Establish a data-driven culture with Azure Synapse Analytics and Power BI

In our 2nd series of Live webinars, we are plunging into the role data intelligence plays in an enterprise to uncover insights through analytics and BI solutions on Azure. In this article you will learn how to generate actionable business insights at scale with Azure Synapse Analytics and Power BI.

The agenda for this webinar included some important concepts surrounding data integration, modern analytics solutions, and data reporting & visualization.

Key concepts discussed in the webinar:

✓ The paradox of analytics data business
✓ The analytics continuum
✓ Azure synapse analytics
✓ Data integration, data warehouse, SQL on demand
✓ Security, compliance and governance
✓ Power BI with synapse and unmatched combination

Data allows analyzing the past, gaining new insights, predicting the future, and planning. Those that manage, govern and secure data effectively thrive in the new world. It is one thing to gather and manage data, and it’s another to make decisions based on it, and that’s where data analytics comes in.

Using dashboards and data reports provides understanding of a business’s current state. Business Intelligence (BI) offers insights about an organization’s past, present, and future performance, and trends through available business data. Artificial Intelligence (AI) and Machine Learning (ML) enables predictive analytics and provides automated insights at deep scale to make better and faster business decisions. To successfully achieve data analytics capabilities, organization’s need to build a platform that provides them an intelligent decision-making aid.

For instance, take the ever-growing volume of data

Industry analysts expect the current volume of data will quadruple in the next five years. However, only 27% of the data will be analyzed if we continue with today’s rate. Although analytics and AI are top investments for business leaders, data initiatives in these areas can be challenging. 80% organizations struggle to advance their data, 55% blame data silos and management issues as roadblocks, 50% admit that they don’t treat data as a business asset. All these issues are faced due to not having the right capability and solution to process the data the way they want.

More analytics solutions lead to more silos

For decades, specialized technologies like data warehouses and data lakes that handle specific data types have helped data professionals collect and analyze data of all sizes and formats. But this has also created misuse of data technologies and expertise, which lead to growth of data silos, and ultimately led to paradox of analytics. We need to break down the silos without creating new ones and approach analytics holistically to harness the power of data. On top of the data warehouses and data lakes, organizations need technologies for visualizations, reporting, and data science and governance.

When we step back and consider the traditional analytics approaches, building a new system for each new activity made sense at the time, however, as the volume of data and the complexity of systems continue to increase, adding more and more technologies only compounds this problem. And there comes the point where the complexity of adding another analytics technology outweighs the benefits of the technology itself.

Data professionals should not learn new skills continuously to deliver insights

Today, the decision-makers are increasingly reaching beyond traditional reports, visualizations, and dashboards to engage with a new source of data. However, with big data, machine learning, and AI, the more advanced the technology, the more siloed the data gets. Nevertheless, we can understand the direct implications that might be there in the larger picture.

That’s why Microsoft and Motifworks took a new approach with data analytics to enable more holistic experiences for business inside. Microsoft re-architected operational and analytics data stores to take full advantage of the new cloud-native architecture. This fundamental shift while maintaining consistent tools and languages enables the long-handled silos to be eliminated across skills, technologies, and data. So, decision-makers across organizations can better access and understand the insights they need.

Introducing Azure Synapse Analytics

Azure Synapse Analytics is a limitless scale service that combines data integration, data warehousing and big data analytics into a single service offering with unmatched time to insert. Organizations can run whole gamut of analytics projects and put data to work much more quickly, productively, and securely generating insights from all sources. Synapse unifies data, data tasks and teams with analytic service and does it all together.

It provides next-generation query processing data management to meet all the needs of the modern large-scale high, volume velocity variety of workload. And as these workloads require both SQL and Spark skills, Synapse provides a cloud-native analytics service engine that covers both big data and data warehousing so that we can achieve unlimited scale on whether it is semi-structured unstructured or structured data.

Azure Synapse provides a unified experience that combines data ingestion, big data analytics, and warehousing. It simplifies the monotonous but necessary data chores every team must do, such as securing pipelines assigning permissions to users for each service, building firewalls, etc. With the deep integration of Spark and SQL engines, Synapse is enabling a data lakehouse paradigm and at the level of collaboration among data professionals that were previously not even possible.

