Simplify your cloud data architecture with Azure Synapse Analytics

More and more companies are moving their data architecture to the cloud. Choosing the right platform and architecture is critical for a smooth and efficient transition. Azure Synapse Analytics helps enterprises streamline the management of their cloud infrastructure, while addressing analytical needs and solving most of the issues caused by a poor cloud migration.

The analytical and insight gathering needs of companies depend on having an efficient data integration consolidated by data quality, data management and data governance measures. Although this process can be carried out in physical and in-house integration systems -also known as on-premise-, more and more companies are opting for the new possibilities of cloud services, mainly because of their advantages in terms of flexibility and scalability.

When a company decides to move part of its infrastructure to the cloud, it must take into account aspects such as the number of workloads, databases, platforms, storage systems, data security models, management platforms, etc.

Most organizations face complexity issues due to disconnected builds of their cloud or migration teams and focus on best-of-breed and multi-cloud architectures. The first step in addressing their complexity is to look at all the data, services, workloads and platforms. It is important to find ways to manage them using tools that support abstraction and automation.

On the other hand, a successful cloud architecture solution depends not only on technical requirements, but also on economic and strategic ones.

In this sense, Azure Synapse Analytics becomes relevant, as it provides fast, flexible and reliable cloud data storage management. In addition, it allows you to scale, process and store data flexibly and independently, with a massively parallel processing architecture.

Azure Synapse: for integrated analytics

As data assets and interest in analyzing them grow, enterprises typically require two types of databases: data warehouses and data lakes, which serve different purposes.

While both storage systems are critical and should be fully integrated, reality shows that they often operate independently, which can lead to uninformed decision making and hinder the data-driven decision making process.

At the same time, companies need to unlock the information that resides in all their data to stay competitive. Azure Synapse is the only cloud analytics service that bridges this gap and provides the agility that enterprises demand, bringing together analytics, enterprise data warehousing and Big Data in a single service.

Specifically, it is an unlimited analytics service that gives organizations the freedom to query data as they prefer, either on-demand serverless (a type of deployment that automatically scales power on demand when large amounts of data are available) for ad hoc data exploration and analysis; or with provisioned resources, at scale.

Azure Synapse: warehousing, integration and analytics in one platform

The big advantage of Azure Synapse Analytics is that it brings together data integration, data warehousing, and data and big data analytics in a single platform. In terms of warehousing and integration, Azure Synapse Analytics has a consistent data model that incorporates administration, monitoring and metadata management sections.

SQL Analytics: full T-SQL-based analytics with support for:

- SQL pool (for a fee per provisioned DWU). Pausing the SQL pool when not in use

SQL pool when not in use, which stops the billing of IT resources.


- SQL on demand (under the formula of payment per TB processed).

Spark: Apache Spark fully integrated.

Data integration: it is possible to address hybrid data integration scenarios, whether on-premise, multi-cloud or hybrid environments.

Azure Synapse Studio: Provides professionals with a single workspace to prepare and manage their data, as well as to manage their Big Data, artificial intelligence and machine learning tasks.

artificial intelligence and machine learning tasks. It also has a no-code environment for managing pipelines.

Azure Synapse supports a wide range of programming languages: SQL, Python, .NET, Java, Scala and R. This makes it well suited for different analytics workloads and different engineer profiles.

Azure Synapse Analytics also integrates with Power BI and Azure Machine Learning to power the discovery of insights in data and for the application of machine learning models to intelligent applications.

On the security side, it is integrated with Azure Active Directory and allows you to secure, monitor and manage your data and analytics solutions, for example, by employing single sign-on.

All in all, Azure Synapse enables professionals to query both relational and non-relational data at petabyte scale and using a SQL language they are already familiar with. For mission-critical workloads, they can easily optimize the performance of all queries through intelligent workload management, workload isolation and unlimited concurrency.

Azure Synapse vs. Databricks?

Azure Synapse Analytics provides a single tool for data engineering that supports data provisioning, reporting and self-service data analysis.

In a typical Azure-based data management project, data engineers may interact with many different tools (Azure Data Factory, Azure Data Explorer, Azure Databricks, Azure SQL, Azure Analysis Services, Power BI, etc.). Each of these tools has its own interface, language and particularities. For all these cases, Synapse drastically simplifies the user experience, offering the possibility to build end-to-end data pipelines through a single unified management tool.

In this way, the native integration of Azure Synapse Analytics with the rest of the Azure platform can come to drive unprecedented security control and trigger the ability to do proof of concept (POC) to find value to new projects faster, then effectively scale business processes within a single tool.

However, for those professionals who require additional scalability, Databricks provides fast data transfers between data services and streaming support, in addition to its Mapping Data Flow capabilities.

The good news is that both Azure Synapse and Azure Databricks can run analytics on the same data hosted in an Azure Data Lake storage. In other words, rather than being clashing platforms, Azure Synapse arguably continues the story started by Databricks, offering next-generation data engineering, visualization and data warehousing in an entirely new tool.

Azure Synapse: Use cases

The multiplicity of functionality and capabilities in Azure Synapse Analytics can make it difficult for enterprises to understand what the tool does and, more importantly, what they can use it for.

In this regard, we list four common use cases for Azure Synapse below:

Enterprise-level data warehousing - Azure Synapse is the ideal solution for enterprise data warehouses in Azure. Whether we are migrating from an existing legacy appliance, or developing a new cloud solution; Azure Synapse brings the scalability, performance and capabilities needed to run the most demanding enterprise workloads.

Modernize mission-critical workloads - Azure Synapse enables you to optimize the migration of legacy devices to Azure Synapse, leveraging innovative capabilities such as code conversion.

Unify enterprise data warehousing and big data analytics: Azure Synapse Studio unifies Big Data warehousing with enterprise analytics. Through a single analytics service, it enables you to prepare, manage and serve data for immediate needs, both business intelligence and machine learning.

Integrate Power BI and Azure Synapse: This integration takes our business intelligence solutions to the next level, delivering valuable insights with sub-second performance while managing massive amounts of data.

Ultimately, Azure Synapse merges storage with analytics through a unified experience; enabling data ingestion, preparation, management and access, and solving organizations' immediate business intelligence and machine learning needs.