Reasons To Leverage Azure Synapse Analytics – The Next Generation SQL Data Warehouse

Decision support systems have a long tradition in the business world. Companies have been using analytics to get actionable data since the 1960s. The aim is to support managers in strategically managing business processes through data-driven reports, models and forecasts. Through Azure Synapse Analytics, Microsoft offers analytics services that combine the benefits of data warehouses and big data analytics.

Azure Synapse Analytics

The terms MIS (Management Information System), and DSS (Decision Support System) refer to analytical information systems that perform this function but do not distinguish between them. At the same time, BI (Business Intelligence) is a generic term that has been used since the 1990s for business applications and related product marketing.

Today, the data infrastructure for BI decision support systems is usually a central data warehouse. It provides an overview of reference architectures for information systems, the leading providers of data warehouse solutions, and free and open-source options.

Azure Synapse Analytics – The Next Generation Of SQL Data Warehouse

With Azure Synapse Analytics, Microsoft offers the successor to Azure SQL Data Warehouse. With this new service, Microsoft aims to extend its modern data warehouse strategy and enable companies to analyze large data sets more efficiently and quickly.

The new service version is designed to take data warehouse management to the next level and provide more excellent analytical capabilities.

Another benefit of Azure Synapse Analytics is its scalability. External systems can view and analyze almost unlimited amounts of data in real-time. This can be stored in external data warehouses or extensive data systems. Azure analytics can also connect local data centers.

Machine Learning And Advanced Data Protection

Machine learning models can be used in Azure Synapse Analytics. They can be integrated directly into the data warehouse for real-time data analysis, and the Spark engine is integrated into Azure Synapse Analytics.

Microsoft also added privacy features that allow you to analyze individual columns and rows with additional security and permission settings. Dynamic shutdown and persistent data encryption are also possible. Azure Synapse Analytics can also be configured to authenticate with Azure Active Directory.

In addition to data protection, data sharing is also essential. For example, Azure Data Share can be used to share data securely and efficiently between Azure services. Azure Data Share works directly with Azure Synapse Analytics. Data can be transferred from the Azure software user interface. Subscription data sharing is also possible. In this case, for example, Azure Synapse Analytics works with Office 365 and Dynamics 365. Any SaaS service that supports open data initiatives can be integrated.

You Can Also Create SQL Queries

You can send data to Azure Synapse Analytics using SQL so that you can analyze both relational and non-relational data. Microsoft claims that petabytes of data can be interpreted in seconds. Synapse Analytics also works with Power BI and Azure Machine Learning in this context. Power BI features integrate directly with Azure Synapse Analytics, including multiple data sources that can be combined with Power BI. Azure Synapse Analytics is also available with Common Data Services (CDS) and Power BI AI capabilities.

Azure Synapse Analytics supports T-SQL and other languages for analysis or interaction with external systems. For example, Python, Scala, Spark and of course .NET. Azure Synapse Analytics includes Azure Data Factory. Here you can graphically connect data sources and visualize data flows. It’s a graphical ETL tool directly within the Synapse environment.

Data Preparation And Visualization With Azure Synapse Analytics Studio

Microsoft introduced Azure Synapse Analytics Studio, an application that presents data in an engaging way for users. It is a centralized management tool that allows control of almost all known analytics functions in the Azure SQL data warehouse.

For example, you can create dashboards and workspaces to manage and prepare data for analysis directly. Workspaces allow data scientists to collect data streams and view all data without code. For example, Azure Synapse Analytics does not require a direct query to a database or data warehouse to access data. New functionality can be added on-demand or integrated directly into Spark Engine.

This workspace can be used by data scientists, business analysts, database administrators and developers who want to prepare and analyze data. You can import datasets into Power BI and prepare them for end users. You can do everything you need in the graphical user interface of Azure Synapse Analytics Studio. This allows you to quickly and easily analyze all relevant sources through a central interface.

What Can You Expect From Our Azure Data Warehouse Consulting Services?

Azure Synapse Analytics consulting and training are delivered in line with Existb’s approach for other Microsoft products. Customers who primarily use data warehouses as part of their services and have previously used Azure SQL Data Warehouse can now expect significant improvements in Azure Synapse Analytics.

Sum It Up

Medium and large enterprises increasingly use data warehouses. The business intelligence and data warehousing market offers businesses a wide range of promising open-source models and cost-effective solutions. For SMEs in particular, this reduces the financial barriers associated with the old world of big data analytics.

Medium-sized users focus on reporting when deploying BI solutions. Enterprises gain initial value by collecting data at a reasonable cost. If the assessment shows gaps in the database, the next step is to set up data collection using ETL or OLAP tools. The integration of data warehouse architecture and the proper IT infrastructure is complemented by data mining tools that can highlight emerging trends and correlations and provide essential insights for strategic decision-making through further analysis.

Medium-sized businesses considering a data warehouse should ensure that they have an appropriate business intelligence strategy in place from the outset that is compliant with data protection requirements.

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