Big Data Integration and Processing from Coursera

At the end of the course, you will be able to:

*Retrieve data from example database and big data management systems

*Describe the connections between data management operations and the big data processing patterns needed to utilize them in large-scale analytical applications

*Identify when a big data problem needs data integration

*Execute simple big data integration and processing on Hadoop and Spark platforms

This course is for those new to data science. Completion of Intro to Big Data is recommended. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Refer to the specialization technical requirements for complete hardware and software specifications.

Hardware Requirements:

(A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size.

Software Requirements:

This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge (except for data charges from your internet provider). Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.

What's inside

Syllabus

Welcome to Big Data Integration and Processing

Welcome to the third course in the Big Data Specialization. This week you will be introduced to basic concepts in big data integration and processing. You will be guided through installing the Cloudera VM, downloading the data sets to be used for this course, and learning how to run the Jupyter server.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Introduces foundational concepts essential for understanding big data integration and processing

Taught by renowned experts in the field, Amarnath Gupta and Ilkay Altintas

Covers vital aspects of big data integration, from basic concepts to advanced techniques

Develops hands-on skills in retrieving, integrating, and processing big data using industry-standard tools like Apache Hadoop and Spark

Prepares learners for careers in big data analytics and data science by equipping them with core competencies

Reviews summary

Practical introduction to big data integration

According to learners, this course provides a solid foundation in big data integration and processing fundamentals, particularly focusing on Hadoop and Spark environments. Many students found the hands-on labs and assignments, especially the final project involving MongoDB and Spark, to be highly valuable and practical for applying concepts. While the course is intended for beginners, some learners noted that the initial technical setup, particularly with the Cloudera VM, can be challenging and requires patience. The video lectures are generally clear, although a few topics might benefit from more depth or updated examples. Overall, it's seen as a good starting point for those new to the field, offering a blend of theoretical understanding and practical application.

Good starting point for those new to big data, despite setup.

"As a beginner, I felt this course was accessible and well-structured for learning the basics."

"It's definitely designed for people new to data science, as stated in the description."

"Requires patience if you're new to working with virtual machines and command lines."

"While no prior programming is needed, some comfort with technical environments helps."

Provides a good foundational understanding of big data topics.

"This course gave me a clear overview of big data integration methods."

"I found the explanations of Hadoop and Spark basics easy to follow."

"It's a solid introduction for someone completely new to the subject."

"The lectures provided a good theoretical foundation for the practical parts."

Labs and assignments provide valuable practical application.

"The hands-on labs were the best part; they really helped solidify the concepts."

"I appreciated the practical assignments that allowed me to work with the tools."

"Putting MongoDB and Spark to work in the final project was a great learning experience."

"The practical exercises helped me understand how to apply what I learned in a real scenario."

Some topics could be more in-depth or use updated examples/tools.

"Could use deeper dives into specific techniques or optimization methods."

"Some of the software versions or examples felt slightly outdated."

"A few concepts went by quickly and I had to supplement with external resources."

"I wished for more advanced use cases after the foundational lessons."

Difficulty installing and configuring the required virtual machine.

"The main issue I had was getting the VM set up properly. It was a frustrating process."

"Installing the Virtual Box and Cloudera took a lot of time and troubleshooting."

"The initial setup steps were not as straightforward as they could have been, requiring extra research."

"I spent more time on the VM setup than on some of the course material itself."

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 Big Data Integration and Processing with these activities:

Review database fundamentals

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Reviewing key concepts from relational database management systems will give you a much stronger footing for the later parts of the course.

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Review the main concepts of database management and relational database models
Understand the main database operations and data retrieval techniques

Review Linux Basics

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Refreshes your core competencies in Linux terminal usage, which is used in this course for various operations and demonstrations.

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Review basic commands
Practice file and directory navigation

Organize Course Materials

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Ensures you have a well-structured and easily accessible repository of course materials, enhancing your ability to review and reinforce key concepts throughout the learning journey.

