Beginners Guide to Azure Synapse Analytics for Data Engineers

Explore Azure Synapse Analytics to Understand How Its a Game Changer in the Data Analytics Space| ProjectPro

Beginners Guide to Azure Synapse Analytics for Data Engineers
 |  BY Daivi

Looking for a unified interface for all your machine learning and big data tasks? Well, Azure Synapse Analytics is your answer! This beginner's guide will give you a detailed overview of Azure Synapse Analytics and its architecture to help you build enterprise-grade data pipelines for your next data analytics project.


Building Data Pipelines in Azure with Azure Synapse Analytics

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What is Azure Synapse Analytics?

Microsoft's Azure Synapse Analytics (formerly SQL Data Warehouse) is a cloud data warehouse that combines data integration, data exploration, enterprise data warehousing, and big data analytics to offer a unified workspace for creating end-to-end analytics solutions. It is an enhanced version of the Azure SQL data warehouse encompassing additional workflow stages and allows users to generate reports and visualizations.

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It supports various programming languages, including SQL, Python,.NET, Java, Scala, and R, making it highly suitable for diverse analysis workloads and engineering profiles. It allows you to explore data using provisioned resources or serverless on-demand processing. Also, the Synapse Analytics Studio has everything that data teams need, making it easier to combine artificial intelligence, machine learning, IoT (internet of things), smart apps, or business intelligence on one unified platform.

Why Use Azure Synapse Analytics For Big Data Analytics Projects?

Azure Synapse Analytics is a one-stop data analytics solution that enables self-service data sourcing, reporting, and analytics. In a typical Azure data pipeline, data engineers can work with various tools (such as ADF, Azure Data Explorer, Azure Databricks, Azure SQL, Azure Analysis Services, and Power BI). Synapse Analytics enables data engineers to use their data much more effectively, productively, swiftly, and securely by integrating knowledge from any data source, data warehouse, or big data analytics platform. It combines the existing features of Azure SQL Data Warehouse with the capability to run Spark and SQL in clustered and serverless form factors, thus minimizing the gap between data science and data engineering workloads. Data scientists employ various technologies that are unknown to data engineers. In contrast, data engineers typically use multiple tools to manipulate and shape data into a format that can enable data science applications. Azure Synapse Analytics helps eliminate team silos by offering a unified analytics experience by supporting data engineering and data science tasks on one platform.

Benefits of using Azure Synapse Analytics For Data Engineering Projects

Database administrators, data engineers, and data scientists also can leverage Azure Synapse's feature to integrate data and insights into business processes and logical business intelligence to draw insights from large amounts of data. The seamless integration of Synapse Analytics with the rest of the Azure platform enables superior security, faster value discovery testing, and efficient scaling across a business process using a single tool.

Here are five reasons to consider Synapse for your next data engineering or analytics project.

With a faster approach, Synapse extracts insights from the data present in data warehouse and big data analytics systems. Using a basic SQL query, data engineers can combine relational and non-relational data in the data lake. With advanced workload partitioning, workload management, and truly limitless concurrency, optimizing the performance of all queries for crucial tasks is easy.

Synapse Analytics provides an integrated workspace for tasks involving data management, warehousing, big data, and artificial intelligence. Data engineers can use a code-free visual environment to manage data pipelines. Data scientists can generate POCs, and business analysts can leverage Power BI to build dashboards using the same analytics solution.

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It significantly broadens the range of insights you can glean from your data and applies machine learning models to your systems. This limitless analytics service integrates Azure machine learning and Power BI, which enables data engineers to speed up the development of their BI and machine learning projects. This service allows them to easily apply intelligence to your crucial data, including that from Office 365, Dynamics 365, and SaaS services that support the Open Data Initiative.

Azure Synapse comes with reliable and secure features such as Threat detection and active data encryption. Organizations can protect the confidentiality and security of the data through native row-level and column-level security for granular access control. The dynamic data masking and Azure Active Directory integration feature instantly secure crucial data.

Azure Synapse Analytics Architecture - A Deep Dive

The architecture of Azure Synapse mainly comprises four components:

  • Synapse SQL: Dedicated SQL pool and Serverless SQL pool 

  • Apache Spark

  • Azure Data Lake Storage Gen2

  • Synapse Analytics Studio

Azure Synapse Analytics Architecture

Source: learn.microsoft.com

In addition to enabling data warehousing and virtualization tasks, Synapse SQL modifies T-SQL to accommodate streaming and machine learning instances. This T-SQL query engine offers two types of consumption models: either serverless or dedicated.

