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Azure Analytics Services | Microsoft Azure

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Azure offers a comprehensive set of intelligent solutions for data warehousing, advanced analytics on big data, and real-time streaming.

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Microsoft Azure Analytics Services and Consulting

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We provide Microsoft Azure analytics services & consulting to help you develop custom cloud-based analytics solutions to manage data in one place.

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Imagine running a website or application where users are worldwide, and to provide the service, you need to have large data servers where you have to hire employees for constant backup and recovery. Imagine the cost your organization has to bear. So to tackle this problem, Microsoft came up with an Azure cloud computing service created by Microsoft for testing, building, deploying, managing apps and other services through 42 data centres in the world named Azure Synapse Analytics.

It provides services like infrastructure as a service (Iaas), Platform as a service (Paas), and software as a service (Saas) with tools and frameworks of your choice. It has evolved from Azure SQL DW to blend data warehousing, data integration, and big data capabilities into a single service.

In this article let us look at:

1. What is Azure Synapse Analytics?

2. Other features of Azure Synapse Analytics

3. Other enhancements included in Azure Synapse Analytics

4. The importance of Data Governance and the Introduction of Azure Purview

5. How do you create Azure Synapse workspaces?

6. Azure Synapse Purview tools to be used

1. What is Azure Synapse Analytics?

Synapse provides a single service for all tasks like processing, managing, and serving data. This is done with the integration of power BI and Azure Machine Learning. Azure can give you the freedom to manage query data on your terms. You have a choice to choose high provisioned resources or less demand when your users are online or not at the moment. Azure Synapse Analytics is a rebranding of Azure SQL Data Warehouse. Azure SQL Data Warehouse was an MPP (Massively parallel processing) service that was designed to store large data with Microsoft Azure Data Warehouse.

However, Microsoft has added some special feature which put them on a competitive edge than other players in the market. Nearly 80% of the fortune 500 companies use Microsoft Azure as their data warehouse. Microsoft has another biggest advantage in that they are everywhere. Users are aware of their interface and their other applications, which are used on an organizational level as well, e.g. Microsoft Excel, Microsoft Access, Microsoft PowerPoint, and MSBI (Microsoft Business Intelligence).

2. Other features of Azure Synapse Analytics

Powerful Insights - expand insights into all your data and machine learning modules in your websites and applications.

Instant clarity - You can instantly clear all your business-related doubts with the freshest data possible regarding Microsoft Azure Synapse Architecture.

Unmatched security - You can secure your data on row-level security and dynamic data masking.

Unified Experience - It reduces project development time with end-to-end solutions.

Limitless Scale - Deliver your data to your upper management without any hassle at blazing fast speeds.

Cost-effective - the most cost-effective and data management cloud.

3. Other enhancements included in Azure Synapse Analytics

Intelligent workload management, limitless concurrency, workload isolation, and workload optimization.

Machine learning integration and business intelligence with applications including Microsoft office 365, Microsoft dynamics software, and any Saas service that support Open data Initiative

Cloud-native HTAP Integration enables data engineers, business analysts, and data scientists to access the freshest operational data.

Advanced security features, e.g. automated theft detection, always-on encryption, dynamic data masking, always-on encryption, and fine-grained access control at column and row level.

Unified developing service to provide end-to-end solutions.

4. The importance of Data Governance and the Introduction of Azure Purview

Data Governance - This helps us to ensure that data is accessible, useful, and protected. Data governance leads to improved data quality, decreased data management costs, easy access to data to shareholders. This results in better decision-making and better outcomes.

Azure Purview is a data governance solution that helps you understand all the data deeply across your data warehouse and maintain control over your data usage. It's built on the Apache Atlas framework, which is open source and easy metadata management and data warehouse governance.

5. How do you create Azure Synapse workspaces?

Under Services, select Azure Synapse Analytics.

Select ADD to create Workspace.

Under the Project details, pick any Subscription.

Use any Resource group and leave the resource group blank.

