Ingeniería de datos en Microsoft Azure (DP 203T00)

Duración: 28 horas.
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A quien va dirigido

La audiencia principal de este curso son los profesionales de datos, arquitectos de datos y profesionales de inteligencia empresarial que desean aprender sobre ingeniería de datos y crear soluciones analíticas utilizando tecnologías de plataforma de datos que existen en Microsoft Azure. El público secundario de este curso: analistas de datos y científicos de datos que trabajan con soluciones analíticas creadas en Microsoft Azure.

Rol de trabajo: ingeniero de datos

Descripción

En este curso, el estudiante aprenderá sobre la ingeniería de datos en lo que respecta al trabajo con soluciones analíticas por lotes y en tiempo real utilizando tecnologías de plataforma de datos de Azure. Los estudiantes comenzarán por comprender las tecnologías básicas de computación y almacenamiento que se utilizan para construir una solución analítica. Los estudiantes aprenderán a …

A quien va dirigido

La audiencia principal de este curso son los profesionales de datos, arquitectos de datos y profesionales de inteligencia empresarial que desean aprender sobre ingeniería de datos y crear soluciones analíticas utilizando tecnologías de plataforma de datos que existen en Microsoft Azure. El público secundario de este curso: analistas de datos y científicos de datos que trabajan con soluciones analíticas creadas en Microsoft Azure.

Rol de trabajo: ingeniero de datos

Descripción

En este curso, el estudiante aprenderá sobre la ingeniería de datos en lo que respecta al trabajo con soluciones analíticas por lotes y en tiempo real utilizando tecnologías de plataforma de datos de Azure. Los estudiantes comenzarán por comprender las tecnologías básicas de computación y almacenamiento que se utilizan para construir una solución analítica. Los estudiantes aprenderán a explorar de forma interactiva los datos almacenados en archivos en un lago de datos. Aprenderán las diversas técnicas de ingesta que se pueden usar para cargar datos utilizando la capacidad de Apache Spark que se encuentra en Azure Synapse Analytics o Azure Databricks, o cómo ingerir usando Azure Data Factory o las canalizaciones de Azure Synapse. Los estudiantes también aprenderán las diversas formas en que pueden transformar los datos utilizando las mismas tecnologías que se utilizan para ingerir datos. Comprenderán la importancia de implementar la seguridad para garantizar que los datos estén protegidos en reposo o en tránsito. Luego, el estudiante mostrará cómo crear un sistema analítico en tiempo real para crear soluciones analíticas en tiempo real.

  • Explore las opciones de procesamiento y almacenamiento para cargas de trabajo de ingeniería de datos en Azure
  • Ejecute consultas interactivas utilizando grupos SQL sin servidor
  • Realizar exploración y transformación de datos en Azure Databricks
  • Explore, transforme y cargue datos en el almacén de datos con Apache Spark
  • Ingesta y carga datos en el almacén de datos
  • Transforme datos con Azure Data Factory o Azure Synapse Pipelines
  • Integre datos de portátiles con Azure Data Factory o Azure Synapse Pipelines
  • Admite el procesamiento analítico transaccional híbrido (HTAP) con Azure Synapse Link
  • Realice la seguridad de un extremo a otro con Azure Synapse Analytics
  • Realice el procesamiento de transmisiones en tiempo real con Stream Analytics
  • Cree una solución de procesamiento de transmisión con Event Hubs y Azure Databricks

Los estudiantes exitosos comienzan este curso con conocimientos de computación en la nube y conceptos básicos de datos y experiencia profesional con soluciones de datos.

Completando específicamente:

  • AZ-900: conceptos básicos de Azure
  • DP-900 - Conceptos básicos de datos de Microsoft Azure

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.

  • Introduction to Azure Synapse Analytics
  • Describe Azure Databricks
  • Introduction to Azure Data Lake storage
  • Describe Delta Lake architecture
  • Work with data streams by using Azure Stream Analytics
  • Combine streaming and batch processing with a single pipeline
  • Organize the data lake into levels of file transformation
  • Index data lake storage for query and workload acceleration
  • Describe Azure Synapse Analytics
  • Describe Azure Databricks
  • Describe Azure Data Lake storage
  • Describe Delta Lake architecture
  • Describe Azure Stream Analytics

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).

  • Explore Azure Synapse serverless SQL pools capabilities
  • Query data in the lake using Azure Synapse serverless SQL pools
  • Create metadata objects in Azure Synapse serverless SQL pools
  • Secure data and manage users in Azure Synapse serverless SQL pools
  • Query Parquet data with serverless SQL pools
  • Create external tables for Parquet and CSV files
  • Create views with serverless SQL pools
  • Secure access to data in a data lake when using serverless SQL pools
  • Configure data lake security using Role-Based Access Control (RBAC) and Access Control List
  • Understand Azure Synapse serverless SQL pools capabilities
  • Query data in the lake using Azure Synapse serverless SQL pools
  • Create metadata objects in Azure Synapse serverless SQL pools
  • Secure data and manage users in Azure Synapse serverless SQL pools

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.

