Unlocking the Power of Azure Synapse Analytics


In today's business landscape, it is crucial for companies to stay updated on market changes to respond quickly and effectively. 

Azure Synapse Analytics, developed by Microsoft, is a tool designed to efficiently analyze and process large volumes of data. It combines data warehousing and SQL technologies with Apache Spark and Azure Data Explorer technologies. 

In the upcoming blog, we will delve into Azure Synapse Analytics, exploring its use cases, configuration, and differences compared to other competitors.

Understanding Azure Synapse Analytics

To comprehend Azure Synapse Analytics, we must understand its architecture, its features, and more. This is what we will delve into next.

A. Architecture Overview

The Synapse architecture is based on different services that allow it to adapt to all needs.

  • SQL

    SQL utilizes 2 services, dedicated and serverless.

  • Spark

    Apache Spark is an integrated service for deploying clusters. Spark includes Scala, Python, Java, and Delta.

  • Synapse Pipelines

    This is used for data transformations and integration.

  • Synapse Studio

    It is the one that manages all components, providing security and user management.

B. Key Features

  • Data Warehousing

    It provides massive cloud data storage that scales automatically.

  • Big Data Analytics

    It provides massive cloud data storage that scales automatically.

  • Integration Capabilities

    It can integrate with other services like Azure Studio, Azure Data Lake, and Azure Blob Storage, as well as any others that may be necessary to use.

C. Pricing Models

While you can initially get $200 in free credits with access to 55 services, you will need to switch to a pay-as-you-go plan.

As for the pricing of Azure Synapse Analytics, it will depend on the region and the amount of usage. Here is a table with the levels, discounts, and prices.

Level

(SCU)

% Discount

Price

1

5.000

6%

$4.700

2

10.000

8%

$9.200

3

24.000

11%

$21.360

4

60.000

16%

$50.400

5

150.000

22%

$117.000

6

360.000

28%

$259.200

Setting Up Azure Synapse Analytics

We've understood the architecture and different features, now let's step-by-step create a workspace in Azure Synapse.

A. Creating an Azure Synapse Workspace

  1. 1

    The first requirement before starting to create our workspace is to create an account in Azure, which can be done with the free trial they offer.

  2. 2

    Log in to the Azure portal. In the service browser, choose Azure Synapse Analytics and select it.

  3. 3

    In New Synapse Analytics, if you don't have one, you can create a new one by clicking "Create new" and fill in all the information (Title, region).

  4. 4

    Once done, click on "Go to resource" and load all the data.

And there you have it, your instance in Azure Synapse Analytics is now ready.

B. Data Ingestion

  • Batch Data

    Batch data ingestion is the preferred type of ingestion as it is optimized to provide high performance. Batch data is divided into small batches to enable faster queries. These can be configured in databases or tables.

  • Streaming Data

    For real-time data ingestion, you can use Azure Stream Analytics, which supports sources such as Event Hubs, IoT Hub, and Kafka.

C. Data Preparation

For data preparation for analysis, Azure Data Factory is utilized. This service allows the creation of pipelines and the transformation of data for analysis. 

Azure Databricks and Azure HDInsight are also tools that can be used for transforming and preparing data.

Data Exploration and Analysis

A. SQL Pool in Synapse Analytics

Synapse SQL is a service for analytics and querying large amounts of data. This service provides flexibility to choose between 2 consumption models: dedicated and serverless.

  • Dedicated SQL

    Stores large amounts of data in a relational table stored in columns. This service is cost-effective with high performance and can analyze data at a large scale.

  • Serverless SQL

    Using this service, you don't need to configure a structure. It follows a "pay-as-you-go" service model, meaning you pay for processed data and executed queries.

B. Azure Synapse Studio

It provides services for data preparation, big data, data exploration, and AI. It allows users to explore data centers, perform, and execute analytical processes.

C. Querying Data

  • Adatis

    Offers specialized services in big data analytics and data consulting.

  • Cognizant

    Brings its consulting skills and expertise to understand the cloud, so you can make the most of Azure services.

D. Data Visualization

For data visualization, several built-in options can be utilized, such as Synapse notebook charts where you can view data without the need for code, popular open-source libraries (Python), Power BI, that allows you to share serverless tables and databases and create analytical solutions.

Advanced Analytics and Machine Learning

A. Integration with Azure Machine Learning

With Azure Machine Learning, you can automatically establish a model based on specific metrics. It is an automated function, and you only need to point to the workspace without entering any credentials.

