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Introduction to SQL Big Data & Analytics

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41 Videos
7 Hours

Course Description

Introduction to SQL Big Data & Analytics

The “Introduction to SQL Big Data & Analytics” course is designed to provide participants with foundational knowledge and skills in SQL for big data analysis. This comprehensive program covers essential SQL concepts and techniques used to manage and analyze large datasets, enabling participants to derive valuable insights from big data.

Key Features:

  • Foundations of SQL: Learn the basics of SQL, including querying, filtering, and sorting data. Understand the role of SQL in big data analytics.
  • Big Data Concepts: Gain an understanding of big data technologies and frameworks such as Hadoop and Spark. Learn how SQL integrates with these technologies.
  • Data Storage and Retrieval: Explore how to store and retrieve large datasets efficiently using SQL. Learn about distributed databases and data warehouses.
  • Data Manipulation: Develop skills in inserting, updating, and deleting data within big data environments. Understand how to maintain data integrity and quality.
  • Advanced SQL Techniques: Master advanced SQL techniques, including subqueries, joins, and window functions, to analyze complex datasets.
  • Performance Optimization: Learn techniques for optimizing SQL queries to handle large volumes of data efficiently. Understand indexing, partitioning, and query optimization strategies.
  • Data Visualization: Discover how to visualize big data using SQL and integrate it with visualization tools like Tableau and Power BI for better insights.
  • Real-World Applications: Apply SQL skills to real-world big data scenarios, such as trend analysis, customer segmentation, and predictive analytics.
  • Hands-On Practice: Engage in practical exercises and projects to apply the concepts learned. Work with big data sets to gain hands-on experience.
  • Data Security and Compliance: Understand the importance of data security and compliance in big data environments. Learn best practices for data protection and privacy.
  • Certification: Upon completing the course, participants receive a certification, validating their skills in SQL for big data and analytics.

This course is ideal for data analysts, data scientists, database administrators, and anyone interested in leveraging SQL for big data analysis. By completing this program, participants will be equipped with the knowledge and skills to manage and analyze large datasets effectively, driving data-driven decision-making in their organizations.


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Step-by-Step Courses List

Chapter One:

Module 1: What are Big Data Clusters?

1.1 Introduction
1.2 Linux, PolyBase, and Active Directory
1.3 Scenarios

Chapter Two:

Module 2: Big Data Cluster Architecture

2.1 Introduction
2.2 Docker
2.3 Kubernetes
2.4 Hadoop and Spark
2.5 Components
2.6 Endpoints

Chapter Three:

Module 3: Deployment of Big Data Clusters

3.1 Introduction
3.2 Install Prerequisites
3.3 Deploy Kubernetes
3.4 Deploy BDC
3.5 Monitor and Verify Deployment

Chapter Four:

Module 4: Loading and Querying Data in Big Data Clusters

4.1 Introduction
4.2 HDFS with Curl
4.3 Loading Data with T-SQL
4.4 Virtualizing Data
4.5 Restoring a Database

Chapter Five:

Module 5: Working with Spark in Big Data Clusters

5.1 Introduction
5.2 What is Spark
5.3 Submitting Spark Jobs
5.4 Running Spark Jobs via Notebooks
5.5 Transforming CSV
5.6 Spark-SQL
5.7 Spark to SQL ETL

Chapter Six:

Module 6: Machine Learning on Big Data Clusters

6.1 Introduction
6.2 Machine Learning Services
6.3 Using MLeap
6.4 Using Python
6.5 Using R

Chapter Seven:

Module 7: Create and Consume Big Data Cluster Apps

7.1 Introduction
7.2 Deploying, Running, Consuming, and Monitoring an App
7.3 Python Example – Deploy with azdata and Monitoring
7.4 R Example – Deploy with VS Code and Consume with Postman
7.5 MLeap Example – Create a yaml file
7.6 SSIS Example – Implement scheduled execution of a DB backup

Chapter Eight:

Module 8: Maintenance of Big Data Clusters

8.1 Introduction
8.2 Monitoring
8.3 Managing and Automation
8.4 Course Wrap Up

Original price was: $100.Current price is: $15.

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Frequently Asked Questions

Instructional methods, course requirements, and learning technologies can vary significantly from one online program to the next, but the vast bulk of them use a learning management system (LMS) to deliver lectures and materials, monitor student progress, assess comprehension, and accept student work. LMS providers design these platforms to accommodate a multitude of instructor needs and preferences.