In the Basics Tab, click Workspace details. Workspace Name > Region - pick any region where your company is located > Select Data Lake Storage Gen 2

Click Form Subscription - select any subscription.

My account name > Click NEW and name the storage account as your organization name.

By File System Name > Click New this will create a primary storage account to Spark tables and application logs.

6. Azure Synapse Purview tools to be used

Azure Synapse data Integration - You can create data pipelines and combinations for itself to access closed sources, open sources, and data warehouses.

Synapse SQL Pools - This service allocates services for data processing and can be imported to other frameworks as well.

Apache Spark for Azure Synapse - It provides azure to perform loading, processing, and data preparation.

Conclusion

Azure Synapse Analytics is an evolving service and was launched in past years to enhance SQL Data Warehousing. If your business needs Azure Synapse Analytics, then you should consider getting Microsoft Azure over other services like Amazon AWS and Google Cloud as it is used by all major companies. But you need to check what suits best for your organization.

If you are interested in making it big in the world of data and evolve as a Future Leader, you may consider our Business Analytics Certification, a 10-month online program, in collaboration with IIM Indore!

ALSO READ

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Azure Synapse Analytics: A Detailed Guide in 6 Points

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Imagine running a website or application where users are worldwide, and to provide the service, you need to have large data servers where you have to hire employees for constant backup and recovery. Imagine the cost your organization has to bear.

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As data becomes increasingly important in the business world, the tools that manage and analyze it gain corresponding relevance. This is the case of Microsoft's Azure Synapse Analytics, which integrates several data management and storage options in order to help business intelligence to obtain new business opportunities or improve company processes.

What is Azure Synapse Analytics?

Azure Synapse Analytics is a versatile tool from Microsoft used to ingest, store and analyze data from various sources of origin. Therefore, within this service, all this information is analyzed to impact the different business processes and grow the business. In addition, since part of the information is processed inside, it can be used to further increase its value in services for the creation of machine learning models.

According to Microsoft, through Azure analytics, you get information up to 380% faster and at a 49% lower cost than with other cloud providers. In addition, thanks to the constant improvement of the product, we can find the latest news and always with transparent management, so that the user only has to worry about their data.

Azure Synapse contains a number of tools that allow the user to perform any type of operation on the data quickly and easily. It features connections for different data sources and origins. In addition, you can get the most out of it thanks to the incorporation of:

SQL for business data storage

Spark to improve the performance of big data analysis applications

Data Explorer for analyzing time series and log information

In fact, it incorporates pipelines to assist in data integration and facilitate ETL/ELT processes. Also, it offers numerous integrations with other Azure services such as Power BI, CosmosDB, and AzureML.

When to use Azure Synapse Analytics

Some possible applications of Azure Synapse suggested by Microsoft are:

Improving factories' productivity by analyzing data concentrated in a single place, with a complete view of the supply chain.

Enhance the storage of patients' data by linking the information to disease symptoms, therefore being able to recommend treatment.

Real-time analysis of customer data to improve customer care and provide them with the help they need at all times.

Fight the fraud that can be incurred with customers' money, as the application instantly detects fraud in customer accounts through automatic and continuous monitoring of data.

Azure Synapse Architecture

Database Templates

They help to 'model' in a simpler and faster way the company's data. These templates have industry-specific schemas with tables, columns, and relationships on operational, commercial, or transactional data. In addition, they are customized for different businesses: banking, insurance, retail ...

According to Product Management Leadership for Azure Synapse Analytics at Microsoft, Santosh Balasubramanian, the advantages of database templates include:

Accelerate the time needed in order to obtain information based on specific schemas for different industries

Identify opportunities in the current business data model

Unify data and query all data from a single site (Synapse Studio)

Create a well-formed Data Lake, ready for analysis at scale

Give more value to data using Azure Machine Learning and Azure Cognitive Services

Develop reports easily using Power BI

Once you choose a database template, you access an editor where you configure tables, columns or relationships. Data connectors (Azure Synapse has more than 90 built-in connectors to take data from SaaS applications) help any source to be loaded directly into your database. From there, different insights or analysis based on machine learning models are elaborated to obtain conclusions that help the business.