B. Predictive Analytics

For predictive analytics, you can use Azure Machine Learning logs without the need for data movement. With ML logs, you can generate millions of predictions quickly and securely without losing data.

C. Using Notebooks

The use of Synapse notebooks allows you to experiment with ideas, visualize results, and draw conclusions without the need for configuration. 

Data Integration

Data integration is one of the most important points to learn. For Azure Synapse, we use Azure Data Factory.

A. Azure Data Factory

With the Azure Data Factory Integration Service, you can integrate and manage data without a server. Additionally, you can build both code-free and custom code-driven ETL and ELT processes.

B. Data Flows

Allows data transformation without the need for code. Data flows are created in Synapse Studio and leverage Apache Spark clusters for horizontal scalability.

C. Data Orchestration

For efficient data orchestration, you can use compatible services, either individually or combined, to achieve the necessary outcomes. You can use Azure Data Factory or SQL Server Integration Services (SSIS).

Security and Compliance

A. Data Encryption

Double keys or dual encryption can be generated to manage and control access to workspaces, and data can be encrypted both at rest and in transit.

B. Authentication and Authorization

For user authorization, you can use:

  • Microsoft Enter Authorization

  • SQL authorization

With these tools, you can enable and disable access for different users to resources during and after the creation of a workspace.

C. Compliance Standards

Ensure compliance with all regulations when creating a workspace, including compliance domains and security controls such as CMMC Level 3, FedRAMP Moderate, FedRAMP High.

Monitoring and Optimization

A. Performance Monitoring

With the features provided by Synapse Analytics, you can obtain metrics that help you assess performance results and predict potential errors. The results are presented both numerically and graphically for better understanding.

B. Query Performance Tuning

Azure Synapse handles query history analysis in four ways, namely:

  • Query Store

  • DMV (Dynamic Management View)

  • Azure Log Analytics

  • Azure Data Explorer

These tools are helpful in obtaining a detailed query history for performance study and query planning.

C. Cost Optimization

To optimize costs, you can consistently study all utilized resources and assess which ones can be optimized and improved. Additionally, you can scale 

Real-world Use Cases

A. Enterprise Data Warehousing

Azure Synapse Analytics is a fundamental service for businesses, as it provides storage and analysis of large data loads, enabling quick responses to market changes and making business outcomes more effective.

B. Business Intelligence

With the tools offered by Synapse for business intelligence, such as Power BI, you can intelligently transform data for easier understanding, aiding in studying and making decisions based on these results quickly.

C. IoT Analytics

With IoT analytics, we can analyze Internet of Things sensors to assess analytical and operational workloads, such as user activities, workflows, and real-time operations. 

Azure Synapse Analytics vs. Competitors

A. Comparison with Amazon Redshift

  1. 1

    Azure Synapse contains 95 connectors for data integration, while AWS Redshift does not provide connectors; however, it offers integration services. 

  2. 2

    Azure Synapse provides granular permissions to schemas and tables, whereas Redshift offers permissions to entire tables. 

  3. 3

    Redshift lacks built-in analytics capabilities but can integrate with other Amazon tools to generate advanced data analytics. In contrast, Azure Synapse serves as a workspace for data analysis, preparation, and big data processing.

B. Google BigQuery vs. Azure Synapse Analytics

  1. 1

    Google BigQuery takes a serverless approach, while Azure Synapse Analytics generally requires one. 

  2. 2

    BigQuery has independent compute scaling, and Azure Synapse requires a manager to scale. 

  3. 3

    Both can scale up and down effectively as needed. 

  4. 4

    Both comply with HIPAA, PCI DSS, SOC 1 Type II, and Type II regulations. 

C. Strengths and Weaknesses

1. Strengths

  • Azure Synapse Analytics offers unlimited scalability for handling large volumes of data.

  • It deeply integrates with the Azure ecosystem and its services.

  • Your data is guaranteed security through row-level and column-level security features.

2. Weaknesses

  • It is not user-friendly for beginners.

  • The absence of a server limits the use of the latest Azure services.

Future Trends and Innovations

A. Roadmap

We can expect great things for the future of Azure Synapse. This is a service that will continue to impress us with its updates, and as technology progresses, it will provide more services and benefits.

B. Integration with Azure Services

We can anticipate integration with more Azure services to enhance services and user experience.

C. AI and ML Advancements

Considering the current advances in the integration of AI and ML, there is a promising future and innovation in terms of services. This promises increased efficiency and speed in automated processes, lower costs, and even faster solutions and implementations.

Migration to Azure Synapse Analytics

The Synapse migration process is of great importance because it must be done correctly to avoid data loss in the process.

