Azure Synapse Analytics is an advanced data analysis platform developed by Microsoft, designed to address the complex challenges of big data. With Synapse Analytics, organizations can manage and analyze data more efficiently, standardizing formats and centralizing workloads. With its industry-specific models and ability to combine data from different sources, Azure Synapse facilitates the transformation of data into useful information, thus supporting more informed business decisions. In this article we are going to take a closer look at its main features, what are the advantages of its use and the most common use cases, to conclude with a small tutorial to test its ease of use.
Over the years and with the advent of the cloud, the problem of data migration and analysis has become a central issue. Often, data ends up in huge data lakes or scattered across various silos and different applications, and there is no standardization between different formats, making it difficult to combine and analyze data from different sources in the same report.
This means that, within a single organization, there are several users who work with different versions of the truth. Many struggle to mature their strategy, not only in collecting data, but also in implementing analysis, that is, in transforming data into useful information.
Across all sectors, organizations are therefore facing significant challenges in terms of time and resources when it comes to managing big data, and for many, Microsoft Azure Synapse could be the solution they were looking for for their problems.
Azure Synapse Analytics by Microsoft Azure aims to solve some of the biggest problems of big data. It's an analytics service that combines data integration, data warehousing, ETL pipelines, big data analysis, visualizations, and more — all in a single platform. Instead of managing different types of data or workloads with different tools, Synapse offers an all-in-one platform for working with your information.
Curious to know more? Let's take a closer look at it in the next sections.
Azure Synapse Analytics is an analysis service defined by Microsoft as “limitless”, which boasts extensive functionality such as provisioned computing, workload isolation, integration with Power BI, Azure ML and Apache Spark, streaming analysis, hybrid data ingestion, column and row level security, dynamic data masking and much more.
This is an evolution of Azure SQL Data Warehouse (DW) with some significant improvements such as on-demand queries as a service and, with a deeper integration with other technology stacks, it allows users to securely retrieve data from sources such as a data warehouse, a data lake and big data analysis systems, thus accelerating the transition from raw data to business insights.
In addition, the platform allows customers to take advantage of cutting-edge technologies such as Power BI, Azure Machine Learning and the latest findings in the field of artificial intelligence.
In short, Azure Synapse Analytics is a single platform for analyzing all your organization's data without having to copy or move terabytes of information, thus strengthening self-service capabilities. Even business users, with minimal technical knowledge, can recover data through departmental silos without any special effort.
Using the familiar SQL language, the service allows users to query both relational and non-relational data. Data analysis and exploration can be carried out both using serverless on-demand queries for ad hoc analysis and exploration, and using provisioned resources (dedicated SQL pool) for predictable and demanding data warehouse needs.
A serverless SQL pool provides access to external files stored in Azure Storage without requiring that the data be copied or uploaded to another location, using the T-SQL dialect. Synapse workspaces include this service by default, so users can use it as soon as their workspace is created.
With this approach, there's no infrastructure to maintain and there are no costs associated with keeping the services running. The service is priced based on consumption, so the costs are based only on the data processed by the queries. Data budget (TB) limits can be used to control the costs of data used in a day, week, or month.
An enterprise data warehouse can benefit from a dedicated SQL pool. Data is stored in tables with columnar storage, which improves performance and reduces costs. A parallel processing architecture is also used to execute queries.
This functionality is not enabled by default in Azure Synapse Analytics; therefore, you must create a pool and select the relevant performance levels, which can be changed later. The cost of a dedicated pool is determined for now, but it can be controlled by scaling the service up or down when necessary. Pools can also be suspended when not in use.
In addition to its core capabilities, Azure Synapse Analytics also offers the following capabilities:
We have created the Infrastructure & Security team, focused on the Azure cloud, to better respond to the needs of our customers who involve us in technical and strategic decisions. In addition to configuring and managing the tenant, we also take care of:
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Now that we have generally understood its characteristics, it is time to move on to practical advantages. The use of Azure Synapse Analytics by Microsoft Azure as a cloud-based analysis tool, Big Data can offer enormous benefits for your business in the short and long term.
In the list below, we offer you some of the most relevant.
Synapse Analytics brings together the best of Azure data services and other services, ensuring that they work together seamlessly to provide a unified data analysis platform that can meet the needs of your organization. These services include Azure Data Warehouse, Azure Data Lake, Azure Active Directory, Azure Data Factory, Apache Spark, and Microsoft Power BI.
With this platform, it is possible to use a single web-based user interface (UT) to carry out various data activities, such as exploring data, executing experiments, and developing data pipelines that guarantee an uninterrupted flow of data to generate useful business insights.