Setting up dedicated SQL pools allows you to save processing power for data stored in SQL tables for reliable performance and expense. There can exist any number of dedicated SQL pools in a workspace, and they are useful for dedicated models in enterprise data warehousing.

The serverless SQL pool is always available for unexpected or spontaneous operations.  Every workspace contains a serverless SQL pool, which is useful for serverless models.

Apache Spark is the most widely used open-source big data platform for data engineering, ETL, data preparation, and machine learning. The Azure Synapse Analytics workspace uses serverless Apache Spark pools, allowing it to leverage Spark analytics. This results from the seamless integration of Apache Spark in Azure Synapse. It has the following components:

  • Apache Spark for Synapse

  • Apache Spark pool

  • Spark application

  • Spark session

  • Notebook

  • Spark job definition

The Apache Spark component supports machine learning models for Apache Spark 3.1, featuring built-in support for Linux Foundation Delta Lake and SparkML algorithms and AzureML integration. It provides a streamlined resource model that minimizes the load of cluster management. Additionally, it has built-in support for.NET for Spark, allowing you to leverage your C# skills and existing.NET code inside a Spark application.

Synapse leverages Azure Data Lake Storage Gen2 (ADLS Gen2) as a cutting-edge data warehouse and storage solution to facilitate large-volume data analytics. The file-level security, scalability, and file system semantics of ADLS Gen1 combine with the layered storage, disaster recovery, and high availability of Azure Blob Storage in ADLS Gen2. SQL and Spark also can easily read and analyze Parquet, CSV, TSV, and JSON files kept in the data lake due to ADLS Gen2. Additionally, ADLS Gen2 makes importing data quickly and flexibly between SQL and Spark databases easier.

The Synapse Analytics Studio is a secure collaborative interface useful for cloud-based business analytics. It also has an integrated ADLS Gen2 account and file system for temporary data storage. Due to Synapse Analytics Studio, businesses can design solutions, maintain them, and secure them using just one user interface. It carries out crucial tasks such as data exploration, preparation, orchestration, and visualization. Additionally, the Synapse Analytics Studio keeps track of users, usage, and resources for SQL, Spark, and Data Explorer. It leverages role-based access management to make access to analytics resources easier. Additionally, you can interact with enterprise CI/CD processes and create SQL, T-SQL queries, Spark, or KQL code using the Synapse Analytics Studio.

With Synapse Analytics Studio, you can access a single interface for exploring storage accounts, data lakes, and databases. The experience of browsing your resources will be similar for people who have utilized Azure Data Explorer or Azure Data Studio. Synapse Analytics Studio has several excellent features that simplify finding data in storage accounts or data lakes.

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When Should You Use Azure Synapse Analytics

By combining insights from various data sources, warehouses, and analytics solutions, Synapse Analytics helps businesses across industries utilize their data much more safely, reliably, and efficiently. Here are a few Azure Synapse Analytics use cases that you must explore -

Applications of Azure Synapse Analytics

Azure Synapse ensures data security while maintaining a competitive edge by applying a modern approach to handling big data, data warehousing, generating individualized customer experiences, and setting up robust compliance and governance mechanisms to protect user data. It integrates multiple data sources to provide a thorough overview of user attributes and trends, allowing it to create relevant offerings and provide top-notch experiences. Additionally, it strengthens client trust by continuously monitoring transactional behavior across accounts and devices. This allows it to send out immediate notice in the event of a potential threat and easily identify fraud.

There are many challenges in the healthcare industry, such as a lack of professionals, legal constraints, and evolving patient preferences. Synapse helps provide personalized care, protect patient data, and empower caregivers. It gives patients quick access to the medical data they need to get the proper care quickly. Combining data from several health IT systems streamlines regular tasks and frees up your care providers to focus on raising the standard of patient care. Additionally, it helps associate symptoms with diseases and recommends the best treatment options by analyzing patient health data.

By combining data from several sources and gaining real-time business insights, Azure Synapse Analytics allows you to understand your customers better and create a sustainable supply chain. By establishing a seamless customer journey, Synapse enables customers to migrate between various sales channels, platforms, and devices while still having a standard shopping experience. Additionally, it enables businesses to collect, clean, process, and analyze customer data to get useful insights for providing relevant and customized products. It enables designing customer journeys and experiences by combining various consumer data sources and improving them in real time.