A. Data Migration Strategies

  1. 1

    You should have a well-constructed plan for migration to ensure compatibility from all possible points.

  2. 2

    Ensure that tables, schema, and data codes are fully compatible with Azure Synapse Analytics to avoid any data loss.

  3. 3

    Create an inventory of all the data you want to migrate.

B. Challenges and Solutions

  • Compatibility

    When it comes to data migration, the compatibility of the data can pose a challenge; however, the solution lies in creating a plan and conducting an exact review of each piece to modify files and make them compatible.

  • Alignment

    If the data is non-relational in origin, you'll need to transform it into rows and columns for alignment with tables and load the data correctly.

Best Practices

A. Design Considerations

You should always assess the environment and solution you need to configure. You can develop different environments and practice solutions before applying them, allowing you to evaluate and design the working environment accurately.

B. Security Best Practices

  • Always implement access and control services to ensure privacy by controlling who can access the data.

  • Utilize the security methods offered by Azure Synapse Analytics, automating threat detection, real-time data encryption, and backup generation.

C. Performance Optimization Tips

  • Use optimization services provided by Azure Synapse Analytics to monitor potential bottlenecks and identify areas for improvement.

  • Maintain monitoring and study the behavior of your analytics, allowing you to create solutions, adjust your approach, and achieve better performance.

Training and Certification

If you want to become a professional in Azure Synapse Analytics, you must prepare adequately to obtain your certification.

A. Azure Synapse Analytics Certification Paths

Microsoft Learn | Exam DP-203: Data Engineering on Microsoft Azure

This exam costs $165 (varies by country) and assesses data analysis, data lake architecture, and requires individuals to have solid knowledge of SQL, Python, and Scala.

B. Training Resources

Microsoft Learn | Introduction to Azure Synapse Analytics

With this course, you will gain an introduction to what Azure Synapse Analytics is. It has a duration of 1 hour and 22 minutes, divided into 7 units.

Pluralsight | Implementing a Cloud Data Warehouse in Microsoft Azure Synapse Analytics

Through this course, you will learn how Azure Synapse Analytics operates, including the creation of a SQL data warehouse, data loading, and working with data.

User Community and Support

A. Online Forums and Communities

Microsoft Community Hubs | Azure Synapse Analytics

Here, you can start a new discussion with others to share and address issues.

Microsoft Azure | Forum

Engage with fellow Azure Synapse Analytics professionals, sharing ideas and insights.

B. Microsoft Support

Azure technical support | Azure Microsoft

Microsoft Azure provides various technical support plans tailored to each company's needs. All plans offer a quick response time to help you resolve your issues.

 Case for Azure Synapse Analytics

Azure Synapse Analytics offers significant benefits, and here we will understand some of them.

A. Benefits for Businesses

The major benefit for businesses is the handling and analysis of large amounts of data, enabling faster and more effective results, providing increased productivity in less time and cost.

B. Scalability and Flexibility

The automatic scalability and flexibility provided by Azure Synapse Analytics allow for quick scaling, adapting to market changes day by day, and automatically adjusting storage capacity up or down.

C. Return on Investment

To calculate the return on investment, you can use the following formula based on your company's costs:

ROI=[(Revenue- Investment)/ Revenue] * 100

Generally, the use of these services is cost-effective and highly efficient, which is why companies employ them today.

Conclusion

Azure Synapse Analytics offers significant benefits to businesses, thanks to its architecture, scalability, and cost-effectiveness in processing, storing, and analyzing large-scale data.

The speed and effectiveness provided by Azure Synapse Analytics ensure a promising future for professionals specializing in data analytics and the use of these tools.

FAQ

What does Azure Synapse analytics do?

It stores, ingests, and analyzes microdata from businesses.

Is Azure Synapse analytics an ETL tool?

No, it has ETL functions but is not an ETL tool.

What programming language is used in Azure Synapse analytics?

T-SQL, Python, Scala, .NET, Java, R, Power BI, Azure Data Studio, and Visual Studio.

When to choose Azure Synapse Analytics?

Azure Synapse Analytics is generally chosen when companies handle a large amount of data, need to improve and enhance efficiency, and require large-scale data analytics.

How does Azure Synapse Analytics store data?

Through Azure Data Lake Storage.

What is Azure Synapse Analytics vs Databricks?

Azure Synapse analyzes and integrates data at scale in a unified platform, while Databricks processes, analyzes, stores, and transforms large amounts of data to create automated models.

About the author

Youssef

Youssef is a Senior Cloud Consultant & Founder of ITCertificate.org

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