The components that are part of this environment include the following:

Synapse Studio

Synapse Studio is the workspace for creating data analysis solutions quickly and clearly. In the company's words, it manages the " preparation, administration, exploration and storage of data, Big Data and Artificial Intelligence ". The interface is based on trying to provide value by abstracting the person from a certain technical knowledge to facilitate its use through a no-code environment.

Synapse SQL

Synapse SQL offers a T-SQL query system for data storage and visualization, extending the possibility of real-time and machine learning scenarios.

Azure Synapse Pathway

Azure Synapse Pathway is the tool to migrate different databases, both local and cloud, to Azure Synapse Analytics. This tool helps to simplify the code translation process: in just a few minutes, without manual rewriting, you can do it with thousands of lines.

Security in Azure Synapse Analytics

Security in Azure Synapse Analytics is an important and very prominent part that gives the product a complete view. The tool has an automated threat detection and data encryption system in constant operation, such as encrypting columns and hidden data in real-time automatically. In addition, there are tamper-evident alerts. Moreover, backups are also automated.

Azure SQL vs. Azure Synapse

Microsoft itself clarifies that " what was called Azure Synapse Analytics (formerly SQL DW) is now called dedicated SQL group (formerly SQL Data Warehouse) in Azure Portal ". In other words, those who use or know the name "Azure SQL Data Warehouse" can continue to use the tool and take advantage of the utilities built into Azure Synapse Analytics.

Do you think Azure Synapse is for your company?

The integration possibilities and versatility of Azure Synapse make it a very suitable tool to cover business processes related to data, from ingest to analysis. In addition, its special care for security (from the protection of the data itself to the automatic creation of backup copies) makes it a tool with which not to worry about complicated and tedious processes that, with it, are solved automatically.

Plain Concepts is a certified company, which means that thanks to our expertise we are able to help you create custom solutions in Azure Synapse Analytics. Send us a message without commitment, we will find a way to get the maximum value from your data.

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As data becomes increasingly important in the business world, the tools that manage and analyze it gain corresponding relevance. This is the case of Microsoft's Azure Synapse Analytics, which integrates several data management and storage options in order to help business intelligence to obtain new business opportunities or improve company processes.

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Raw data is meaningless. It is the process of big data analysis that turns meaningless datasets into actionable insights. Big data analytics is the foundation of data-driven decisions, which enable organizations to avoid guesses and hopeful intuition.

Before you can transform your raw data into insights, you need to set up an analysis process. Each project merits a different approach. You can use a combination of a cloud-based data warehouse with a compatible analysis service. Alternatively, you can combine managed services with private clouds. Or you can set up your own hybrid operation.

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If you're using or considering Azure cloud services, this article can help you learn about eight popular big data analytics options on Microsoft Azure, what differentiates each service, and typical use cases for each option.

1. Azure Synapse Analytics

Azure Synapse Analytics is the next generation of Azure SQL Data Warehouse. It lets you load any number of data sources - both relational and non-relational databases, whether on-premise or in the Azure cloud. It unifies all the data and lets you process and analyze it using the SQL language. In addition, it provides the Azure Synapse Studio that offers a workspace for big data analysis and AI tasks and creates engaging visualizations of your data.

Image Source: Microsoft Azure

2. Azure Databricks

Databricks is an analytics service based on Apache Spark. Apache Spark is a veteran tool used to process huge amounts of unstructured data at high speed. Databricks supports languages like Python, Scala, Java, SQL, and R, as well as AI/ML libraries like TensorFlow and PyTorch, allowing you to work with Spark data using any of these languages and frameworks. In addition, Databricks integrates with Azure Machine Learning (see below), giving you access to a large number of pre-trained machine learning algorithms.

