Cod: MS_DP-203 Cloud MICROSOFT Dificultate: Avansat

Data Engineering on Microsoft Azure

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Preț 900,00 €

Cerere data speciala

Obiectiv

n this course, the student will learn about the data engineering patterns and practices as it pertains to working with batch and real-time analytical solutions using Azure data platform technologies. Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. They will then explore how to design an analytical serving layers and focus on data

Durată

4 Zile (8h/day)

Cerințe

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

Conţinut

Course outline 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.

Lessons 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 Lab : Explore compute and storage options for data engineering workloads 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 After completing this module, students will be able to:

Describe Azure Synapse Analytics Describe Azure Databricks Describe Azure Data Lake storage Describe Delta Lake architecture Describe Azure Stream Analytics Module 2: Design and implement the serving layer This module teaches how to design and implement data stores in a modern data warehouse to optimize analytical workloads. The student will learn how to design a multidimensional schema to store fact and dimension data. Then the student will learn how to populate slowly changing dimensions through incremental data loading from Azure Data Factory.

Lessons Design a multidimensional schema to optimize analytical workloads Code-free transformation at scale with Azure Data Factory Populate slowly changing dimensions in Azure Synapse Analytics pipelines Lab : Designing and Implementing the Serving Layer Design a star schema for analytical workloads Populate slowly changing dimensions with Azure Data Factory and mapping data flows After completing this module, students will be able to:

Design a star schema for analytical workloads Populate a slowly changing dimensions with Azure Data Factory and mapping data flows Module 3: Data engineering considerations for source files This module explores data engineering considerations that are common when loading data into a modern data warehouse analytical from files stored in an Azure Data Lake, and understanding the security consideration associated with storing files stored in the data lake.

Lessons Design a Modern Data Warehouse using Azure Synapse Analytics Secure a data warehouse in Azure Synapse Analytics Lab : Data engineering considerations Managing files in an Azure data lake Securing files stored in an Azure data lake After completing this module, students will be able to:

Design a Modern Data Warehouse using Azure Synapse Analytics Secure a data warehouse in Azure Synapse Analytics Module 4: 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).

Lessons 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 Lab : Run interactive queries using 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 After completing this module, students will be able to:

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 Module 5: 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.

Lessons 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 Lab : Explore, transform, and load data into the Data Warehouse using Apache Spark 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 After completing this module, students will be able to:

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 Module 6: 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.

Lessons Describe Azure Databricks Read and write data in Azure Databricks Work with DataFrames in Azure Databricks Work with DataFrames advanced methods in Azure Databricks Lab : Data Exploration and Transformation 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 After completing this module, students will be able to:

Describe Azure Databricks Read and write data in Azure Databricks Work with DataFrames in Azure Databricks Work with DataFrames advanced methods in Azure Databricks Module 7: 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.

Lessons Use data loading best practices in Azure Synapse Analytics Petabyte-scale ingestion with Azure Data Factory Lab : Ingest and load Data into the Data Warehouse 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 After completing this module, students will be able to:

Use data loading best practices in Azure Synapse Analytics Petabyte-scale ingestion with Azure Data Factory Module 8: 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.

Lessons Data integration with Azure Data Factory or Azure Synapse Pipelines Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines Lab : Transform Data 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 After completing this module, students will be able to:

Perform data integration with Azure Data Factory Perform code-free transformation at scale with Azure Data Factory Module 9: 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.

Lessons Orchestrate data movement and transformation in Azure Data Factory Lab : Orchestrate data movement and transformation in Azure Synapse Pipelines Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines After completing this module, students will be able to:

Orchestrate data movement and transformation in Azure Synapse Pipelines Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse In this module, students will learn strategies to optimize data storage and processing when using dedicated SQL pools in Azure Synapse Analytics. The student will know how to use developer features, such as windowing and HyperLogLog functions, use data loading best practices, and optimize and improve query performance.