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Create a dedicated folder or notebook for course materials
Organize materials by module or topic
Include notes, summaries, and practice questions

11 other activities

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Organize materials

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Putting course materials in one location before the course begins makes it easier to follow content and reinforcing your understanding of key topics.

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Create a clean folder on your computer for storing materials
Set up a Google Drive or Dropbox folder to serve as a backup
Print materials as needed

Review NoSQL Databases

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Refreshes your understanding of NoSQL databases and their applications, providing a strong foundation for the upcoming modules on data integration and processing.

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Revisit key concepts of NoSQL databases
Review different types of NoSQL databases

Connect with a Data Scientist

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Offers the opportunity to gain valuable insights and guidance from experienced professionals in the field, enhancing your learning experience and career prospects.

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Attend industry events and conferences
Join online communities and forums

Attend a workshop on big data analytics

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Attending a workshop on big data analytics will provide you with insights into the latest trends and best practices in the field, which can complement your learning in this course.

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Research and identify relevant workshops in your area
Register for and attend the workshop

Spark SQL Tutorial

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Enhances your understanding of Spark SQL by providing step-by-step guidance through essential concepts and use cases.

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Follow the official Spark SQL tutorial
Practice writing SQL queries on sample datasets

Build a simple NoSQL database

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Building a simple NoSQL database will reinforce the concepts of NoSQL databases and enhance your understanding when studying them in this course.

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Choose a NoSQL database such as MongoDB or Cassandra
Develop a simple schema for your database
Insert data into your database
Query and retrieve data from your database

Practice data processing with Spark

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Practicing data processing with Spark will enhance your knowledge and skills on data processing using Apache Spark.

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Work through the exercises provided in the Apache Spark documentation
Find additional practice exercises and examples online

Develop a Data Integration Pipeline

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Provides hands-on experience with building and deploying a data integration pipeline, comparable to those used in real-world applications.

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Design the pipeline architecture
Select and configure data sources
Implement data transformation and cleaning
Deploy and monitor the pipeline

Develop a small-scale data integration pipeline

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Creating a small-scale data integration pipeline will solidify the principles of data integration and processing you will learn in this course.

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Identify two data sources that are relevant to each other
Develop a plan for integrating the data
Implement your plan using a data integration tool such as Informatica or Talend
Test your data integration pipeline

Spark MLlib Exercises

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Strengthens your grasp of Spark MLlib through a series of targeted exercises, reinforcing key concepts and algorithms.

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Solve classification problems using Logistic Regression
Implement clustering algorithms like k-means

Big Data Analytics Project

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Provides a comprehensive challenge by applying the concepts learned in the course to a real-world data analytics project, fostering practical implementation skills.

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Define the project scope and objectives
Collect and preprocess relevant data
Develop data analysis and modeling pipelines
Evaluate and interpret results

Career center

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

Data Engineer

Data Engineers are responsible for designing, building, and maintaining the infrastructure that supports data analytics. A Data Engineer may be responsible for collecting, storing, and processing data from a variety of sources, as well as developing and maintaining data pipelines. This course may be useful to a Data Engineer as it provides a comprehensive overview of big data integration and processing. Learners will gain experience with a range of big data tools and technologies, including Hadoop, Spark, and MongoDB.

See salaries and explore the career path for Data Engineer

Data Scientist

Data Scientists use their knowledge of statistics, machine learning, and data mining to extract insights from data. They can work in a variety of industries, including healthcare, finance, and retail. This course may be useful to a Data Scientist as it provides a solid foundation in big data integration and processing. Learners will gain experience with data retrieval, data integration, and big data analytics using Apache Spark.

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Data Analyst

A Data Analyst can work independently to analyze data and draw conclusions from it. They are tasked with gathering and interpreting large amounts of data, applying statistical techniques to uncover patterns and trends, and presenting their findings to stakeholders. This course may be useful to a Data Analyst as it provides a strong foundation in data retrieval and big data processing. Learners will gain experience with data integration tools including Apache Hadoop and Spark, which are essential for success in this role.

