Azure Synapse Analytics simplifies cloud architecture by uniting data warehousing, data integration and analytics into a single service.

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

arquitectura nube edificio

Nowadays, companies' analytical needs and their necessity to obtain insights depend on having an efficient data integration strategy, consolidated by data quality, data management and data governance. Although this process can be carried out in physical and on-premise integration systems, more and more companies are choosing 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, they must consider elements such as the number of workloads, databases, platforms, storage systems, data security standards, management platforms, etc.

Most organizations face problems due to disconnected clouds or inefficient migration teams and focus on multi-cloud architectures. The first step to adress these issues is to explore all the data, services, workloads and platforms in the cloud. It is also important to manage all the assets 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.

This is where Azure Synapse Analytics becomes relevant, as it provides a fast, flexible and reliable data storage and management solution in the cloud. In addition, it allows companies to scale, process and store data in a flexible and independent way, with a massive parallel processing architecture. 

Azure Synapse: the path to integrated analytics

As data assets and the interest in analyzing them grow, businesses tend to require two types of databases: data warehouses and data lakes, which fulfill different purposes.

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

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

More 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 data exploration and ad hoc analysis, or with provisioned resources, at scale.

Azure Synapse: storage, integration and analytics in one platform

The great advantage of Azure Synapse Analytics is that it brings together data integration, data warehousing, and macro 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 analysis that supports:
    - SQL pool (via payment by provisioned DWU). Being able to pause the SQL pool 
    when not in use, which stops the billing of IT resources. 
    - SQL on demand (under the payment formula for processed TB).
  • Spark: Apache Spark is fully integrated.
  • Data Integration: it is possible to address hybrid data integration scenarios whether they are on-premise, multi-cloud or hybrid environments.
  • Azure Synapse Studio: provides a unified user experience. Azure Synapse Studio provides data 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. A code-free environment for managing data pipelines is also available.

arquitectura de azure synapse analytics

Source: Bismart

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 and for the application of machine learning models to intelligent apps.

In terms of security, it integrates with Azure Active Directory and allows to protect, monitor and manage data and analytic solutions with, for example, single sign-on.

With Azure Synapse, data professionals can query both relational and non-relational data at a petabyte scale using an already familiar SQL language. And for mission-critical workloads they can easily optimize the performance of all queries through intelligent workload management, workload isolation, as well as unlimited concurrency. 

Azure Synapse vs. Databricks?

Azure Synapse Analytics vs Databricks

Azure Synapse Analytics provides a unique 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 (including Azure Data Factory, Azure Data Explorer, Azure Databricks, Azure SQL, Azure Analysis Services or Power BI), so that each one has its own interface and particularities. 
For all these cases, Synapse drastically simplifies this user experience by offering the ability to build end-to-end data pipelines through a single unified management tool.

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

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

The good news is that both Azure Synapse and Azure Databricks can run analyses on the same data hosted in an Azure Data Lake storage. Rather than being competing platforms, Azure Synapse continues the story started by Databricks in that it offers state-of-the-art data engineering, visualization and data storage in a completely new tool. 

Azure Synapse: Use cases 

Azure Synapse Analytics' multiple features and capabilities can make it difficult for companies to understand what the tool does and, more importantly, what can they use it for.

Below we list four common use cases for Azure Synapse:

  • 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, only Azure Synapse is capable of delivering the scalability, performance and feature set needed to run the most demanding tier-1 enterprise workloads.
  • Modernize mission-critical workloads: Allows you to optimize the migration of legacy devices to Azure Synapse by leveraging the most innovative capabilities, including code conversion and enterprise-level developer tools.
  • Unify enterprise data storage and big data analysis: These two worlds can be brought together through a unified experience using Azure Synapse Studio. Empowered to prepare, manage, and serve data for immediate needs in both business intelligence and self-learning and all from a single analysis service.
  • Integrating Power BI and Azure Synapse: It’s about taking business intelligence solutions to the next level and providing high-value insights with sub-secondary performance while managing massive amounts of data.

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

Posted by Núria Emilio