Azure Synapse Analytics enables precise equipment failure prediction to lower maintenance costs, prevent excessive downtime, and increase production efficiency. Additionally, it connects various data sources from all supply chain points to provide a 360-degree view of corporate activities.

Azure Synapse Analytics vs. Azure Data Factory

Comparison between Azure Synapse Analytics vs. Azure Data Factory

Features

Azure Synapse Analytics

Azure Data Factory

  1. Purpose

Azure Synapse Analytics allows businesses to analyze, orchestrate, and visualize data using a unified platform.

Businesses have access to codeless data integration options using Azure Data Factory. With the help of Data Factory's cloud-based ETL services, businesses can design data-driven workflows.

  1. Data flow monitoring for Spark jobs

Synapse Analytics does not support monitoring Spark jobs for data flow. You can use Synapse Spark pools for this purpose.

On the other hand, Data Factory supports monitoring Spark jobs for data flow.

  1. Integration Runtime

Azure Synapse Analytics uses SSIS and SSIS Integration Runtime but does not support Cross-region Integration Runtime (Data Flows). Also, it does not support Integration Runtime sharing.

Azure Data Factory supports Cross-region Integration Runtime and uses SSIS and SSIS Integration Runtime. It also supports Integration Runtime sharing between different data factories.

  1. Solution Templates

It offers solution templates in the Synapse Workspace Knowledge Center.

It offers solution templates in the Azure Data Factory Template Gallery.

 

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Azure Synapse Analytics vs. Databricks

Comparison between Azure Synapse Analytics vs. Azure Databricks

Features

Azure Synapse Analytics

Azure Databricks

  1. Architecture

Storage, processing, and visualization are the three layers that form the Synapse architecture. While Power BI is useful for the visualization layer, Azure Data Lake Storage is useful for storage. Additionally, it contains a Spark engine for Big Data Processing and Business Intelligence applications, as well as a standard SQL engine.

Databricks architecture, on the other hand, is not merely a Data Warehouse. For metadata management and data governance, it works with a LakeHouse infrastructure that integrates the main components of any Data Lake and Data Warehouse.

  1. Smart Notebooks

Synapse allows for co-authoring of notebooks, but only with the restriction that one person must save the notebook before the other person can see the changes. It lacks automatic version control.

Along with automated version control, Databricks Notebooks also offers real-time co-authoring.

  1. Support for Apache Spark

Synapse's Spark implementation is open-source, with built-in support for .NET apps.

Databricks' enhanced Apache Spark support enables users to pick GPU-enabled clusters that process data more quickly and can process more data concurrently.

  1. Developer Experience

Developers can only access the Spark environment through Synapse Studio; no other local IDEs are supported (Integrated Development Environment). Git integration with Synapse Studio Notebooks is also missing.

On the other hand, Databricks improves the developer experience with Databricks UI and Databricks Connect, which allows remote connections via Pycharm or Visual Studio within Databricks.

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Best Resources to Learn Azure Synapse Analytics

The following resources will help you on your path to understanding Azure Synapse Analytics:

There’s no better way to learn about any big data tool or service than by working on a practical project leveraging it. So, here’s are some exciting projects on Azure Synapse that will give you a clear understanding of the service and its fundamental concepts.Check out these exciting project ideas to master big data analytics using Azure Synapse Analytics -

1. Building Data Pipelines in Azure with Azure Synapse Analytics

This Microsoft Azure Data Engineering Project will teach you how to create a data pipeline by leveraging Azure Synapse Analytics, Azure Storage, and an Azure Synapse SQL pool to execute data analysis on the 2021 Olympics dataset.

Tech Stack:

Language: SQL

Services: Azure Synapse Analytics, Azure Storage, Azure Synapse SQL Pool, Power BI

Dataset:

This project involves working with the 2021 Olympics dataset. The dataset contains information on more than 11,000 athletes representing 743 Teams and competing in 47 sports at the Tokyo Olympics in 2021. This dataset contains data on the teams, athletes, coaches, and entries that participated, subdivided by gender.

After completing this project, you will learn more about Azure Synapse Analytics. You will discover the differences between the Serverless pool, the Spark pool, and the Dedicated SQL pool. The first step in this project is to create an azure storage account and import data files into a container. Next, build an azure synapse analytics workspace and an SQL pool. Create a data pipeline to feed data into Power BI from SQL pool tables once you load it into Azure storage. The final step is creating the dashboard in Power BI and publishing it to an Azure Synapse workspace.