  • Describe Azure Databricks
  • Read and write data in Azure Databricks
  • Work with DataFrames in Azure Databricks
  • Work with DataFrames advanced methods in Azure Databricks
  • Use DataFrames in Azure Databricks to explore and filter data
  • Cache a DataFrame for faster subsequent queries
  • Remove duplicate data
  • Manipulate date/time values
  • Remove and rename DataFrame columns
  • Aggregate data stored in a DataFrame
  • Describe Azure Databricks
  • Read and write data in Azure Databricks
  • Work with DataFrames in Azure Databricks
  • Work with DataFrames advanced methods in Azure Databricks

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.

  • Understand big data engineering with Apache Spark in Azure Synapse Analytics
  • Ingest data with Apache Spark notebooks in Azure Synapse Analytics
  • Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics
  • Integrate SQL and Apache Spark pools in Azure Synapse Analytics
  • Perform Data Exploration in Synapse Studio
  • Ingest data with Spark notebooks in Azure Synapse Analytics
  • Transform data with DataFrames in Spark pools in Azure Synapse Analytics
  • Integrate SQL and Spark pools in Azure Synapse Analytics
  • Describe big data engineering with Apache Spark in Azure Synapse Analytics
  • Ingest data with Apache Spark notebooks in Azure Synapse Analytics
  • Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics
  • Integrate SQL and Apache Spark pools in Azure Synapse Analytics

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.

  • Use data loading best practices in Azure Synapse Analytics
  • Petabyte-scale ingestion with Azure Data Factory
  • Perform petabyte-scale ingestion with Azure Synapse Pipelines
  • Import data with PolyBase and COPY using T-SQL
  • Use data loading best practices in Azure Synapse Analytics
  • Use data loading best practices in Azure Synapse Analytics
  • Petabyte-scale ingestion with Azure Data Factory

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.

  • Data integration with Azure Data Factory or Azure Synapse Pipelines
  • Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines
  • Execute code-free transformations at scale with Azure Synapse Pipelines
  • Create data pipeline to import poorly formatted CSV files
  • Create Mapping Data Flows
  • Perform data integration with Azure Data Factory
  • Perform code-free transformation at scale with Azure Data Factory

In this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines.

  • Orchestrate data movement and transformation in Azure Data Factory
  • Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines
  • Orchestrate data movement and transformation in Azure Synapse Pipelines

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.

  • Secure a data warehouse in Azure Synapse Analytics
  • Configure and manage secrets in Azure Key Vault
  • Implement compliance controls for sensitive data
  • Secure Azure Synapse Analytics supporting infrastructure
  • Secure the Azure Synapse Analytics workspace and managed services
  • Secure Azure Synapse Analytics workspace data
  • Secure a data warehouse in Azure Synapse Analytics
  • Configure and manage secrets in Azure Key Vault
  • Implement compliance controls for sensitive data

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.

  • Design hybrid transactional and analytical processing using Azure Synapse Analytics
  • Configure Azure Synapse Link with Azure Cosmos DB
  • Query Azure Cosmos DB with Apache Spark pools
  • Query Azure Cosmos DB with serverless SQL pools
  • Configure Azure Synapse Link with Azure Cosmos DB
  • Query Azure Cosmos DB with Apache Spark for Synapse Analytics
  • Query Azure Cosmos DB with serverless SQL pool for Azure Synapse Analytics
  • Design hybrid transactional and analytical processing using Azure Synapse Analytics
  • Configure Azure Synapse Link with Azure Cosmos DB
  • Query Azure Cosmos DB with Apache Spark for Azure Synapse Analytics
  • Query Azure Cosmos DB with SQL serverless for Azure Synapse 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.

  • Enable reliable messaging for Big Data applications using Azure Event Hubs
  • Work with data streams by using Azure Stream Analytics
  • Ingest data streams with Azure Stream Analytics
  • Use Stream Analytics to process real-time data from Event Hubs
  • Use Stream Analytics windowing functions to build aggregates and output to Synapse Analytics
  • Scale the Azure Stream Analytics job to increase throughput through partitioning
  • Repartition the stream input to optimize parallelization
  • Enable reliable messaging for Big Data applications using Azure Event Hubs
  • Work with data streams by using Azure Stream Analytics
  • Ingest data streams with Azure Stream Analytics

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.

  • Process streaming data with Azure Databricks structured streaming
  • Explore key features and uses of Structured Streaming
  • Stream data from a file and write it out to a distributed file system
  • Use sliding windows to aggregate over chunks of data rather than all data
  • Apply watermarking to remove stale data
  • Connect to Event Hubs read and write streams
  • Process streaming data with Azure Databricks structured streaming