In this modern era, every business or organization relies on data. Data has become the crucial aspect for the success of any business and is continuously growing with each passing day. Data can be termed as the heart of every business or organization. This has led to the rise of Data Engineering. Before diving deep into the Microsoft Azure Data Engineering concepts and techniques, it is essential to understand the concept of Data Engineering.
Data Engineering is an approach that involves the effective organization of data to make it accessible for different types of users in different fields such as Marketing, Sales, Business Associates, and many more. In simple terms, you can understand this with an example.
Consider an organization that has a large amount of data spread across various data sources like MySQL Database, ERP (Enterprise Resource Planning) tools, CRM (Customer Relationship Management) tools, etc. Now, to analyze the data from these data sources, it is essential to unify the data into a central location. This central location might be a Data Warehouse or a Database. The entire process that is involved in moving the data from data sources to the Data Warehouse or vice versa comes under the discipline of Data Engineering.
The main goal of Data Engineering is to enable individuals to make data-driven decisions and at the same time provide a uniform and consistent flow of data through each stage of the process. Microsoft Azure Data Engineering makes the process of building the infrastructure for accessing and organizing the data easier with its wide variety of services and tools.
This article will give you a comprehensive guide to Microsoft Azure Data Engineering and how it can be beneficial for organizations and businesses. This article will also provide you the important services and tools provided by Microsoft Azure Data Engineering.
Table of Contents
- Introduction to Microsoft Azure
- Introduction to Microsoft Azure Data Engineering
- Key Concepts Involved in Microsoft Azure Data Engineering
- Key Features of Microsoft Azure Data Engineering
- Services and Tools provided by Microsoft Azure for Data Engineering
- Microsoft Azure Databases
- Microsoft Azure Data Factory
- Microsoft Azure Databricks
- Microsoft Azure Analytic Tools
Introduction to Microsoft Azure
Microsoft Azure is one of the top Cloud Computing Platforms developed by Microsoft in 2008. The core Cloud services that are provided by Microsoft Azure include Storage, Analytics, and Computing Power. Additionally, It provides 3 different types of Cloud Computing services:
- PaaS (Platform as a Service): PaaS (Platform as a Service) is one of the Cloud Computing models where the vendor provides access to its cloud-based environment for building applications on its infrastructure over the internet.
- IaaS (Infrastructure as a Service): IaaS (Infrastructure as a Service) is a Cloud Computing model where the vendor provides access to its Storage, Computing Power, Networking, and Servers over the internet.
- SaaS (Software as a Service): SaaS (Software as a Service) is also a Cloud Computing model where the vendor provides access to its Applications and Softwares over the internet.
Moreover, Microsoft Azure has a wide range of APIs that can be integrated with your applications like Face Recognition, Computer Vision, Form Recognizer, etc. Most of the Fortune 500 companies rely on the services provided by Microsoft Azure to build and deploy new or existing applications. It eases the challenges faced by large-scale businesses. Moreover, it provides flexibility to the businesses to use their preferred tools and technologies for building products or applications.
For more information about Microsoft Azure, you can click here.
Introduction to Microsoft Azure Data Engineering
Data is spread across various data sources like CRM tools, ERP tools, Databases, Third-Party Apps, etc. Data Engineering enables data-driven tasks by providing consistent data flow from a source to a destination. The methodology is implemented in 3 phases:
- Extraction: In this phase, data is extracted from the data sources like Salesforce, Hubspot, Intercom, etc., and moved to a staging area. This staging area is a temporary area where data from multiple sources can be combined, transformed, cleaned, etc.
- Transformation: In this phase, the extracted raw data are transformed, cleaned, and mapped. It is the key step in the ETL process as the data is converted into a usable format and can be used to gain insights.
- Load: It is the last phase of the ETL process where the data is finally loaded to a target Data Warehouse or Database.
This process is also termed a Data Pipeline.
The Data Engineers are responsible for the design, maintenance, and construction of the Data Pipeline. They are also responsible for ensuring the security, storage, and uniform processing of the data. In the further sections, you will get in-depth knowledge on how you can utilize Microsoft Azure for Data Engineering.
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Key Concepts Involved in Microsoft Azure Data Engineering
To understand the concepts of Microsoft Azure Data Engineering, knowing certain key concepts is quite beneficial. Below are the key concepts and terms that are most commonly used in the field of Microsoft Azure Data Engineering:
It is a central repository consisting of large collections of business data that can be used to aid in decision-making for an organization. Some of the examples of Data Warehouse include Microsoft Azure, Google BigQuery, Amazon Redshift, Snowflake, etc.
The full form of ETL is Extract, Transform and Load. It is the key process that is used to replicate the data. In the ETL process, firstly the data is extracted from multiple data sources and put into a staging area. In the staging area, the raw data is transformed, cleaned, and mapped. Finally, the cleaned data is loaded into a target Data Warehouse or a Database. The ETL process is depicted using the figure below:
The Data Engineers need to ensure that there is a proper and consistent flow of data from the source to the destination. This is termed Data Monitoring.
