Data Processing with Azure from Coursera

What's inside

Syllabus

Introduction

This Azure training course is designed to equip the students with the knowledge need to process, store and analyze data for making informed business decisions. Through this Azure course, the student will understand what big data is along with the importance of big data analytics, which will improve the students mathematical and programming skills. Students will learn the most effective method of using essential analytical tools such as R, and Apache Spark.

Section 1 - Batch Processing with Databricks and Data Factory on Azure

One of the primary benefits of Azure Databricks is its ability to integrate with many other data environments to pull data through an ETL or ELT process. In module course, we examine each of the E, L, and T to learn how Azure Databricks can help ease us into a cloud solution.

Section 2 - Creating Pipelines and Activities

Processing big data in real-time is now an operational necessity for many businesses. Azure Stream Analytics is Microsoft’s serverless real-time analytics offering for complex event processing. In this section we examine how customers unlock valuable insights and gain competitive advantage by harnessing the power of big data.

Section 3 - Link Services and Datasets

A data factory can have one or more pipelines. A pipeline is a logical grouping of activities that together perform a task. The activities in a pipeline define actions to perform on your data. Before you create a dataset, you must create a linked service to link your data store to the data factory. This section deals with linked services and data sets within Azure Blob Storage.

Section 4 - Schedules and Triggers

Azure Data Factory is a fully managed, cloud-based data orchestration service that enables data movement and transformation. In this section, we explore scheduling triggers for Azure Data Factory to automate your pipeline execution.

Section 5 - Selecting Windowing Functions

In time-streaming scenarios, performing operations on the data contained in temporal windows is a common pattern. Stream Analytics has native support for windowing functions, enabling developers to author complex stream processing jobs with minimal effort. In this section, we study windowing functions related to in-stream analytics.

Section 6 - Configuring Input and Output for Streaming Data Solutions

This section teaches how to analyze phone call data using Azure Stream Analytics. The phone call data, generated by a client application, contains some fraudulent calls, which will be filtered by the Stream Analytics job.

Section 7 - ELT versus ETL in Polybase

Traditional SMP data warehouses use an Extract, Transform and Load (ETL) process for loading data. Azure SQL Data Warehouse is a massively parallel processing (MPP) architecture that takes advantage of the scalability and flexibility of compute and storage resources. Utilizing an Extract, Load, and Transform (ELT) process can take advantage of MPP and eliminate resources needed to transform the data prior to loading.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches essential analytical tools such as Python, R, and Apache Spark

Helps students understand the importance of big data and big data analytics for business decision-making

Develops skills for processing, storing, and analyzing big data

Provides hands-on labs and interactive materials for better learning

Improves students' mathematical and programming skills

Is taught by Kenny Mobley and Samant Bali, who are experienced in data analytics

Reviews summary

Mixed reviews for azure data processing

Learners say this course on Data Processing with Azure offers a general overview of Microsoft's services with the same name. The contents of the course, however, are considered to be outdated by some students. Despite the outdated content, some learners found that the course provided them with a good understanding of data streaming, ELT, and ETL. Also, some learners felt the course was difficult to follow due to the instructor primarily reading from slides. Overall, this course may be of value to learners looking to gain a general overview of Data Processing with Azure, with the understanding that more up-to-date resources may be needed to supplement this course.

Course provides a good surface level overview of Azure Data Processing.

"This course gave a detailed overview of Microsoft azure"

"The course gives you an overview off Azure Data Factory and covers some ways on how to read stream events in Azure."

"I've definitely come out of it with a broadly better understanding of ETL, ELT, Data Streaming and the various tools available on Azure for performing these tasks."

Instructor primarily reads slides and does not add much value beyond what students could learn from documentation.

"The instructor is reading the slides like a newsreading robot."

"I was expecting some real time examples and a proper lecture, instead of slides being read by the instructor."

"Instructor mostly reads text from the documentation and from the internet. He does not add any extra value."

Course content may be outdated compared to recent advancements in Azure Data Processing.

"It makes no sense. Worst course I've taken."

"Great content, but already a little bit outdated"

"Significantly out of date. Also, doesn't have good continuity of the information being presented."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Processing with Azure with these activities:

Read 'Big Data Analytics' by Michael Minelli

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Read a book on big data analytics to gain insights and understanding of the subject matter.

View Big Data, Big Analytics: Emerging Business... on Amazon

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Read one chapter per week
Take notes and highlight important concepts
Discuss the book with classmates or online forums

Review Python programming

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Prepare for this course by reviewing python programming. This will provide a solid foundation for understanding the materials.

Browse courses on Python Programming

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Read online tutorials about python programming basics
Complete practice problems using an online programming judge
Work through a python programming tutorial or book

Review linear algebra concepts

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Refresh your knowledge of linear algebra concepts used in this course, such as matrix operations and vector spaces.

Browse courses on Linear Algebra

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Review notes or textbooks from a previous linear algebra course
Solve practice problems involving linear algebra concepts
Complete online quizzes or assessments on linear algebra

Five other activities

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Show all eight activities

Organize your course materials

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Keep your course materials organized to enhance your understanding of the content.

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Create a system for organizing your notes, assignments, and other materials
Regularly review and update your organized materials
Consider using digital tools or apps to assist in organizing your materials

Complete Azure Databricks practice problems

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Enhance your understanding of Azure Databricks by completing practice problems.