Databricks lets you set up managed Apache Spark clusters with auto-scaling and auto-termination, eliminating the complexity of setting up Spark in your local data center.

3. Azure HDInsight

Apache Hadoop was a huge deal for big data in the previous decade, and while usage has declined, the Hadoop ecosystem is still incredibly powerful. It allows you to perform complex, distributed analysis tasks on virtually any volume of data. HDInsight lets you quickly create big data clusters using Hadoop and scale them up or down based on your needs. It integrates with other Azure services like Data Factory and Data Lake Storage, allowing you to apply Hadoop analytics to the data you already have there.

HDInsight comes with the full set of popular Hadoop tooling, including Apache Spark, Apache Kafka, HBase, Hive, and Storm. It provides enterprise-scale infrastructure in the form of monitoring, security, compliance, and high availability via Azure redundancy options.

4. Azure Data Factory

Azure Data Factory is an Extract Transform Load (ETL) service. ETL is a term from the old days of large-scale processing of structured data. An ETL process takes a structured database, cleans it, and converts the data into a format that is suitable for analysis. Data Factory helps you build ETL and also Extract Load Transform (ELT) strategies with no code or configuration using a visual editor.

Data Factory provides built-in connectors with over 90 data sources including Amazon S3, Google BigQuery, and many on-premise data sources. You can also copy the data from Data Factory to Azure File Storage.

Image Source: Microsoft Azure

5. Azure Machine Learning

This is a huge library of pre-packaged, pre-trained machine learning algorithms. It also provides an environment for consuming these algorithms and applying them to real-world tasks. Azure ML speeds up model creation with a convenient machine learning UI that allows you to build machine learning pipelines combining multiple algorithms, with steps like model training, testing, and evaluation.

In addition, Azure ML provides solutions for interpretable AI. It includes visualization and other data that can help understand model behavior, apply fairness metrics, and make comparisons between algorithms to understand the best variant to choose.

6. Azure Stream Analytics

Azure Stream Analytics lets you build an end-to-end pipeline for streaming events. It is based on serverless technology. Stream Analytics lets you define an analytics pipeline for streaming data, with data processing defined using SQL syntax, and go-to production in minutes. It scales up elastically depending on the volume and throughput of your streaming data.

Because streaming data often requires very high-performance processing and realtime responses, Azure Stream Analytics offers sub-second latency with guaranteed "exactly once" event processing. It also offers 99.9% availability.

7. Data Lake Analytics

Azure Data Lake Analytics lets you develop data transformation programs using a variety of languages including U-SQL (a special language provided by Microsoft that combines the benefits of SQL and C#), Python, .NET, and R. It can process petabytes of data.

Data Lake Analytics is different from Azure Synapse Analytics in that it does not pull all your data into a data lake and then process it. Instead, it connects to Azure-based data sources, such as Azure Data Lake Storage, and performs on-the-fly analytics using code you provide.

8. Azure Analysis Services

Azure Analysis Services can be set up using the Azure Resource Manager, which combines data from multiple sources and creates one trusted semantic model. It allows you to develop high-performance BI solutions with secured access and fast time to delivery. It scales up and down based on analytical workload, and you pay only for the resources you consume. Analysis Services also lets you import existing models or SQL Server 2016 tabular models.

Conclusion

Hopefully this article has helped you learn about big data analytics options on Microsoft Azure. Be sure to properly assess your needs and requirements and then experiment with different services and solutions. Big data architecture is already complex and introducing new tools should be done with care.

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Eight Big Data Analytics Options on Microsoft Azure - DATAVERSITY

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Click here to learn more about Gilad David Maayan. Raw data is meaningless. It is the process of big data analysis that turns meaningless datasets into actionable insights. Big data analytics is the foundation of data-driven decisions, which enable organizations to avoid guesses and hopeful intuition.