Synapse Analytics offers Machine Learning (ML) capabilities that can be applied to a variety of purposes. The most common is the application of ML algorithms to facilitate the acquisition and understanding of data. Azure Data Factory can be used to create data pipelines that transform business data into a consumable format for ML and generate insights from that data through reports prepared with Apache Spark or serverless SQL pool.
You can also train ML models using both Apache Spark Pools and Azure Machine Learning Automated ML, and these ML models can then be distributed to generate forecasts within the data warehouse itself.
Azure Synapse Analytics offers a number of security features and complies with nearly 30 industry-leading compliance regulations, such as the International Organization for Standardization (ISO), System and Organization Controls (SOC), and the Health Insurance Portability and Accountability Act (HIPAA), among others. It supports Azure Active Directory (AD), SQL-based authentication, and multi-factor authentication.
In addition, it supports the encryption of data at rest and in transit, as well as the classification of sensitive data. Azure Synapse Analytics also supports row, column, and object level security with dynamic data masking, as well as network-level security with virtual networks and firewalls. This ensures that when your sensitive business data is processed through Synapse Analytics, it will be protected with the highest level of security.
Azure Synapse Analytics integrates directly with Microsoft Power BI, which offers dashboards and visualizations with a robust set of analytical and reporting capabilities. Using Synapse Studio, data analysts can easily analyze data and generate dashboards that provide useful business insights.
Azure Synapse Analytics offers numerous advantages to data engineers, simplifying and accelerating the development and management of data warehousing and analytics solutions. With visual tools and drag-and-drop interfaces, data engineers can design and implement complex workflows without the need to write detailed code, reducing development time and minimizing errors.
By virtue of its functionality, Azure Synapse Analytics can be used in a wide variety of scenarios that require the rapid and precise processing of the data that is produced. Let's see some of the most common and important ones below to get an even clearer idea of the versatility and usefulness of the service in real settings.
One of the most significant applications of Azure Synapse Analytics lies in its ability to centralize data from various sales channels for resellers.
Thanks to the service, resellers can seamlessly integrate data from different sources, eliminating data silos and allowing a complete view of their business operations. In the meantime, the tool can also help clean, process, and review this consolidated data.
By offering a unified approach to data management, Microsoft Azure Synapse Analytics allows resellers to obtain more accurate and actionable insights from data. For example, by analyzing past customer purchases, browsing habits, and preferences, retailers can better understand their target audience.
This helps retailers create tailored services and improve the relevance of marketing efforts and offers. The result is an improved business strategy and customer experience that foster loyalty, encourage repeat purchases, and promote long-term growth for the retail business.
Improving the performance monitoring process is another highly impactful use case for Azure Synapse Analytics. With the ability to provide users with real-time visibility into inventory levels and sales trends, this powerful analytics platform enables the sharing of accurate data that helps manufacturers establish effective collaboration with their customers.
This increased transparency and accuracy of data allows manufacturers to make well-informed decisions regarding production, replenishment and logistics. Meanwhile, customers can benefit from the manufacturer's improved demand forecasts to minimize cases of stock runs out and lost business opportunities.
On the supply chain front, Azure Synapse Analytics provides suppliers with advanced analytics capabilities that allow them to gain deeper insights into the performance of their supply chain. By analyzing critical data points such as delivery times and order fulfillment, manufacturers can identify potential areas for improvement and implement targeted strategies to optimize their supply chain operations.
This optimization of the supply chain allows manufacturers to be more competitive in the market, responding quickly to changing needs and offering superior customer service.
Microsoft Azure Synapse Analytics excels in its ability to assist users in detecting fraud, making it a valuable tool for financial institutions. With its robust set of tools and capabilities, Azure Synapse Analytics allows users to effectively analyze vast volumes of data and apply advanced fraud detection algorithms, resulting in actionable insights.
One of the distinctive features of Azure Synapse Analytics is its support for continuous monitoring of transactional activity on accounts and devices in real time. This real-time monitoring capability allows users to quickly identify any suspicious or fraudulent behavior, allowing them to take immediate mitigation actions.
In this way, financial institutions can minimize the risk of financial losses and safeguard their reputation, while obtaining the assistance necessary to meet regulatory compliance requirements and implement effective governance practices.
Setting up Azure Synapse Analytics is a simple process that can be completed in a few clicks. To demonstrate this, in this section, we offer you a small example with a few simple steps that you can take to experiment by creating a new resource, making it operational in a very short time.
The first step is to navigate the Azure portal and sign in with your Azure account credentials. Once you log in, you will be shown this page:
Let's click on the button + Create new resource located on the left side of the screen. This will open the menu Create a resource. Below the search bar we type synapse and, from the options, we click on Azure Synapse Analytics.
