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DP-200 Azure Data Solution - Implementation

Couple of remarks about course and training of Azure Data Solitions Free training materials (corseware) on FutureProof website

Exam

  • Not the easiest one, but not so difficult if you compare it to AZ-300 even to AZ-103
  • Broad range of technologies is covered in some depth
  • No virtual labs :-(
  • Databricks - spin up cluster, working with notebook, connecting to data, basic libraries, Spark, Python
  • Storage - focus on Data Lake Gen 2 (but Gen 1 as well), loading blobs, using polybase, mounting to Databricks, security
  • CosmosDB - scalability, avaliability, partitioning, RU
  • Azure SQL RDBMS - more DW than SQL, Polybase, ADF load, partitioning (indexes, distributing tables to node), vCPU, DTU, DWU
  • Stream Analytics - SU, IoT hub, Event hub/ Event grid, storage, azure functions
  • ADF - basic concepts, transformations, SSIS
  • Monitoring - Azure Log analytics, Alerts, Actions
  • Security - how to secure data TDE, RLS, CLS, RBAC, AAD, keys, credentials

Recommended preparation for exam

  • Practice, practice, practice
  • Exam does not have any labs, but if you will play with technology around, you will get most of the answers for the questions on the exam.

Usuall schedule

  1. Azure for the Data Engineer
  2. Working with Data Storage
  3. Enabling Team Based Data Science with Azure Databricks
  4. Building Globally Distributed Databases with Cosmos DB
  5. Working with Relational Data Stores in the Cloud
  6. Performing Real-Time Analytics with Stream Analytics
  7. Orchestrating Data Movement with Azure Data Factory
  8. Securing Azure Data Platforms
  9. Monitoring and Troubleshooting Data Storage and Processing

Training sessions specific schedule

17.09.2019 - Warszawa

  1. Azure for the Data Engineer

  2. Working with Data Storage

  3. Building Globally Distributed Databases with Cosmos DB

  4. Working with Relational Data Stores in the Cloud

  5. Securing Azure Data Platforms

  6. Orchestrating Data Movement with Azure Data Factory

  7. Enabling Team Based Data Science with Azure Databricks

  8. Performing Real-Time Analytics with Stream Analytics

  9. Monitoring and Troubleshooting Data Storage and Processing

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