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If you can't make sense of your business data, you're effectively flying blind. Insights hidden in your data are essential for optimizing business operations, finetuning your customer experience, and developing new products - or new lines of business, like predictive maintenance. Analytics is the means for discovering those insights, and doing it well requires the right tools for ingesting and preparing data, enriching and tagging it, building and sharing reports, and managing and protecting your data and insights. And as businesses contend with increasingly large amounts of data, the cloud is fast becoming the logical place where analytics work gets done.

For many enterprises, Microsoft Azure has become a central hub for analytics. Taking the broadest possible interpretation of data analytics, Azure offers more than a dozen services - and that's before you include Power BI, with its AI-powered analysis and new datamart option, or governance-oriented approaches such as Microsoft Purview. Leaving aside more specialized options such as ingesting telemetry, sharing data externally, or building machine learning models to deliver specific analyses, there are still enough Azure analytics services that you might wonder which one is best suited for any given job.

The truth is, Microsoft aims to provide CIOs a full stack of analytics services on Azure designed to work together, rather than a piecemeal approach, says Amir Netz, CTO of Microsoft Analytics. It is geared toward IT chiefs who want to be chief data officers, not chief integration officers, he suggests.

Although there is overlap between the various services, Netz explains that Azure's analytics services broadly correspond to the layers an organization would build in creating an analytics architecture framework, "from creating the data lake and storing the data, processing the data in the lake and doing the data engineering, the ability to build data warehouses on top of that, to run machine learning algorithms, to do data science, to serve the data to business users," he says.

The recent announcement of the Microsoft Intelligent Data Platform makes that more obvious, though analytics is only one part of that new brand.

Here we take a look at Microsoft Azure's essential analytics services, what they are used for, and how they come together to make a comprehensive stack for your analytics strategy in the cloud.

The reason Azure has so many analytics services is so you can build your entire stack there.

Microsoft

1. Azure Analysis Services

If you're used to using SQL Server Analysis Services for business intelligence, Analysis Services offers that enterprise-grade analytics engine as a cloud service that you can also connect to Power BI. But the features in Power BI Premium are now more powerful than the functionality in Azure Analysis Services, so while the service isn't going away, Microsoft will offer an automated migration tool in the second half of this year for customers who want to move their data models into Power BI instead.

2. Azure Data Factory

Data Factory is a service for code-free data movement and data transformation pipelines to make it easier to integrate data from various sources into data warehouses: Think ETL (extract, transform, load) and ELT (extract, load, transform) as a service with built-in connectors, but with the emphasis on transforming and enriching data rather than just moving it into the right place (although you can also use it to move data into the cloud). Data Factory includes features such as " code by example " to help users build queries but also has options to use languages such as Python, Java, and .NET with Git and CI/CD support, making it particularly useful for migrating SQL Server Integration Services to Azure.

3. Azure Data Explorer

Data Explorer is a big data analytics platform that you can use, as the name suggests, for exploring data using KQL, also known as the Kusto Query Language, from the codename for the project which may or may not be a reference to exploring your ocean of data as if you were Jacques Cousteau. Azure Data Explorer is used to store and query data in services such as Microsoft Purview, Microsoft Defender for Endpoint, Microsoft Sentinel, and Log Analytics in Azure Monitor.

4. Azure Data Lake Analytics

Data warehouses are designed for questions you already know you want to ask about your data, again and again. Data lakes, on the other hand, enable you to store structured and unstructured data to explore with new questions that you haven't asked before. Azure Data Lake Analytics helps you extract, clean, and prepare data from Azure Data Lake using R, Python, .NET, or U-SQL (which combines SQL and C#) to write queries, with key technologies from Azure Cognitive Services included as functions for processing text, speech, and images using machine learning. This is a serverless analytics job service that can handle petabyte scale data transformation, so you pay for the job rather than needing to manage infrastructure.