At every stage of the ETL process, the data must be kept safe from leaks and exposure. This is termed Data Security. It is one of the essential requirements in the ETL process.
Deployment is the method to bring data to a platform to analyze reports, logs, and other data in a structured manner to gain valuable insights from it.
Analytics is the process of visualizing the data in the form of Graphs, Bar Charts, Pie Charts, Histograms, etc. This enables businesses to make strategic decisions.
Key Features of Microsoft Azure Data Engineering
Some of the key features of using Microsoft Azure for Data Engineering include:
- Microsoft Azure Data Engineering provides a built-in encryption tool that enhances security.
- Microsoft Azure Data Engineering provides virtually unlimited data storage and is scalable enough for large datasets.
- Microsoft Azure Data Engineering also provides a variety of pricing options including a pay-per-use model as well.
- Microsoft Azure Data Engineering provides a fully managed infrastructure that requires no maintenance from its users.
- Microsoft Azure Data Engineering provides a wide range of services like easy access to other SaaS applications, SQL and NoSQL support, Database Integration, etc.
- Microsoft Azure Data Engineering provides global access to your Cloud data.
- Microsoft Azure Data Engineering also provides easy recovery and backup solutions for your data.
Services and Tools provided by Microsoft Azure for Data Engineering
Microsoft Azure provides a wide array of tools and services that ease the process of organizing and replicating data from a source to a destination. Some of the services and tools provided by Microsoft Azure Data Engineering include:
- Microsoft Azure Databases
- Microsoft Azure Data Factory
- Microsoft Azure Databricks
- Microsoft Azure Analytic Tools
Microsoft Azure Databases
Microsoft Azure provides a wide range of Databases that can be used based on the requirements. Here are some of the popular Microsoft Azure Databases that are widely used:
Microsoft Azure SQL Database
It is a Relational Database service provided by Microsoft Azure and is a fully managed service. It also provides AI (Artificial Intelligence) powered features which makes it more user-friendly.
Microsoft Azure Cosmos Database
This Database is suitable for storing all kinds of Non Relational data. In this case, data can be stored in the form of Key-Value Pairs, Documents, Graphs, etc. Moreover, it stores data in a non-hierarchical manner.
Microsoft Azure Data Lake Storage
Microsoft Azure Data Lake Storage is suitable when you need to store Non-Relational Data in a hierarchical or tree-like structure. It can easily process large datasets.
Microsoft Azure PostgreSQL Database
It is a fully managed Database service provided for PostgreSQL-based applications. It also provides advanced security and AI (Artificial Intelligence) powered performance optimizations. You can also integrate Microsoft Azure Cloud benefits like unified management, elastic scale, etc.
Microsoft Azure Blob
It is also one of the data storage solutions that is suitable for Non-Relational Data. One of the key benefits of using this is that it is massively scalable and provides high-performance computing.
Microsoft Azure Data Factory
Microsoft Azure Data Factory allows easy replication of data from source to destination. For example, if you want to replicate the data from Microsoft Azure Blob to a MySQL Database, you need to use the Microsoft Azure Data Factory. Moreover, Data Transformation can also be enabled using Microsoft Azure Data Factory.
Microsoft Azure Databricks
Microsoft Azure Databricks enables you to get insights from your existing data and also help build AI (Artificial Intelligence) solutions. It also supports numerous frameworks and libraries to build applications like PyTorch, TensorFlow, etc.
Microsoft Azure Analytic Tools
Microsoft Azure provides a wide variety of built-in Analytic Tools. Some of the Analytic Tools include:
Microsoft Azure Stream Analytics
Stream Analytics It allows users to run real-time Analytics on numerous streams of data and it is easy to use. Moreover, this kind of analytical tool is suitable for mission-critical workloads.
Microsoft Azure Synapse Analytics
It also provides various Analytics services like Big Data Analytics, Enterprise Data Analytics, etc. Moreover, it allows you to query data on your terms, using serverless or dedicated resources based on your requirements.
Data Lake Analytics
It is also an on-demand Analytic Tool that follows a pay-per-job model. Moreover, it is instantly scalable and enables you to easily design and run parallel data transformations.
Microsoft Azure Data Engineering has gained popularity because of its wide array of services and tools that can be integrated into it while implementing the Data Pipeline and allowing easy access to data. This article also gave an overview of Microsoft Azure Data Engineering and its key features. You also got to know about the services and tools that are required for Microsoft Azure Data Engineering.
Businesses can use automated platforms like Hevo Data to set this integration and handle the ETL process. It helps you directly transfer data from a source of your choice to a Data Warehouse, Business Intelligence tools, or any other desired destination in a fully automated and secure manner without having to write any code and will provide you a hassle-free experience.
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