Microsoft SQL Server - Big Data

Course Information

Course Name: Microsoft SQL Server – Big Data
Total Video Hours: 7 Hrs 6 Min
Total Videos: 41
Course Level: Intermediate to Advanced
Delivery Mode: Online, self-paced
Industry Focus: Data Engineering, Big Data Analytics, Enterprise Data Platforms

This Microsoft SQL Server Big Data Online Course delivers detailed instruction on SQL Server 2019 Big Data Clusters, covering architectural concepts, deployment processes, data ingestion, analytics, and operational maintenance. The course is aligned with enterprise-level data engineering practices and is suitable for professionals working with large-scale data systems.

Included in This Course

• 41 professionally recorded video lessons

• 7 hours and 6 minutes of structured technical instruction

• Real-world demonstrations using SQL Server 2019 Big Data Clusters

• Hands-on examples involving Spark, Kubernetes, PolyBase, and HDFS

• Practical guidance for data virtualization and analytics integration

• Machine learning workflows using Python, R, and MLeap

• Big data application deployment and monitoring examples

• Industry-aligned explanations suitable for enterprise environments

Course Outline

Module 1: What are Big Data Clusters?

1. Introduction

2. Linux, PolyBase, and Active Directory

3. Scenarios

Module 2: Big Data Cluster Architecture

1. Introduction

2. Docker

3. Kubernetes

4. Hadoop and Spark

5. Components

6. Endpoints

Module 3: Deployment of Big Data Clusters

1. Introduction

2. Install Prerequisites

3. Deploy Kubernetes

4. Deploy BDC

5. Monitor and Verify Deployment

Module 4: Loading and Querying Data in Big Data Clusters

1. Introduction

2. HDFS with Curl

3. Loading Data with T-SQL

4. Virtualizing Data

5. Restoring a Database

Module 5: Working with Spark in Big Data Clusters

1. Introduction

2. What is Spark

3. Submitting Spark Jobs

4. Running Spark Jobs via Notebooks

5. Transforming CSV

6. Spark-SQL

7. Spark to SQL ETL

Module 6: Machine Learning on Big Data Clusters

1. Introduction

2. Machine Learning Services

3. Using MLeap

4. Using Python

5. Using R

Module 7: Create and Consume Big Data Cluster Apps

1. Introduction

2. Deploying, Running, Consuming, and Monitoring an App

3. Python Example – Deploy with azdata and Monitoring

4. R Example – Deploy with VS Code and Consume with Postman

5. MLeap Example – Create a yaml file

6. SSIS Example – Implement scheduled execution of a DB backup

Module 8: Maintenance of Big Data Clusters

1. Introduction

2. Monitoring

3. Managing and Automation

4. Course Wrap Up

Microsoft SQL Server – Big Data

Enterprise data ecosystems increasingly rely on platforms capable of managing structured, semi-structured, and unstructured data at scale. Microsoft SQL Server – Big Data Online Course addresses this requirement by focusing on SQL Server 2019 Big Data Clusters, a solution that unifies traditional relational databases with big data technologies such as Apache Spark and HDFS. This approach enables organizations to analyze large volumes of data using familiar SQL-based tools while extending analytical capabilities through distributed computing frameworks.

Big Data Clusters introduce a new architectural model within SQL Server, combining SQL Server on Linux, Kubernetes orchestration, Spark analytics, and data virtualization. This course explains how these components operate together to create a single, scalable platform that supports advanced analytics, machine learning, and real-time data processing. Professionals working in data engineering, analytics, or database administration benefit from understanding how these technologies interact in enterprise-grade deployments.

Microsoft SQL Server Big Data training begins with foundational explanations of what Big Data Clusters are and why they were introduced. Traditional data warehouses often struggle with the volume, velocity, and variety of modern data. Big Data Clusters address this challenge by enabling SQL Server to access and query external data sources without requiring data movement. Through PolyBase and data virtualization, SQL queries can seamlessly integrate data stored in HDFS or other distributed systems.