Lessons Optimize data warehouse query performance in Azure Synapse Analytics Understand data warehouse developer features of Azure Synapse Analytics Lab : Optimize Query Performance with Dedicated SQL Pools in Azure Synapse Understand developer features of Azure Synapse Analytics Optimize data warehouse query performance in Azure Synapse Analytics Improve query performance After completing this module, students will be able to:

Optimize data warehouse query performance in Azure Synapse Analytics Understand data warehouse developer features of Azure Synapse Analytics Module 11: Analyze and Optimize Data Warehouse Storage In this module, students will learn how to analyze then optimize the data storage of the Azure Synapse dedicated SQL pools. The student will know techniques to understand table space usage and column store storage details. Next the student will know how to compare storage requirements between identical tables that use different data types. Finally, the student will observe the impact materialized views have when executed in place of complex queries and learn how to avoid extensive logging by optimizing delete operations.

Lessons Analyze and optimize data warehouse storage in Azure Synapse Analytics Lab : Analyze and Optimize Data Warehouse Storage Check for skewed data and space usage Understand column store storage details Study the impact of materialized views Explore rules for minimally logged operations After completing this module, students will be able to:

Analyze and optimize data warehouse storage in Azure Synapse Analytics Module 12: 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.

Lessons 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 Lab : Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link 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 After completing this module, students will be able to:

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 Module 13: 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.

Lessons Secure a data warehouse in Azure Synapse Analytics Configure and manage secrets in Azure Key Vault Implement compliance controls for sensitive data Lab : End-to-end security with Azure Synapse Analytics Secure Azure Synapse Analytics supporting infrastructure Secure the Azure Synapse Analytics workspace and managed services Secure Azure Synapse Analytics workspace data After completing this module, students will be able to:

Secure a data warehouse in Azure Synapse Analytics Configure and manage secrets in Azure Key Vault Implement compliance controls for sensitive data Module 14: 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.

Lessons 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 Lab : Real-time Stream Processing with 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 After completing this module, students will be able to:

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 Module 15: 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.

Lessons Process streaming data with Azure Databricks structured streaming Lab : Create a Stream Processing Solution with Event Hubs and Azure Databricks 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 After completing this module, students will be able to:

Process streaming data with Azure Databricks structured streaming Module 16: Build reports using Power BI integration with Azure Synpase Analytics In this module, the student will learn how to integrate Power BI with their Synapse workspace to build reports in Power BI. The student will create a new data source and Power BI report in Synapse Studio. Then the student will learn how to improve query performance with materialized views and result-set caching. Finally, the student will explore the data lake with serverless SQL pools and create visualizations against that data in Power BI.

Lessons Create reports with Power BI using its integration with Azure Synapse Analytics Lab : Build reports using Power BI integration with Azure Synpase Analytics Integrate an Azure Synapse workspace and Power BI Optimize integration with Power BI Improve query performance with materialized views and result-set caching Visualize data with SQL serverless and create a Power BI report After completing this module, students will be able to:

Create reports with Power BI using its integration with Azure Synapse Analytics Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics This module explores the integrated, end-to-end Azure Machine Learning and Azure Cognitive Services experience in Azure Synapse Analytics. You will learn how to connect an Azure Synapse Analytics workspace to an Azure Machine Learning workspace using a Linked Service and then trigger an Automated ML experiment that uses data from a Spark table. You will also learn how to use trained models from Azure Machine Learning or Azure Cognitive Services to enrich data in a SQL pool table and then serve prediction results using Power BI.

Lessons Use the integrated machine learning process in Azure Synapse Analytics Lab : Perform Integrated Machine Learning Processes in Azure Synapse Analytics Create an Azure Machine Learning linked service Trigger an Auto ML experiment using data from a Spark table Enrich data using trained models Serve prediction results using Power BI After completing this module, students will be able to:

Use the integrated machine learning process in Azure Synapse Analytics

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Sălile de instruire se modernizează in mod continuu. În Centrul de Competențe, folosim pentru instruire și seminarii Cisco Spark Board. Spark Board este în esență o tabletă uriașă care combină funcțiile și ecranul whiteboard pentru videoconferințe. Aveți ocazia unică de a experimenta acest mediu modern de comunicare.

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Echipa de traineri

O echipă bine pregătită, cu certificări importante și experință în domeniul IT, este pilonul principal al ALEF Training Center. Punem preț pe dezvoltarea continuă a trainerilor noștri, astfel încât să îți oferim toate cursurile de care ai nevoie. Echipa noastră este formată din peste 50 de traineri calificați, specializați în multe domenii, gata să parcurgă alături de tine și echipa ta tot traseul necesar unei formări profesionale.