See salaries and explore the career path for Data Analyst

Data Architect

A Data Architect designs and builds the infrastructure that supports data analytics. They are responsible for ensuring that data is accessible, reliable, and secure. This course may be useful to a Data Architect as it provides a comprehensive overview of big data integration and processing. Learners will gain experience with a range of big data tools and technologies, including Hadoop, Spark, and MongoDB.

See salaries and explore the career path for Data Architect

Database Administrator

A Database Administrator is responsible for managing and maintaining databases. They ensure that databases are operational and that data is secure. This course may be useful to a Database Administrator as it provides a strong foundation in data retrieval and big data processing. Learners will gain experience with a range of database technologies, including PostgreSQL and MongoDB.

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Machine Learning Engineer

Machine Learning Engineers apply machine learning techniques to solve real-world problems. They work with data scientists to develop and deploy machine learning models. This course may be useful to a Machine Learning Engineer as it provides a strong foundation in big data integration and processing. Learners will gain experience with a range of big data tools and technologies, including Hadoop, Spark, and MongoDB.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians use statistical methods to collect, analyze, and interpret data. They work in a variety of industries, including healthcare, finance, and public policy. This course may be useful to a Statistician as it provides a strong foundation in data retrieval and big data processing. Learners will gain experience with a range of statistical techniques and tools, including R and Python.

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Information Security Analyst

An Information Security Analyst is responsible for protecting an organization's IT infrastructure from security threats. They are responsible for identifying and mitigating vulnerabilities, and for developing and implementing security policies. This course may be useful to an Information Security Analyst as it provides a strong foundation in data retrieval and big data processing. Learners will gain experience with a range of security tools and technologies, including firewalls and intrusion detection systems.

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Information Technology Manager

An Information Technology Manager is responsible for managing an organization's IT infrastructure. They are responsible for ensuring that IT systems are operational and that data is secure. This course may be useful to an Information Technology Manager as it provides a comprehensive overview of big data integration and processing. Learners will gain experience with a range of big data tools and technologies, including Hadoop, Spark, and MongoDB.

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Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical methods to analyze financial data. They work with investment banks and hedge funds to develop trading strategies. This course may be useful to a Quantitative Analyst as it provides a strong foundation in data retrieval and big data processing. Learners will gain experience with a range of financial data analysis tools and techniques, including Python and R.

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Software Engineer

Software Engineers design, develop, and maintain software systems. They work in a variety of industries, including healthcare, finance, and technology. This course may be useful to a Software Engineer as it provides a strong foundation in data integration and processing. Learners will gain experience with a range of software development tools and technologies, including Java and Python.

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Business Analyst

A Business Analyst helps businesses to identify and solve problems. They use data to analyze business processes and recommend solutions. This course may be useful to a Business Analyst as it provides a strong foundation in data retrieval and big data processing. Learners will gain experience with a range of data analysis tools and techniques, including SQL and Python.

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Market Researcher

A Market Researcher conducts research to understand consumer behavior. They use data to identify trends and make recommendations for marketing campaigns. This course may be useful to a Market Researcher as it provides a strong foundation in data retrieval and big data processing. Learners will gain experience with a range of market research tools and techniques, including surveys and focus groups.

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Operations Research Analyst

An Operations Research Analyst uses mathematical models to solve business problems. They work with businesses to improve efficiency and profitability. This course may be useful to an Operations Research Analyst as it provides a strong foundation in data retrieval and big data processing. Learners will gain experience with a range of mathematical modeling techniques, including linear programming and simulation.

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Financial Analyst

A Financial Analyst uses financial data to make investment recommendations. They work with clients to develop investment strategies and manage portfolios. This course may be useful to a Financial Analyst as it provides a strong foundation in data retrieval and big data processing. Learners will gain experience with a range of financial data analysis tools and techniques, including Excel and Python.

See salaries and explore the career path for Financial Analyst