Data Engineering on Microsoft Azure (DP-203T00)

Managing data has become of the key components of success in today’s data-centric world. With exabytes and petabytes of data getting generated on a daily basis, there is a hidden treasure that can be unearthed by proper analysis. Data Engineering has become one of the most sought-after professions by individuals globally across industry sectors. With businesses moving their critical processes onto the cloud, it is critical to understand how to perform data analysis on the cloud to reap rich dividends.

Today, everything depends on thorough data analysis to understand the customer pain points and also to identify new opportunities to gain market share. In such a challenging business landscape, it is critical for individuals and enterprises to know how to integrate, transform, and consolidate data across platforms. This Data Engineering on Microsoft to Azure certification (DP-203) is one such certification that helps professionals to build some of the best analytics solutions using Microsoft Azure as a platform.

During this four-days course students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. They will learn how to interactively explore data stored in files in a data lake. Students will study the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines. They will also learn the various ways they can transform the data using the same technologies that is used to ingest data. Patricipants will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The students will then show how to create a real-time analytical system to create real-time analytical solutions.

Target audience:
The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course data analysts and data scientists who work with analytical solutions built on Microsoft Azure.

Prerequisites to the course (recommended):
Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions.

Specifically completing:

• AZ-900 – Azure Fundamentals
• DP-900 – Microsoft Azure Data Fundamentals

The training topics and description:

• Module 1: Explore compute and storage options for data engineering workloads
  • This module provides an overview of the Azure compute and storage technology options that are available to data engineers building analytical workloads. This module teaches ways to structure the data lake, and to optimize the files for exploration, streaming, and batch workloads. The student will learn how to organize the data lake into levels of data refinement as they transform files through batch and stream processing. Then they will learn how to create indexes on their datasets, such as CSV, JSON, and Parquet files, and use them for potential query and workload acceleration.
• Module 2: Run interactive queries using Azure Synapse Analytics serverless SQL pools
  • In this module, students will learn how to work with files stored in the data lake and external file sources, through T-SQL statements executed by a serverless SQL pool in Azure Synapse Analytics. Students will query Parquet files stored in a data lake, as well as CSV files stored in an external data store. Next, they will create Azure Active Directory security groups and enforce access to files in the data lake through Role-Based Access Control (RBAC) and Access Control Lists (ACLs).
• Module 3: Data exploration and transformation in Azure Databricks
  • This module teaches how to use various Apache Spark DataFrame methods to explore and transform data in Azure Databricks. The student will learn how to perform standard DataFrame methods to explore and transform data. They will also learn how to perform more advanced tasks, such as removing duplicate data, manipulate date/time values, rename columns, and aggregate data.
• Module 4: Explore, transform, and load data into the Data Warehouse using Apache Spark
  • This module teaches how to explore data stored in a data lake, transform the data, and load data into a relational data store. The student will explore Parquet and JSON files and use techniques to query and transform JSON files with hierarchical structures. Then the student will use Apache Spark to load data into the data warehouse and join Parquet data in the data lake with data in the dedicated SQL pool.
• Module 5: Ingest and load data into the data warehouse
  • This module teaches students how to ingest data into the data warehouse through T-SQL scripts and Synapse Analytics integration pipelines. The student will learn how to load data into Synapse dedicated SQL pools with PolyBase and COPY using T-SQL. The student will also learn how to use workload management along with a Copy activity in a Azure Synapse pipeline for petabyte-scale data ingestion.
• Module 6: Transform data with Azure Data Factory or Azure Synapse Pipelines
  • This module teaches students how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flowss, and perform data movement into one or more data sinks.
• Module 7: Orchestrate data movement and transformation in Azure Synapse Pipelines
  • In this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines.
• Module 8: End-to-end security with Azure Synapse Analytics
  • In this module, students will learn how to secure a Synapse Analytics workspace and its supporting infrastructure. The student will observe the SQL Active Directory Admin, manage IP firewall rules, manage secrets with Azure Key Vault and access those secrets through a Key Vault linked service and pipeline activities. The student will understand how to implement column-level security, row-level security, and dynamic data masking when using dedicated SQL pools.
• Module 9: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
  • In this module, students will learn how Azure Synapse Link enables seamless connectivity of an Azure Cosmos DB account to a Synapse workspace. The student will understand how to enable and configure Synapse link, then how to query the Azure Cosmos DB analytical store using Apache Spark and SQL serverless.
• Module 10: Real-time Stream Processing with Stream Analytics
  • In this module, students will learn how to process streaming data with Azure Stream Analytics. The student will ingest vehicle telemetry data into Event Hubs, then process that data in real time, using various windowing functions in Azure Stream Analytics. They will output the data to Azure Synapse Analytics. Finally, the student will learn how to scale the Stream Analytics job to increase throughput.
• Module 11: Create a Stream Processing Solution with Event Hubs and Azure Databricks
  • In this module, students will learn how to ingest and process streaming data at scale with Event Hubs and Spark Structured Streaming in Azure Databricks. The student will learn the key features and uses of Structured Streaming. The student will implement sliding windows to aggregate over chunks of data and apply watermarking to remove stale data. Finally, the student will connect to Event Hubs to read and write streams.

More detailed information about the training: https://docs.microsoft.com/en-us/learn/certifications/courses/dp-203t00

After completing this course, students will be able to:

• Explore compute and storage options for data engineering workloads in Azure
• Run interactive queries using serverless SQL pools
• Perform data Exploration and Transformation in Azure Databricks
• Explore, transform, and load data into the Data Warehouse using Apache Spark
• Ingest and load Data into the Data Warehouse
• Transform Data with Azure Data Factory or Azure Synapse Pipelines
• Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines
• Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
• Perform end-to-end security with Azure Synapse Analytics
• Perform real-time Stream Processing with Stream Analytics
• Create a Stream Processing Solution with Event Hubs and Azure Databricks

Training Principles:

• The training is held in Estonian!
• Each learning module is supported by practical exercises with hands-on laboratories.
• The prerequisite for issuing the certificate is full participation in training.
• Access to the standalone exercise environment Microsoft Labs Online (MLO) for an additional fee.

Koolitaja:
Henn Sarv
Henn koolitab IT Koolituses juba üle 25 aasta. Koolituskogemus oli tal ka varem – jagas paar aastat teadmisi Novelli administraatoritele. IT Koolituses on ta keskendunud peamiselt (aga mitte ainult) Microsofti toodetele, seda nii tavakasutajatele (Excelist Visioni) kuni süsteemiadministraatorite ja programmeerijateni. Peamiselt siiski kõik, mis puudutab andmebaase (SQL) ja ärianalüüsi (BI, Excel).
Tugevused koolitajana
Hoolimata sellest, et Henn on 25+ aastat klassi ees seisnud, ei ole ta kaotanud sidet igapäevase eluga: vahepeal on ta loonud arendusettevõtte ja jõudnud selle maha müüa; on konsulteerinud ja juurutanud süsteeme mitmes ettevõttes, peab talu ja korraldab sulgpallivõistlusi. Pidev side tegeliku eluga on see, mis aitab Hennul olla ekspert ja usaldusväärne allikas ka teadmiste jagamisel. Hennu enda õppimisvõime ja üle 60 sooritatud erialaeksami võimaldab õppijaid suurepäraselt mõista ning on tugevuseks ka koolitajana.

The training price also includes:

• study materials;
• a trainer’s consultation on the topics learned, by e-mail after the training;
• certificate.

As an added value, we offer:

• free parking;
• lunch;
• hot drinks with cookies;
• fresh fruits.

Küsin koolituse kohta lisainfot