5. Azure Synapse Analytics

If you want to get away from building your own analytics framework that multiple teams in your organization will then use to extract data from data lakes and build data warehouses that business users have to access and work with separately, 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 ETL processes or worrying about where the data is stored or how to connect to it. Synapse Analytics data flows are powered by Azure Data Factory, and if you use Cosmos DB, transactions in your operational database will be mirrored and available for analytics seconds after they're recorded, so you can explore big data and relational data together. If the questions are useful enough to ask again and again, you can formalize them with traditional analytics techniques.

6. Azure Databricks

If you want to spin up Spark clusters on demand for transforming, cleaning, and enriching your data, Azure Databricks is an Apache Spark-based big data analytics service optimised for Azure with data adapters for various data types and an interactive workspace for building Spark dataflows. You can work in Python, Scala, R, Java, or SQL, but it's particularly suitable for building AI systems and you can use common data science frameworks such as TensorFlow, PyTorch, and sci-kit learn, plus there's integration with Azure Machine Learning.

7. Datamarts in Power BI

Think of datamarts as relational databases designed to do analytics at the business unit level rather than the enterprise data warehouse level, frequently driven by business users who need to collect data from multiple sources and integrate it together in a lightweight way. They don't have the skills or the budget to provision a full relational data warehouse in the Azure portal, they don't need the petabyte scale or even terabytes of data and they're currently using technologies such as SharePoint lists or Excel for this, making it an underserved market with less governance than CIOs might prefer.

Datamarts in Power BI Premium are a fully managed, self-service, no-code option for up to 100GB of data with workloads automatically optimised for performance and a user interface that looks like Power Query (although advanced users can write DAX or SQL queries). Datamart discovers relationships between tables and generates the dataset, combining the semantic model of Power BI with the relational database model.

"You don't need to know anything about how to be a DBA. We don't ask you about partitioning scheme, or how to create an index," Netz explains. "You don't need to know how to write SQL and to import data or to query. Everything is visual. Everything is easy to use. Everything is designed for the user who knows how to create Power BI reports."

Bonus: Azure Stream Analytics and Azure Time Series Insights

There are new ways to use analytics for which the cloud is particularly well-suited. Traditional analytics focuses on data in databases, but with sensors and IoT devices, you have transient, time-sensitive data that you want to process and take action on almost in real-time. The same is true for the clickstream form web and mobile apps. Azure Stream Analytics enables you to look at the data as it streams in and process it immediately to discover whether you want to take action. This processing wants to be done close to where the data is ingested, so you'd use Event Hubs to collect the data and pass it to Stream Analytics. You can also aggregate data to reduce the amount being stored and query it later to analyze trends or to forecast demand so that you're storing, say, a moving average of the last second rather than recording the temperature every millisecond.

If you don't want to build your own stack for that kind of analytics: Azure Time Series Insights is an end-to-end platform that takes data from IoT devices for you to monitor, analyze, visualize, and act on. You can use it to spot trends, highlight anomalies, and dig into root causes, and because Azure offers a full stack of analytics services, you can feed that data into other services such as Azure Databricks or use it to make models with Azure Machine Learning. But the market is moving away from single-purpose services and Time Series Insights is being deprecated in favour of Azure Data Explorer.

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7 key Microsoft Azure analytics services (plus one extra)

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If you can't make sense of your business data, you're effectively flying blind. Insights hidden in your data are essential for optimizing business operations, finetuning your customer experience, and developing new products - or new lines of business, like predictive maintenance.

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Synapse Analytics is an integrated platform service from Microsoft Azure that combines the capabilities of data warehousing, data integrations, ETL pipelines, analytics tools & services, the scale for big-data capabilities, visualization & dashboards.

Microsoft Azure Synapse Analytics Logical Architecture. Credit: Microsoft Azure

The platform enables enterprise-wide analytics requirements for decision-making. The tools, processes, and techniques support - Analytics capabilities across the dimensions: Descriptive & Diagnostic Analyticsby leveraging its data-warehouse capabilities whereby gathering business insights with the help of T-SQL queries. It empowers an organization's decision-making by leveraging Predictiveand Prescriptive Analytics capabilities using its integration with Apache Spark, Databricks, Stream Analytics.

