What is Azure Synapse Analytics and How Does it Work

Azure Synapse Analytics is a cloud-based enterprise data warehouse that allows you to integrate, analyze, and visualize data from various sources. It's a unified analytics service that combines enterprise data warehousing and big data analytics.

Azure Synapse Analytics is built on the foundation of Azure SQL Data Warehouse, which was first released in 2015. It's designed to handle large-scale data processing and analytics workloads.

The service supports a wide range of data sources, including relational databases, NoSQL databases, and cloud storage. It also provides a unified view of your data, making it easier to analyze and gain insights.

By integrating data from various sources, Azure Synapse Analytics enables you to create a single, unified view of your data, making it easier to analyze and gain insights.

Azure Synapse offers serverless Apache Spark pools in your workspace, which creates a spark session to handle resources associated with that session.

You can use Spark analytics in two ways within Synapse: Spark Notebooks for data science and engineering, and Spark job definitions for running batch Spark jobs using jar files.

Spark Notebooks support multiple languages, including Scala, PySpark, C#, and SparkSQL.

Spark job definitions allow you to run batch Spark jobs.

Azure Synapse is built to handle high-level architecture, specifically designed for Online Transaction Processing (OLTP) workloads.

OLTP involves transactional data with high reads and writes, and data ingestion typically happens through user transactions in small batches of rows.

The data access pattern in OLTP usually involves a lot of scalar and tabular datasets.

Azure Synapse fits in the overall data landscape for Online Analytical Processing (OLAP) applications, which store and process large volumes of data collected from various sources.

These large datasets are aggregated for ad-hoc reporting and analytical use-cases in OLAP applications.

Azure Data Lake Storage forms the bedrock of big data storage.

Power BI forms the visualization layer in the overall data landscape.

Azure Synapse makes it easy to work with your data lake by removing traditional technology barriers between SQL and Spark. This means you can seamlessly mix and match based on your needs and expertise.

Tables defined on files in the data lake can be consumed by either Spark or Hive, allowing for flexibility in your data analysis.

SQL and Spark can directly explore and analyze Parquet, CSV, TSV, and JSON files stored in the data lake, making it simple to get the insights you need.

Fast, scalable data loading between SQL and Spark databases enables you to quickly move data around and focus on analysis.

Here's a quick rundown of the file formats you can work with:

Azure Synapse offers top-notch security and authentication features to safeguard your data. The platform boasts automated threat detection and always-on data encryption, ensuring your sensitive information remains protected.

Azure Synapse supports two types of authentication: SQL Authentication and Microsoft Entra authentication. SQL Authentication uses a username and password, while Microsoft Entra authentication leverages identities managed by Microsoft Entra ID.

With Microsoft Entra authentication, multifactor authentication can be enabled, adding an extra layer of security. It's recommended to use Active Directory authentication (integrated security) whenever possible.

Authorization in Azure Synapse is controlled by user account database role memberships and object-level permissions. This means that users can only access objects and data that they have been explicitly granted permission to.

Here are the supported authorization types for accessing storage accounts:

Azure Storage administrators must grant permissions to Microsoft Entra users or workspace identities before they can access data. This ensures that only authorized users can access sensitive information.

Azure Synapse allows you to query data on your terms, using either serverless on-demand or provisioned resources, at scale.

With Azure Synapse, data professionals of all types can collaborate, build, manage, and analyze their most important data with ease, all within the same service.

Companies like Unilever are already benefiting from Azure Synapse, with Nallan Sriraman, Global Head of Technology, praising its ability to "streamline our analytics processes even further with the seamless integration the way all the pieces have come together so well."

Azure Synapse is built to integrate with a wide range of services, including Power BI and Azure Machine Learning, making it easy to apply intelligence to all your most important data.

Pipelines are the backbone of Azure Synapse, allowing you to move data between services and orchestrate activities.

A pipeline is a logical grouping of activities that perform a task together, making it easier to manage complex data workflows.

Activities define actions within a pipeline, such as copying data, running a Notebook, or executing a SQL script.

Data flows are a specific type of activity that provide a no-code experience for doing data transformation, leveraging Synapse Spark under the hood.

Here's a quick rundown of the key components of a pipeline:

Azure Synapse Analytics is a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale.

Businesses can continue running their existing data warehouse workloads in production today with Azure Synapse and will automatically benefit from the new capabilities. This means you can put your data to work much more quickly, productively, and securely, pulling together insights from all data sources.

With Azure Synapse, data professionals of all types can collaborate, build, manage, and analyze their most important data with ease, all within the same service. From Apache Spark integration with the powerful and trusted SQL engine to code-free data integration and management.

Companies like Unilever are choosing Azure Synapse for its ability to streamline analytics processes and enable seamless integration. Their adoption of the Azure Analytics platform has revolutionized their ability to deliver insights to the business.

Azure Synapse is deeply integrated with Power BI and Azure Machine Learning to greatly expand the discovery of insights from all your data and apply machine learning models to all your intelligent apps. This enables you to significantly reduce project development time for business intelligence and machine learning projects.

Linked Services are essentially connection strings that define the connection information needed for a workspace to connect to external resources.

In other words, Linked Services are like a set of instructions that tell your workspace how to talk to other systems and tools. This is a powerful feature that enables you to integrate your workspace with various external resources.

A workspace can contain any number of Linked Services, which allows you to connect to multiple external resources at the same time.

Having multiple Linked Services in a workspace is like having a Rolodex of connections - it makes it easy to access and use different external resources as needed.

Each Linked Service defines the connection information needed for the workspace to connect to a specific external resource.

This connection information can include things like usernames, passwords, and server addresses.