Microsoft Azure Analytics Services Application Lead - Intellipaat

23 Aug 2022

Here, we examine Microsoft Azure's key analytics services, their functions, and how they combine to provide a complete stack for your cloud analytics strategy.

1. Azure Analysis Services
If you frequently use SQL Server Analysis Services for business intelligence, you can also link Analysis Services' enterprise-grade analytics engine to Power BI as a cloud service. Although Azure Analysis Services is still available, Microsoft will offer an automated migration tool in the second half of this year for users who want to move their data models into Power BI instead. This is because the features in Power BI Premium are now more powerful than the capabilities in Azure Analysis Services.

2. Azure Data Factory
To make it simpler to combine data from diverse sources into data warehouses, Data Factory provides a service for code-free data mobility and data transformation pipelines: Think of ELT (extract, load, transform) and ETL (extract, transform, load) as a service with built-in connectors, but with a focus on transforming and enriching data rather than just moving it to the appropriate location (although you can also use it to move data into the cloud). Data Factory is very helpful for moving SQL Server Integration Services to Azure because it provides features like "code by example" to assist users in creating queries and also offers options for using Python, Java, and.NET with Git and CI/CD support.

3. Azure Data Explorer
As the name implies, Data Explorer is a big data analytics tool that you may use to explore data using KQL, commonly referred to as the Kusto Query Language, which may or may not be a reference to exploring your data ocean as if you were Jacques Cousteau. Data from services like Microsoft Purview, Microsoft Defender for Endpoints, Microsoft Sentinel, and Log Analytics in Azure Monitor are stored and searched for using Azure Data Explorer.

4. Azure Data Lake Analytics
Data warehouses are made to answer queries about your data that you already know you'll ask repeatedly. On the other side, data lakes give you the ability to store both organized and unstructured data to investigate fresh inquiries. Using R, Python,.NET, or U-SQL (which combines SQL and C#) to write queries, Azure Data Lake Analytics assists you in extracting, cleaning, and preparing data from Azure Data Lake. Additionally, key Azure Cognitive Services technologies are integrated as functions for machine learning-based text, speech, and image processing. With our serverless analytics job service, you only pay for the work that needs to be done rather than having to worry about managing infrastructure for data transformation on a petabyte scale.

5. Azure Synapse Analytics
Synapse Analytics gives you the capabilities of cloud data warehouse and data lake services but lets you run your preferred analytic engine — whether that's SQL or Spark — over all your data, structured and unstructured, without waiting for other teams in your organization to build their own analytics framework to extract data from data lakes and create data warehouses that business users have to access and work with separately. Azure Data Factory powers the data flows used by Synapse Analytics, and if you use Cosmos DB, transactions in your operational database will be mirrored and made available for analytics just seconds after they are recorded. This allows you to explore both big data and relational data at the same time. If the queries are worthwhile to ask repeatedly, you can codify them using conventional analytics methods.

6. Azure Databricks
Azure Databricks is an Apache Spark-based big data analytics solution optimized for Azure with data adapters for a variety of data formats and an interactive workspace for creating Spark dataflows. It allows you to instantly launch Spark clusters for converting, cleaning, and enriching your data. It's particularly suited for developing AI systems, and you can utilize popular data science frameworks like TensorFlow, PyTorch, and sci-kit learn, plus there is integration with Azure Machine Learning. You can work in Python, Scala, R, Java, or SQL.

7. Datamarts in Power BI
Think of datamarts as relational databases that are frequently driven by business users that need to gather data from various sources and integrate it together in a lightweight manner. They are designed to do analytics at the business unit level rather than the enterprise data warehouse level. It is an underserved market with less governance than CIOs might prefer because they lack the resources or expertise to provide a full relational data warehouse in the Azure portal. They also don't require petabyte or even terabyte scales of data and are currently using Excel or SharePoint lists for this.

A fully managed, self-service, no-code alternative for up to 100GB of data, datamarts in Power BI Premium have a Power Query-like user interface and workloads that are automatically optimized for performance (although advanced users can write DAX or SQL queries). By fusing the relational database model and Power BI's semantic model, Datamart finds relationships between tables and creates the dataset.
"You don't need to have any prior knowledge of DBA operations. We don't inquire about your indexing or partitioning techniques, says Netz. "To import data or run queries, you don't need to know how to write SQL. Every aspect is visible. Everything is simple to operate. Everything has been created with the creator in mind. Everything is designed for the user who knows how to create Power BI reports.”

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