review! 3 -4 case studies Cannot move between case studies No longer a Beta Exam! Qualifies for the Second Shot offer.

Exam TIP: Answer ALL questions!

Major Areas of the Test Design machine learning solutions (25 -30%) Operationalize end-to-end cloud analytics solutions (25 -30%) Design Big Data Batch and Interactive Processing Design machine learning solutions Design big data batch processing & interactive solutions (20 -25%) Design big data real-time processing solutions (20 - 25%) Design big data real-time processing solutions Operationalize end-to-end cloud analytics solutions

https: //msdn. microsoft. com/en-us/library/azure/gg 433040. aspx https: //msdn. microsoft. com/en-us/library/dn 749874. aspx#sec 4

Data Ingestion Interactive Power. Shell AZ Copy X-plat CLI Visual Studio Hadoop Command Line Streaming Data Stream Insight Reactive extensions (RX) Storm Custom/ 3 rd Party How I want to handle a one time bulk data load On-Premises vs. Cloud considerations How would I deal with small routine writes on a continual basis What is an appropriate directory structure for processing? Automation ADF SSIS Power. Shell Custom (Azure SDK) Log Files Flume SSIS Relational Scoop SSIS Polybase (APS)

• R Server on Spark (preview)

X X

§ Batch Layer § Speed Layer Serving Layer

Analytics Clients Power BI Excel Speed Layer Ingest SQL DB Event Hubs Azure Stream Analytics Import/Export Service HDInsight Storm Azure Data Factory HDInsight Spark Streaming Redis Azure ML Custom Apps Command Line Batch Processing Batch Layer HDInsight Sqoop Doc. DB Stream Processing Azure Data Lake HDInsight (Map. Reduce, Pig, Tez, Spark) Azure Batch Azure SQL DW HBase + Phoenix HDFS Blob Storage Batch View Serving Layer Azure ML Azure Data Factory Azure Search

Batch Layer Azure HDInsight Azure Blob Storage Map. Reduce, Pig, Hive, Oozie, SSIS Speed Layer Serving Layer Azure Storage Explorer, Excel, Power Query/pivot/map/view Reporting Services, Linq to Hive, SSAS Stream. Insight, Spark Azure SQL DB Azure Tables Mem. Cache Mongo. DB

Basic Steps for ML from “Introducing Azure Machine Learning” by David Chappell & Associates

Publishing an ML API Web Service