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.
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.
Job role: Data Engineer
Preparation for exam: DP-203
After completing this course, students will be able to:
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.
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.
Length: 32 academic hours
Access to the standalone exercise environment Microsoft Labs Online (MLO) for an additional fee.
Choose the Microsoft exam that suits you and buy it through IT Koolitus 10% cheaper.
Koolituse läbiviimise põhimõtted:
Koolitus toimub eesti keeles!
Koolituse osad on toetatud praktiliste harjutustega õpilaborites.
Tunnistuse väljastamise eelduseks on koolitusel osalemine terves mahus.
Maht: 32 akadeemilist tundi
Lisatasu eest ligipääs iseseisvale harjutuskeskkonnale Microsoft Labs Online (MLO).
Vali endale sobiv Microsofti eksam ja osta see läbi IT Koolituse 10% soodsamalt.
Continuing Education Curriculum Group: 0688 Information and Communication Technology Interdisciplinary curriculum group
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/ Täpsemat infot koolituse kohta saad:
The training price also includes:
As an added value, we offer:
You can participate in the training also with the Unemployment Insurance Fund training card.
See you at the training!