Microsoft is constantly introducing new products. Azure Synapse Analytics has been available since the end of 2020. In this blog, we’ll help you understand what the Azure Synapse Analytics is and for whom the tool worth using.
What is Azure Synapse Analytics?
Azure Synapse Analytics is a solution developed by Microsoft for integrating, analyzing, transforming and storing large amounts of data. When you create an Azure Synapse Analytics product in Azure, you create a Synapse Workforce and storage.
Synapse Studio is available through the Synapse Workspace website and is configured, analyzed and deployed through a web browser.
Microsoft offers a wide range of capabilities in Azure Synapse Analytics cloud. In this article we want to introduce Azure Synapse and show you who should use it.
Azure Synapse is Microsoft’s cloud-based analytics service that allows you to integrate, combine and analyze data from any source using different methods to draw conclusions.
Synapse Analytics allows you to extract data from multiple data sources by aligning pipelines. Pipelines are designed for integration and can be created without programming. Sequential operations are used to integrate data from different sources and map it into some kind of data stream. In this way, data can be queried locally and in the cloud. In particular, the connection to a high frequency data streaming and the processing of input data can be done with different priorities.
SQL pools can be used both to store data and to create data lakes. Databases have the advantage of storing data in native formats that can be used by different technologies. Especially for very large data sets, traditional SQL methods may reach their limits.
In addition to SQL technologies, open source software’s and tools such as Apache Spark can be used for data analysis. Microsoft is therefore opening up its technologies and providing powerful big data processing and machine learning capabilities such as Spark.
Who Should Use Azure Synapse Analytics?
In some cases the benefits are obvious,
- If you need to process large amounts of data for analysis, you should take a close look at Azure Synapse. When it comes to “parallel processing”, processing and searching is extremely efficient. If you are dealing with “big data”, it comes quickly. This volume exceeds normal system requirements, for example for enterprise applications.
- One of the main advantages of Azure Synapse Analytics is its ability to handle different data formats and source systems. Of course, you can also aggregate and process this data in a local data warehouse. But Azure Synapse, with its integrated pipelining and storage capabilities, offers this solution out of the box, without programming. This doesn’t mean that the work is completely eliminated, but the data flow characteristics of the pipeline no longer require specialized programming skills for different applications.
- If some applications or data warehouses already exist in Azure, it makes sense to port analytics to Azure as well. A similar data warehouse can be deployed in Azure Synapse Analytics.
- Azure Synapse offers advanced machine learning analytics that goes beyond traditional BI. These are complex and resource-intensive processes. It makes sense to use cloud services, and in this case it makes sense to use Microsoft Azure data analytics.
- As with all cloud services, scalability is a big advantage. As resource demands increase and decrease, it becomes easier to allocate and control them. This is more cost effective and eliminates the need to purchase IT infrastructure.
What About Small And Medium Sized Businesses?
Most SMEs are still using their own on-premises systems, possibly with some cloud or SaaS (Software as a Service)applications. Big data is still a long way off, so analysis is done on an ad hoc basis, using reports or even a small data warehouse.
Is it worth using Azure Synapse in these businesses? From a technical point of view, of course it is. If there is no cloud strategy and if the above criteria do not apply, the usefulness of cloud analytics is low. If analytics is the only data in the cloud, the disruption to other organizations is too great to be beneficial. In this case, existing technology is sufficient (although it can probably be optimized).
If you already have a cloud strategy in place and want to use more data or even machine learning capabilities, you may want to look at Azure Synapse. If you’re already familiar with Azure, you can quickly create an Azure Synapse workspace that you can use to prove concepts, for example.
Wrap Up
Azure Synapse Analytics is filling the gap in the world of data warehouses. Lightweight, even when it comes to big data. It’s easy to get started with and can meet even the most demanding requirements.
The web interface is transparent, highly functional and generally impressive. Unfortunately, Microsoft does not provide enough detail on the individual fields and settings, so trial and error is inevitable. Some error messages are difficult to explain and not very helpful.
The practical implementation is very entertaining. It is therefore worth trying out the problems on a large scale to get a feel for the speed. The ability to solve problems in a different way is modern and exciting. Experience plays an important role in this and prevents the wrong approach to implementation.
Azure Synapse Analytics is the missing piece between multiple data sources and end-user reporting. It reduces the complexity of implementing multiple data sources and ensures system stability.