Online education may seem relatively new, but years of research suggests it can be just as effective as traditional coursework, and often more so. According to a U.S. Department of Education analysis of more than 1,000 learning studies, online students tend to outperform classroom-based students across most disciplines and demographics. Another major review published the same year found that online students had the advantage 70 percent of the time, a gap authors projected would only widen as programs and technologies evolve.

All new learning innovations are met with some degree of scrutiny, but skepticism subsides as methods become more mainstream. Such is the case for online learning. Studies indicate employers who are familiar with online degrees tend to view them more favorably, and more employers are acquainted with them than ever before. The majority of colleges now offer online degrees, including most public, not-for-profit, and Ivy League universities. Online learning is also increasingly prevalent in the workplace as more companies invest in web-based employee training and development programs.

The concern that online students cheat more than traditional students is perhaps misplaced. When researchers at Marshall University conducted a study to measure the prevalence of cheating in online and classroom-based courses, they concluded, “Somewhat surprisingly, the results showed higher rates of academic dishonesty in live courses.” The authors suggest the social familiarity of students in a classroom setting may lessen their sense of moral obligation.

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Description

Introduction to SQL Big Data & Analytics

The “Introduction to SQL Big Data & Analytics” course is designed to provide participants with foundational knowledge and skills in SQL for big data analysis. This comprehensive program covers essential SQL concepts and techniques used to manage and analyze large datasets, enabling participants to derive valuable insights from big data.

Key Features:

  • Foundations of SQL: Learn the basics of SQL, including querying, filtering, and sorting data. Understand the role of SQL in big data analytics.
  • Big Data Concepts: Gain an understanding of big data technologies and frameworks such as Hadoop and Spark. Learn how SQL integrates with these technologies.
  • Data Storage and Retrieval: Explore how to store and retrieve large datasets efficiently using SQL. Learn about distributed databases and data warehouses.
  • Data Manipulation: Develop skills in inserting, updating, and deleting data within big data environments. Understand how to maintain data integrity and quality.
  • Advanced SQL Techniques: Master advanced SQL techniques, including subqueries, joins, and window functions, to analyze complex datasets.
  • Performance Optimization: Learn techniques for optimizing SQL queries to handle large volumes of data efficiently. Understand indexing, partitioning, and query optimization strategies.
  • Data Visualization: Discover how to visualize big data using SQL and integrate it with visualization tools like Tableau and Power BI for better insights.
  • Real-World Applications: Apply SQL skills to real-world big data scenarios, such as trend analysis, customer segmentation, and predictive analytics.
  • Hands-On Practice: Engage in practical exercises and projects to apply the concepts learned. Work with big data sets to gain hands-on experience.
  • Data Security and Compliance: Understand the importance of data security and compliance in big data environments. Learn best practices for data protection and privacy.
  • Certification: Upon completing the course, participants receive a certification, validating their skills in SQL for big data and analytics.

This course is ideal for data analysts, data scientists, database administrators, and anyone interested in leveraging SQL for big data analysis. By completing this program, participants will be equipped with the knowledge and skills to manage and analyze large datasets effectively, driving data-driven decision-making in their organizations.


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9 reviews for Introduction to SQL Big Data & Analytics

  1. Emily J –

    This course was the perfect starting point for understanding how SQL plays a role in big data and analytics. Clear explanations and hands-on exercises made learning fun and practical

  2. Mohammed R –

    Great intro for beginners. I now understand the basics of writing SQL queries and how they’re used in data analysis. The section on joins and aggregations was especially useful

  3. Julia T –

    This course helped me connect the dots between SQL and real-world data analytics. A must for anyone looking to enter the field of data science or business intelligence

  4. Carlos M –

    Solid beginner course. I appreciated the walkthroughs, though a few topics like indexing and optimization could have used more depth. Still very helpful overall

  5. Renee L –

    A great combination of theory and practice. The course helped me clean and analyze large datasets with SQL, and it even introduced basic big data concepts like distributed systems

  6. Daniel P –

    Amazing course for beginners! It walks you through SQL basics and smoothly introduces you to how it applies in the big data world. Highly recommended for aspiring data analysts

  7. Aisha K –

    Very helpful course. The examples were easy to follow, and I especially liked the section that showed how SQL integrates with big data tools like Hadoop and Spark

  8. Tom B –

    I’ve taken a few SQL courses, but this one stood out because it linked everything back to analytics. Great for professionals trying to upskill in data

  9. Natalie H –

    Good for getting started, though I would have liked more exercises using real-world datasets. Still a solid foundation for working with large data environments

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