Hello @Manoj G ,
Welcome to the MS Q&A platform.
You can use Azure services to ingest data, perform analytics, and create visual representations. The below list of supported Azure services, discloses their level of support, and provides you with links to articles that help you to use these services with Azure Data Lake Storage Gen2.
For more details, refer to Tutorials that use Azure services with Azure Data Lake Storage Gen2.
Azure Synapse started as a cloud data warehousing solution but recently evolved into a multipurpose data processing solution. Not only it allows to store data inside dedicated SQL pools, but also using Synapse Studio we can do ad-hoc analysis, data processing, create and schedule pipelines and event create PowerBI reports. Basically, Azure Synapse Studio is providing a unified environment for end-to-end data analysis, from raw data on ADLS to PowerBI dashboards. Also, Spark notebooks are first-class citizens together with SQL. With Spark pools and notebooks, we can utilize a Spark cluster while paying only for the time that we use them. Also, Spark notebooks support .NET for Spark so you can reuse C# code. These notebooks and other artifacts can be versioned with Git. Synapse studio provides integration with Git like ADF, and whatever you do in ADF can be in Synapse Studio.
Azure Databricks is a data analytics platform optimized for the Microsoft Azure cloud services platform. Azure Databricks offers three environments for developing data intensive applications: Databricks SQL, Databricks Data Science & Engineering, and Databricks Machine Learning.
There are several differences between Databricks workspace and Synapse Spark poll. Databricks support classical set languages for Spark API: Python, Scala, Java, R, and SQL. While Synapse supports Python, Scala, SQL, and C#. Another important difference is the runtime. Synapse is using Azure Synapse for Spark which is based on Apache Spark but optimized by the Synapse team. Meanwhile, Databricks is using its own custom runtime again based on Apache Spark but optimized by Databricks (creators of Spark). There are some benchmarks for each one compared to vanilla Apache Spark but I am not aware of Synapse vs Databricks runtime comparison.
Azure Data Explorer serves a different role in comparison to Azure Databricks and Synapse. Its main purpose is interactive analytics of structured and unstructured data such as logs and telemetry. Not only data can be ingested from streaming sources such as EventHub, but also it becomes immediately available for querying after ingestion. ADX can serve also as backing storage for your dashboards such as Grafana and PowerBI.
Hope this will help. Please let us know if any further queries.
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