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Dana Chibac este Business Development Manager al departamentului de Training și are o experiență de 20 de ani în zona IT&C. Dana Chibac a preluat departamentul de Training din cadrul ALEF în urmă cu 2 ani și jumătate și l-a dezvoltat constant, fiind astăzi unul dintre cele mai bune centre de training pentru zona IT din România.

Gabriela Lefter
Training Assistant

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Gabi s-a alăturat echipei ALEF Training Center în luna Iunie 2021 având la bază o experiență în lumea IT&C, a acceptat provocarea să fie alături de echipa de training și va crește alături de noi, oferind suport participanților la curs, precum și colaboratorilor ALEF.


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Cisco Trainer ALEF

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George Ciolacu este System Engineer și instructor certificat Cisco și are experință vastă în industria tehnologiei informației. Este specializat în tehnologii de rețea, precum routing, switching si security și are certificări importante, precum CCNP Enterprise si CCNP Security.

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Bogdan Păun este Microsoft System Engineer si Microsoft Certified Trainer în cadrul ALEF Distribution RO, cu o experiență de peste 10 ani în zona IT. Activează în proiecte ce variază, de la implementări de soluții până la livrarea de training-uri custom sau oficiale Microsoft, având certificări atât în zona de licențiere, cât și în cea tehnică.

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Microsoft Trainer ALEF

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Adrian Topa este Azure System Engineer și Microsoft Certified Trainer în cadrul ALEF Distribution RO, cu o experiență de peste 10 ani în domeniul informației tehnologice. De asemenea, este specialist Cloud, deținând multiple certificări Microsoft Azure.

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Sergiu Calinciuc este trainer certificat Cisco, cu o experiență de 10 ani în domeniul IT și expertiză pe tehnologiile de colaborare. De asemenea, Sergiu are cunoștințe avansate despre infrastructurile de tip enterprise networking - routing si switching. Pe baza certificărilor obținute, precum CCNA si CCNP, este avizat să ofere servicii de consultanță și training-uri tehnice partenerilor ALEF si clientilor acestora.

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Pasionat încă din adolescență de tehnologie și în special de networking, Rareș Odobescu este certificat ca instructor Cisco, pregătit să împărtășească cu bucurie cunoștințele pe care le-a acumulat de-a lungul proiectelor complexe în care a fost implicat.

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Adrian Murgescu are o bogată experiență în tehnologiile de Data Center. Deține o serie de certificări la nivel profesional de la Cisco, VMware si Veeam și oferă constant servicii de implementare și training către partenerii ALEF.

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Daniela Apostol
Cisco Trainer ALEF

Certificări

  • 4011 Recognition
  • CCDA
  • CCIE Certification - Data Center
  • CCNA Data Center
  • CCNA Routing and Switching
  • CCNA Security
  • CCNP Data Center
  • CCNP Routing and Switching
  • Cisco Data Center Unified Computing Design Specialist
  • Cisco Data Center Unified Fabric Design Specialist
  • Cisco Data Center Unified Fabric Support Specialist

Trainerii noștri se remarcă prin faptul că au o experiență practică vastă, dobândită de-a lungul proiectelor complexe la care au lucrat. Astfel, aceștia pot aborda orice subiect sau problematică ce vă este de interes, cursurile având parte teoretică și demo-uri practice pentru e înțelege cât mai bine soluția/produsul, în vederea obținerii certificărilor necesare.

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Training-uri

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Săli de training

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Certificate

50+

Lectori certificați

6

Țări

Unicitatea lectorilor noștri se datorează în principal faptului că au o experiență practică vastă, bazată pe diverse proiecte. Astfel, ei pot răspunde foarte flexibil la orice întrebare sau comentariu venind de la cursanți. Lectorii își transmit astfel cunoștințele practice participanților, la cursurile de formare. Datorită sinergiei funcționării fiabile a companiei și anilor de experiență, am reușit să răspundem în mod flexibil la viitoarele schimbări în specializările Cisco, astfel încât să putem garanta o gamă largă de cursuri certificate care vă vor permite ție și colegilor tăi să obțineți know-how-ul necesar pentru testele ulterioare de certificare.

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