How it Works

Azure Synapse Analytics is a one-stop-shop analytics solution that offers the following capabilities:

The dedicated-poolof SQL Servers, known as, Synapse SQL, is the backbone for the entire analytics data storage, these provide the necessary infrastructure for implementing a data warehouse under its hood. Enables engineers to execute T-SQL queries native to their existing experience.
It also enables empower with the serverless model is for unplanned or ad-hoc workloads, by using data virtualization allows unlocking insights from their own data stores without going through the formal processes of setting up a data warehouse.

ETL and data integration capabilities from disparate sources using Synapse Pipelines enables the organization to churn the data efficiently for warehousing and analytical purpose. The reusable workflows and Pipeline orchestration capabilities are easy to adapt. The support for big-data compute services such as HDInsight for Hadoop and DataBricks make this a more powerful ETL tool.

Enable the development of big-data workloads and machine learning solutions with Apache Spark for Azure Synapse. This platform processes big data workloads by enabling massively scalable high-performance-compute resources. SparkML algorithms and Azure ML integration make it a complete solution for training machine learning workloads.

Deliver real-time operational analytics from operational data sources using Synapse Link.

Where it fits?

The most common business use-cases for Azure Synapse Analytics are:

Data Warehouse: Ability to integrate with various data platforms and services.

Descriptive/Diagnostic Analytics: Use T-SQL queries against the Synapse database to perform data exploration and discovery.

Realtime Analytics: Azure Synapse Link enables integration with disparate operational data sources to implement real-time analytics solutions.

Advanced Analytics: Uses Azure Databricks to support decision-making by leveraging Azure Databricks.

Reporting & Visualization: Integrate with PowerBI to empower and enhance business decision-making.

Assuming that you have already set up and configured the Azure Synapse Analytics workspace in your Azure subscription.

Setting up and configuration of Azure Synapse Analytics workspace is beyond the scope of this article. Please look into the references section for more details.

Load and Analyze Data using Spark

From Data Hub, Browse the gallery and the covid-tracking dataset.

Open the new Spark notebook using the dataset as below.

The notebook contains the following default code, with further capabilities to analyze the dataset using Spark backbone framework:-

## ---- ##
df = spark.read.parquet(wasbs_path)
display(df.limit(10))

Again, load the sample dataset from the Data Hub, but, this time create a New SQL Script as shown below:

This will give you the free hand script to analyze the data set using native T-SQL queries

Analyze the Data in Serverless SQL Pool

From the hub of the Synapse Analytics Studio select the

Screengrab of a demo - create integration Pipeline. Credit: MS Azure Synapse Analytics Studio

Once the pipeline is instantiated - you can add workflow Activities according to the problem at hand.

Also, create a link to the PowerBI workspace to leverage data visualization and reporting.

You can add trigger conditions to respond to an event or manual execution of the Pipeline workflow. Also, select the Monitor hub, and choose the Pipeline runs to monitor any pipeline execution progress.

Setup and Integrate a Pipeline

This small demo will show to integrate pipeline tasks in Azure Synapse

Integrate Linked Services

Use Synapse Analytics Studio to integrate and enable various Linked Services, e.g. Power BI

In this brief introduction of Azure Synapse Analytics, I have navigated through the shallow waters to understand the basic functional capabilities of this managed service from Azure.

You now understand what is Synapse Analytics, why it is very important to solve optimize organizations' goals, and very slightly about how its capabilities can be leveraged for various analytical use-case.

Hope you have gained some insights, do let me know about your feedback and queries.

[1] Microsoft Documentation | Azure Synapse Analytics

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Synapse Analytics is an integrated platform service from Microsoft Azure that combines the capabilities of data warehousing, data integrations, ETL pipelines, analytics tools & services, the scale for big-data capabilities, visualization & dashboards. The platform enables enterprise-wide analytics requirements for decision-making.