Then let's click on the button Create on the page that opens.
Once we click on Create, this will open the menu where we must specify the details to configure Azure Synapse Analytics. First we will have to select the subscription that we want to use. After that, we will have to choose the resource group in which we want to distribute our service.
If you don't have a resource group, you can create one by clicking Create new. In this tutorial, we specified d4ssynapserg as the name of the resource group, but you can choose any other name for it as long as it's unique.
The next step is to specify the name for your workspace. In this case we have specified d4ssynapsews as a name but, again, you can choose any other name as long as it is unique. As far as the Region is concerned, we have specified here West Europe, but you can safely choose the region closest to you.
Sotto Select Data Lake Storage Gen 2, in the sections Account Name and File system name, provide a unique name in both of them again.
In this particular case we have specified as a name d4ssynapsedatagen for the account name and d4ssynapsefn as the name of the file system.
Let's keep the default option for Assign the Storage Blob Data Contributor role to me on the account box Data Lake Storage Gen2, and click Review + Create.
This will open a page where we will see the message”Validation Succeeded”, review our configurations and go back to modify them, if necessary. You can also see the estimated cost per month in the currency chosen for payment.
Once we are satisfied with the configurations above, we will simply have to click on the button Create to distribute our database. The deployment may take a few minutes and the distribution panel will show the status.
Let's click on Go to resource group to open the next page, where we can view information about the Synapse Analytics resource group we just created, such as the associated workspace and storage account.
Now we can open Synapse Studio by clicking on the Synapse workspace and then selecting Open In the box Open Synapse Studio.
By completing the steps above, Synapse Analytics Studio will open, as shown below. On the left side, you can explore the tabs for date, Develop, Integrate, Monitor and Manage.
The ability to analyze all the data generated by your business processes and to generate useful insights to improve your company's strategic decisions is now fundamental in the contemporary landscape. But with such a large volume of data generated every day, all stored in various disconnected data warehouses and data lakes, actually exploiting your data is easier said than done.
Azure Synapse Analytics is proposed as a solid answer to all these problems, offering a data analysis platform that unifies all the data stored in your company's systems and provides a unique and intuitive user interface, well suited to any data scientist who wants to focus on creating models and insights on data, without having to worry about the infrastructure.
It is easy to set up, easy to use and can generate good models that can be used immediately, with a perfect balance between speed, efficiency and precision. Why not try it, then and see if it is also the answer to your needs?
Azure Synapse Analytics is a comprehensive data analytics service that integrates big data processing, data warehousing, and data integration on a single platform. It allows users to query both relational and non-relational data using serverless or provisioned resources.
Azure Synapse Analytics integrates data from various sources like data lakes and warehouses, offering both serverless on-demand queries and dedicated resources. It simplifies big data analysis by combining multiple services such as Apache Spark and Azure Data Factory.
Azure Synapse Analytics offers centralized data management, enhanced performance with parallel processing, and seamless integration with tools like Power BI and Azure Machine Learning. It also provides advanced security features and cost-efficient data handling with its consumption-based pricing model.
Azure Synapse Analytics integrates with Azure Machine Learning and Apache Spark, enabling users to train and apply machine learning models directly within the platform. This supports advanced analytics like forecasting and predictive modeling.
Azure Synapse Analytics is widely used for trend prediction, omnichannel data integration, supply chain optimization, and fraud detection. Its versatility makes it valuable for industries like retail, finance, and manufacturing.
Azure Synapse Analytics offers robust security with features such as encryption, multi-factor authentication, role-based access control, and compliance with major regulations like HIPAA and ISO. It also supports row-level and object-level security with dynamic data masking.
Azure Synapse Analytics supports multiple programming languages such as T-SQL, Python, Scala, Spark SQL, and .Net, allowing flexibility in data query and manipulation based on user preferences.
Azure Synapse Analytics integrates seamlessly with Power BI, enabling data visualization and dashboard creation directly within Synapse Studio. This helps transform complex datasets into actionable insights.
Azure Synapse Analytics follows a consumption-based pricing model for serverless SQL queries, where users only pay for the data processed. Dedicated resources, like SQL pools, are priced based on performance and can be scaled or paused to control costs.
Setting up Azure Synapse Analytics is straightforward through the Azure portal. Users need to create a resource, configure the resource group, select storage, and deploy the Synapse workspace. Synapse Studio is then used for data management and analytics.
The Infra & Security team focuses on the management and evolution of our customers' Microsoft Azure tenants. Besides configuring and managing these tenants, the team is responsible for creating application deployments through DevOps pipelines. It also monitors and manages all security aspects of the tenants and supports Security Operations Centers (SOC).