Source Code: Building Data Pipelines in Azure with Azure Synapse Analytics

2. Spotify Dashboard using Azure Synapse Analytics

This Azure Synapse Analytics project entails creating a Power BI dashboard to visualize the data from the Spotify dataset. Start the project using an Azure Synapse notebook to extract data from the Spotify API. Alternatively, you can extract the data without a notebook by using the Azure Synpase pipeline. The next step is to re-transform the data using Synapse notebook and load it from Synapse notebook into Azure Data Lake. After that, you will use Synapse notebook to analyze the data before connecting it from the data lake to Power BI and creating the dashboard.

3. Geospatial Analysis using Azure Synapse Analytics

This project involves performing end-to-end geospatial analysis using Azure Synapse workflow and the Custom Vision Model as a sample AI model. For object detection or other purposes, you are free to utilize any other AI model with the same or different specifications as those specified by the AI model to test against this approach. You will collect spaceborne data from data sources, including Airbus, NAIP/USDA (through the Planetary Computer API), and Maxar, and then ingest it into Azure Data Lake Storage. To connect to these sources and replicate the data into Data Lake Storage, Synapse Analytics offers a variety of pipelines and activities, including Web activity, Data flow activity, and Custom activities.

4. Azure Synapse Content Recommendations Solution Accelerator

This accelerator presents a simple method for generating personalized content recommendations based on user behavior. Companies from all domains publish content and collect user activity information. Personalized suggestions can help users find new content, reduce data overload, and have a better overall experience. In this project, you can customize recommendations based on each user's history and how closely other users' spending patterns match theirs by analyzing user behavior and content details. The AI model receives a single user and various content pieces as input to generate a click probability score for each content item. Use the MIND: Microsoft News Dataset has around 15 million impression logs from 1 million users and about 160k English news stories.

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If you are a beginner-level data engineer, you must explore these textbooks that will enable you to grasp the concepts of Azure SQL Synapse Analytics.

  1. Limitless Analytics with Azure Synapse

This book will give you a beginner-level introduction to using Azure Synapse Analytics. You will discover how Azure Synapse works and get detailed guidance on data ingestion, securing, and monitoring. You will learn how to use machine learning, real-time streaming, and analyze data to extract precise, detailed insights from your data. This book will also help you learn how to create complete analytics solutions, including data warehousing, preparation, and management tasks.

  1. Azure Synapse Analytics Cookbook

This book offers the key to making the most of Microsoft Azure Synapse, an enterprise-grade, cloud-based data warehousing solution. This book is a fantastic option to start with if you seek the skills to create a powerful business analytical platform. You will also be able to combine different services with Synapse to make it a reliable solution for all your data needs by using the step-by-step instructions and supporting theory given in this book. If you are new to Azure Synapse, this book will help you resolve all the questions you might face while working with the platform.

  1. Learning Azure Synapse Analytics

This popular textbook provides a detailed overview of the Azure Synapse Analytics platform, with an analysis of each service inside the platform. You will learn about each service separately and how to combine them to create a robust end-to-end platform for performing next-generation data analytics. With the help of this Synapse Analytics book, you will learn how to create and manage your solutions and resources and also understand the basics of Azure Synapse Analytics. By the time you finish, you will know how Azure Synapse Analytics fits within a certain data architecture and be able to create dynamic integration and orchestration pipelines.

Here are a few informative Youtube videos/tutorials that will enable you to gain an in-depth knowledge of Azure Synapse Analytics.

  1. Why You Should Look At Azure Synapse Analytics

Why you should look at Azure Synapse Analytics!

  1. What is Azure Synapse Analytics

What is Azure Synapse Analytics? Generally Available Today.

  1. Azure Synapse (Full Course)

Azure Synapse [Full Course]

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FAQs on Azure Synapse Analytics

The three components of Azure synapse analytics are Azure Synapse Studio, Synapse SQL Pools, and Apache Spark.

Yes, Azure Synapse is an ETL tool. Azure Synapse Analytics offers affordable, nearly limitless computing capabilities to quickly load, process, and convert all the data when running analytics queries.

Azure Synapse is used for integrating various big data platforms and services. It enables you to carry out data exploration using T-SQL queries. Additionally, it incorporates PowerBI to facilitate and improve business decision-making.

 

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About the Author

Daivi

Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill

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