New members: Get your first 7 days of ITTutorPro Premium for free! Join for free

Introduction to SQL Big Data & Analytics

Introduction to SQL Big Data & Analytics

Course Description

7 Hours

41 Videos

Introduction to SQL Big Data & Analytics

Dive into the depths of SQL Server with this Microsoft SQL – SQL Big Data course and discover one of its most invaluable tools, SQL Big Data Clusters. Here, you will fully explore data virtualization and lakes in order to build a complete artificial intelligence (AI) and machine learning (ML) platform directly within the SQL Server database engine.

Understanding big data and big data analytics is crucial for any organization aiming to make informed decisions. Our Microsoft SQL Big Data course is designed to equip you with the skills needed to become a proficient big data engineer. With a focus on big data analysis, this course offers a deep dive into big data technologies and big data tools, including big data analytics tools.

Career Growth and Opportunities

The career prospects for big data engineers are promising. As organizations continue to realize the value of big data analytics in decision-making, the need for engineers who can build and maintain big data platforms is likely to increase. Career progression often includes roles like Senior Big Data Engineer, Big Data Architect, and even managerial positions where you could be overseeing a team of engineers or an entire data department.

Skill Development and Certifications

To enhance career prospects, big data engineers often pursue various certifications in big data technologies, big data tools, and big data platforms. Certifications from reputable organizations can provide an edge in the job market and are sometimes essential for advancing to higher-paying positions.

Job Market Trends

The job market for big data engineers is not just limited to the tech industry. Sectors like healthcare, finance, retail, and government are also integrating big data solutions into their operations, widening the scope of opportunities. Remote work has also become more prevalent in the field, offering big data engineers the flexibility to work from anywhere.

The role of a big data engineer is both challenging and rewarding, offering competitive salaries and a wide range of career opportunities. With the ever-increasing importance of big data analytics in today’s world, the prospects for big data engineers look promising for the foreseeable future.

Key Features of Big Data Platforms

Scalability: One of the most critical features, allowing the platform to handle increasing amounts of data effortlessly.
High Availability: Ensures that the data is accessible whenever needed, contributing to better data management and analytics.
Data Integration: Allows for the seamless integration of data from various sources, making it easier to perform big data analysis.
Real-Time Processing: Enables real-time analytics, allowing organizations to make data-driven decisions promptly.
Popular Big Data Tools

Big data platforms often come with a suite of tools designed to help with different tasks, from data collection to analysis. Some of the popular big data tools include:

Hadoop: An open-source framework that allows for the distributed processing of large data sets.
Spark: Known for its in-memory processing capabilities, it’s often used for tasks that require real-time analytics.
Kafka: A streaming platform that can handle real-time data feeds.
Tableau: A data visualization tool that integrates well with various big data platforms.
NoSQL Databases: Such as MongoDB and Cassandra, designed to handle unstructured data.

Course Highlights

Closed Caption

Certificate

Dedicated Tutors

Share on:

Proudly Display Your Achievement

Upon completion of your training, you’ll receive a personalized certificate of completion to help validate to others your new skills.

Course Syllabus

Module 1: What are Big Data Clusters?

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

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

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

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

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

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

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

Module 8: Maintenance of Big Data Clusters

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

From: $14.99 / month

Clear
it-2-logo.png

You Will Get Certification After Completetion This Course.

From: $14.99 / month

Clear

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.

Choosing the right course takes time and careful research no matter how one intends to study. Learning styles, goals, and programs always vary, but students considering online courses must consider technical skills, ability to self-motivate, and other factors specific to the medium. Online course demos and trials can also be helpful.
Our platform is typically designed to be as user-friendly as possible: intuitive controls, clear instructions, and tutorials guide students through new tasks. However, students still need basic computer skills to access and navigate these programs. These skills include: using a keyboard and a mouse; running computer programs; using the Internet; sending and receiving email; using word processing programs; and using forums and other collaborative tools. Most online programs publish such requirements on their websites. If not, an admissions adviser can help.

Description

Introduction to SQL Big Data & Analytics

Dive into the depths of SQL Server with this Microsoft SQL – SQL Big Data course and discover one of its most invaluable tools, SQL Big Data Clusters. Here, you will fully explore data virtualization and lakes in order to build a complete artificial intelligence (AI) and machine learning (ML) platform directly within the SQL Server database engine.

Understanding big data and big data analytics is crucial for any organization aiming to make informed decisions. Our Microsoft SQL Big Data course is designed to equip you with the skills needed to become a proficient big data engineer. With a focus on big data analysis, this course offers a deep dive into big data technologies and big data tools, including big data analytics tools.

Career Growth and Opportunities

The career prospects for big data engineers are promising. As organizations continue to realize the value of big data analytics in decision-making, the need for engineers who can build and maintain big data platforms is likely to increase. Career progression often includes roles like Senior Big Data Engineer, Big Data Architect, and even managerial positions where you could be overseeing a team of engineers or an entire data department.

Skill Development and Certifications

To enhance career prospects, big data engineers often pursue various certifications in big data technologies, big data tools, and big data platforms. Certifications from reputable organizations can provide an edge in the job market and are sometimes essential for advancing to higher-paying positions.

Job Market Trends

The job market for big data engineers is not just limited to the tech industry. Sectors like healthcare, finance, retail, and government are also integrating big data solutions into their operations, widening the scope of opportunities. Remote work has also become more prevalent in the field, offering big data engineers the flexibility to work from anywhere.

The role of a big data engineer is both challenging and rewarding, offering competitive salaries and a wide range of career opportunities. With the ever-increasing importance of big data analytics in today’s world, the prospects for big data engineers look promising for the foreseeable future.

Key Features of Big Data Platforms

Scalability: One of the most critical features, allowing the platform to handle increasing amounts of data effortlessly.
High Availability: Ensures that the data is accessible whenever needed, contributing to better data management and analytics.
Data Integration: Allows for the seamless integration of data from various sources, making it easier to perform big data analysis.
Real-Time Processing: Enables real-time analytics, allowing organizations to make data-driven decisions promptly.
Popular Big Data Tools

Big data platforms often come with a suite of tools designed to help with different tasks, from data collection to analysis. Some of the popular big data tools include:

Hadoop: An open-source framework that allows for the distributed processing of large data sets.
Spark: Known for its in-memory processing capabilities, it’s often used for tasks that require real-time analytics.
Kafka: A streaming platform that can handle real-time data feeds.
Tableau: A data visualization tool that integrates well with various big data platforms.
NoSQL Databases: Such as MongoDB and Cassandra, designed to handle unstructured data.

Reviews

There are no reviews yet.

Be the first to review “Introduction to SQL Big Data & Analytics”

Your email address will not be published. Required fields are marked *

preloader