Browse courses on Azure Databricks

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Find online resources or tutorials that provide practice problems for Azure Databricks
Set aside time to regularly work through these practice problems
Review your solutions and identify areas for improvement

Join a study group with classmates

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Study with classmates regularly to discuss course materials, ask questions, and share insights.

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Find a group of classmates to study with
Meet regularly to discuss course materials
Collaborate on assignments and projects

Create a small data analysis project

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Develop a small project to solidify your understanding of data analysis techniques.

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Identify a small dataset to analyze
Use data analysis tools and techniques to explore and analyze the data
Summarize your findings and insights in a brief report or presentation

Participate in a data science or analytics competition

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Test your skills and knowledge in a competitive environment.

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Identify a relevant data science or analytics competition
Form a team or participate individually
Work on the competition and submit your solution

Career center

Learners who complete Data Processing with Azure will develop knowledge and skills that may be useful to these careers:

Chief Data Officer

Chief Data Officers are responsible for overseeing all aspects of data management within an organization. They work with senior leadership to set the strategic direction for data initiatives and ensure that data is used effectively to support business goals. This course may be useful for aspiring Chief Data Officers because it provides a comprehensive overview of data processing, storage, analysis, and governance.

See salaries and explore the career path for Chief Data Officer

Machine Learning Engineer

Machine Learning Engineers design, build, and deploy machine learning models. They work with a variety of data sources and tools, and they may specialize in a particular area, such as natural language processing or computer vision. This course may be useful for aspiring Machine Learning Engineers because it provides a foundation in data processing, analysis, and visualization.

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Data Scientist

Data Scientists use data to build predictive models and solve business problems. They work with a variety of data sources and tools, and they may specialize in a particular area, such as machine learning or artificial intelligence. This course may be useful for aspiring Data Scientists because it provides a foundation in data processing, analysis, and visualization.

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Data Architect

Data Architects design and build data architectures that meet the needs of businesses. They work with a variety of data sources and technologies, and they may specialize in a particular area, such as data warehousing or big data. This course may be useful for aspiring Data Architects because it provides a foundation in data processing, storage, and analysis.

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Data Governance Analyst

Data Governance Analysts are responsible for developing and implementing data governance policies and procedures. They work with a variety of stakeholders, including data owners, data stewards, and data users, to ensure that data is used ethically and responsibly. This course may be useful for aspiring Data Governance Analysts because it provides a foundation in data processing, storage, and security.

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Data Scientist Manager

Data Scientist Managers lead teams of Data Scientists and other data professionals. They are responsible for setting the strategic direction for data science projects and ensuring that projects are completed on time and within budget. This course may be useful for aspiring Data Scientist Managers because it provides a foundation in data processing, analysis, and management.

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Machine Learning Researcher

Machine Learning Researchers develop new machine learning algorithms and techniques. They work with a variety of data sources and tools, and they may specialize in a particular area, such as natural language processing or computer vision. This course may be useful for aspiring Machine Learning Researchers because it provides a foundation in data processing, analysis, and visualization.

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Data Science Consultant

Data Science Consultants help businesses use data to make better decisions. They work with a variety of clients, including Fortune 500 companies and startups, to implement data science solutions. This course may be useful for aspiring Data Science Consultants because it provides a foundation in data processing, analysis, and visualization.

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Business Analyst

Business Analysts help businesses improve their operations and make better decisions by analyzing data. They work with a variety of data sources and tools, and they may specialize in a particular area, such as financial analysis or marketing research. This course may be useful for aspiring Business Analysts because it provides a foundation in data processing, analysis, and visualization.

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Data Engineer

Data Engineers design, build, and maintain data pipelines that collect, transform, and store data for analysis. They work closely with Data Scientists and other data professionals to ensure that data is available in a timely and reliable manner. This course may be useful for aspiring Data Engineers because it provides a foundation in data processing, storage, and analysis.

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Statistician

Statisticians collect, analyze, and interpret data to help businesses make informed decisions. They use a variety of statistical methods to draw conclusions from data, and they may specialize in a particular area, such as survey research or quality control. This course may be useful for aspiring Statisticians because it provides a foundation in data processing, analysis, and visualization.

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Database Administrator

Database Administrators maintain and optimize databases to ensure that they are available and reliable. They work with a variety of database technologies, and they may specialize in a particular area, such as performance tuning or security. This course may be useful for aspiring Database Administrators because it provides a foundation in data storage, management, and security.

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Data Analyst

Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. They use a variety of tools and techniques to extract insights from data, and they may specialize in a particular area, such as financial analysis or marketing research. This course may be useful for aspiring Data Analysts because it provides a foundation in data processing, analysis, and visualization.

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Product Manager

Product Managers are responsible for developing and launching new products and features. They work with a variety of stakeholders, including engineers, designers, and marketers, to ensure that products meet the needs of customers. This course may be useful for aspiring Product Managers because it provides a foundation in data processing, analysis, and visualization.

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Software Engineer

Software Engineers design, build, and maintain software applications. They work with a variety of programming languages and technologies, and they may specialize in a particular area, such as web development or mobile development. This course may be useful for aspiring Software Engineers because it provides a foundation in data processing, storage, and analysis.

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