In this blog, we are going to cover Azure Synapse Analytics, Components, its functionalities, Databricks, Functionalities of data bricks, and Use Cases of Azure Synapse Analytics and Azure Databricks.
Topics we’ll cover:
- What is Azure Synapse Analytics
- Components of Synapse
- Functionalities of Azure Synapse Analytics
- What is Azure Databricks
- Functionalities of Azure Databricks
- Use Cases of Azure Databricks and Azure Synapse Analytics
What Is Azure Synapse Analytics?
Azure Synapse Analytics is a scalable and cloud-based data warehousing solution from Microsoft. It is the next iteration of the Azure SQL data warehouse.
Source: Microsoft
It provides a unified environment by combining the data warehouse of SQL, the big data analytics capabilities of Spark, and data integration technologies to ease the movement of data between both, and from external data sources. We can ingest, prepare, manage, and serve data for immediate BI and machine learning needs easily with Azure Synapse Analytics
Components of Synapse
- Synapse SQL
- Provisioned Pool
- On-demand Pool
- Open-source Spark & Delta
- Synapse Pipelines
- Studio
Functionalities of Azure Synapse Analytics
- Azure Synapse offers cloud data warehousing, dashboarding, and machine learning analytics in a single workspace.
- It ingests all types of data, including relational and non-relational data, and it lets you explore this data with SQL.
- Azure Synapse uses massively parallel processing or MPP database technology, which allows it to manage analytical workloads and also aggregate and process large volumes of data in an efficient manner.
- It is compatible with a wide range of scripting languages like Scala, Python, .Net, Java, R, SQL, T-SQL, and Spark SQL.
- It facilitates easy integration with Microsoft and azure solutions like Azure Data Lake, Azure Blob Storage, and more.
What Is Azure Databricks?
Azure Databricks is a managed version of the Databricks platform optimized for running on Azure. Azure has tightly integrated the platform in its Azure Cloud integrating it with Active Directory, Azure virtual networks, Azure key vault, and various Azure Storage services.
Source: Microsoft
Setting up an integrated platform for data scientists and data engineers to collaborate is tough. Although a lot of organizations start with Data Science development locally on their laptop or a VM, organizations who embrace the power of AI will need at a certain time both more computing power as well as the ability to truly collaborate among teams. Databricks is a hassle-free platform offering both IT as well as data users (engineers and scientists) a top-notch platform.
Functionalities of Azure Databricks
- Managed Clusters in Spark consist of a driver node and -exceptions aside- one or more executor nodes. The driver distributes the tasks over the different executors and handles communication.
- Collaborative Notebooks in Databricks is built around the concept of a notebook for writing code. Notebooks allow developers to combine code with graphs, markdown text, and even pictures. In terms of programming languages, Databricks supports Python, Scala, R, and SQL.
- On-demand Spark Jobs in Databricks makes it possible to run workloads as ‘jobs’, both on-demand or according to a defined schedule. At this point, there are four types of jobs: notebooks, spark jars, spark python, or spark-submit.
- Real-time and Batch in Databricks can support data users in both real-time pipelines (using Delta or Spark Streaming) or batch data jobs.
Use Cases Of Azure Databricks and Azure Synapse Analytics
Azure Synapse introduced Spark to make it possible to do big data analytics in the same service. With all the new functionalities that Synapse brings and you might get confused about when to use Synapse and when Databricks because we can use Spark in both products.
| Use Cases |
Synapse |
Databricks |
Preferred |
| Real-Time Transformation |
|
|
Databricks |
| SQL Analyses & Data warehousing |
|
|
Synapse |
| Ad-hoc data lake discovery |
|
|
Synapse & Databricks |
Related/References
- Microsoft Certified Azure Data Engineer Associate | DP 203 | Step By Step Activity Guides (Hands-On Labs)
- Exam DP-203: Data Engineering on Microsoft Azure
- Microsoft Azure Data Engineer Associate [DP-203] Interview Questions
- Azure Data Lake For Beginners: All you Need To Know
- Batch Processing Vs Stream Processing: All you Need To Know
- Reading and Writing Data In DataBricks
Next Task For You
In our Azure Data Engineer training program, we will cover all the exam objectives, 27 Hands-On Labs, and practice tests. If you want to begin your journey towards becoming a Microsoft Certified: Azure Data Engineer Associate check our FREE CLASS.
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