Architecture plays a critical role in Big Data Cluster performance and reliability. This Microsoft SQL Server analytics course provides a detailed examination of Docker containers, Kubernetes orchestration, Hadoop Distributed File System (HDFS), and Apache Spark. Understanding these components is essential for designing systems that are scalable, fault-tolerant, and secure. The course explains how control plane services, data pools, compute pools, and storage pools work together within the cluster environment.

Deployment of Big Data Clusters is another central focus. Enterprise implementations require careful planning, prerequisite installation, and validation. This SQL big data training walks through Kubernetes deployment, configuration of SQL Server Big Data Clusters, and verification steps to ensure a stable environment. Monitoring tools and operational checks are discussed to support ongoing reliability and performance management.

Loading and querying data efficiently remains a core requirement for any big data platform. The Microsoft SQL Server – Big Data Online Course demonstrates multiple methods for ingesting and accessing data, including HDFS interactions using Curl, loading datasets with Transact-SQL, and restoring existing databases into the cluster. These workflows highlight how SQL Server extends its traditional capabilities into distributed data environments without forcing organizations to abandon existing tools or processes.

Data virtualization is a defining feature of SQL Server Big Data Clusters. By exposing external data sources as logical tables, analysts and engineers can query diverse datasets as if they were stored locally. This approach simplifies analytics pipelines, reduces data duplication, and improves governance. The course explains how virtualization supports business intelligence, reporting, and advanced analytics use cases across multiple data platforms.

Apache Spark integration represents a major advancement for SQL Server users. This SQL big data management course explains Spark fundamentals, job submission methods, notebook-based workflows, and data transformation processes. Spark-SQL and ETL pipelines demonstrate how large-scale data processing can be integrated with SQL Server analytics. These capabilities enable advanced data preparation and transformation tasks that are not feasible using traditional relational engines alone.

Machine learning has become a standard component of modern analytics strategies. SQL Server 2019 Big Data Clusters incorporate machine learning services that support Python, R, and MLeap models. This course explains how to implement and operationalize machine learning workflows directly within the Big Data Cluster environment. Predictive analytics, model scoring, and integration with enterprise data sources are addressed through practical examples.

Application development and deployment within Big Data Clusters are also covered. Data-driven applications often require scalable execution environments and reliable monitoring. The course demonstrates how to deploy, run, consume, and monitor applications using Python, R, SSIS, and MLeap. These examples show how analytics solutions can be operationalized and integrated into enterprise workflows.

Ongoing maintenance is essential for long-term success in big data environments. Monitoring, automation, and management strategies are discussed to help professionals maintain performance and reliability. This Microsoft SQL Server Big Data Online Course concludes with operational best practices that support enterprise-scale deployments.

Overall, this course provides structured, in-depth training on SQL Server 2019 Big Data Clusters. It equips data professionals with the technical knowledge required to design, deploy, and manage big data solutions that integrate relational databases with distributed analytics platforms. By focusing on real-world scenarios and enterprise technologies, the course supports informed decision-making and effective implementation of modern data architectures.

FAQs

Who is this Microsoft SQL Server – Big Data Online Course designed for?
Data engineers, data scientists, data architects, and database administrators working with large-scale data environments.

Is prior SQL Server experience required for this course?
Familiarity with SQL Server concepts is recommended to fully understand Big Data Cluster workflows.

Does the course focus on real enterprise use cases?
Yes, the course emphasizes practical deployment, analytics, and operational scenarios.

Are machine learning technologies included in the training?
Machine learning workflows using Python, R, and MLeap are explained in detail.

Does this course cover Apache Spark integration with SQL Server?
Spark fundamentals, job execution, and Spark-SQL integration are core topics.

Is Kubernetes deployment explained in the course?
Kubernetes architecture, deployment, and monitoring are covered as part of Big Data Cluster setup.