Cloud-native services become serverless

Cloud-native services are becoming serverless and allowing users to control cost per consumption. For example, Synapse offers serverless and a dedicated response model, giving you more flexibility to better budget your control.

For predictable performance and cost, you can create dedicated SQL pools to reserve processing power for data stored in the SQL table. But if you need to accommodate unplanned or vastly workloads, you can always use available serverless endpoints.

Synapse Studio

Synapse Studio provides a unified workspace for your data prep, data management, data warehousing and big data & AI tasks. A highly visible code-free interface works for everyone. Data engineers can manage data pipelines, administrators can control query optimization, and data scientists can build proofs of concept in minutes. Synopsis Studio is a native experience that ties everything together in one location, so you do not need every task to build a complete solution.

Integrated data governance with Azure Purview

Azure Synapse native integration with the third view allows the implementation of data governance policy. Azure Purview automatically discovers and classifies data assets based on what to set up and provides end-to-end lineage.

Hybrid data integration

Integration might be the biggest problem when it comes to modern analytics. Well, if data is in different silos, how do you connect them? Synapse provides a hybrid approach to data integration that puts everything you need at your fingertips to bring together different data sources.

Whether you prefer to code yourself or want to use a low code or no code interface, you can create and schedule automated event-based triggers that keep your data current. In addition, the low code no code interface has a standard accent-like fee that you can easily use.

Suitable for any scale

Azure Synapse provides an MPP data warehousing capability that carries out massive parallel processing whether you are looking to build a departmental data mart, an organization-level warehouse, or an enterprise-level data warehouse.

Being a Platform-as-a-Service solution, the entire environment is ready to be available for development within hours. The elastic scaling capabilities allow for growth for any storage size or compute needs and drastically reduce the time to market and value cycle.

Workload management

To better manage your resources in cloud, Azure Synapse offers workload management capabilities. You can use the workload management life cycle to better utilize your data warehousing capabilities.

To monitor the workload, you can analyze that specific workload, classify them, and put them in different groups, and configure the systems to need SMA within defined limited hardware. And if you want to understand a little bit more synapse workload management cycle has three main concepts.

The first is classification, mapping a request to a resource allocation and establishing importance.

For example, you can have data load queries versus the reporting queries or add-on queries versus scheduled queries, or it could be based on data sources like sales data versus marketing data. So, you can classify based on all those things, and you can define importance.

Maybe like sales data is critical for you to load, but marketing data that might be coming from your clickstream or web and Google analytics sources can take a lower priority over sales data.

So that’s the classification.

Another essential part of workload management is that it influences the resources. They are talking about CPU, IO memory and locks, which avoids and allows high business value work tasks to win. Depending on that, you can define the high or low importance of your workloads. But, of course, the high workload will always win and get the resources they need on CPU io in memory.

And then, you can use it to create a workload classifier command and define your workload group importance and username. In this way, you can either scale in approach or allow for predictable cost and a simple way of allocating resources or scale out to increase the computing power you need in your database house.

Integrating operational data and analytics systems

Azure Synapse makes it possible to perform real-time operational analytics for faster decision-making and improved response time. Historically it has been nearly impossible to integrate operational data with analytical systems as it requires complex ETL processes and data pipelines to connect the two.

Synapses’ link technology breaks down that barrier that that long existed between operational as well as analytical systems. It links your operational databases to sniffs and analytics in Azure, providing the ability to get immediate insights into your businesses.

As soon as a transaction is recorded in your operational store, that transaction will be available in a columnar format in mere seconds. As a result, you do not need to manage any more complex ETL processes and worry about multiple copies of the data.

This link technology is available using Cosmos DB, and recently Synapse also added this technology for azure SQL database and support in SQL server 2022 version. Microsoft is also working on bringing this technology to other commercial and open-source databases.

Streaming event ingestion

Azure Synapse also supports streaming data injection from IoT devices without aggregation, which supports up to 726 GB per hour of raw data. As a result, you can use it immediately to create rich insights with business data that previously would have been an isolated data lake and data warehouse.

And you can manage across using SQL language. You do not need to know any language to do this. And to complement our existing SQL and spark engines, Synapse provides a data explorer engine that is now part of the ecosystem that you can run your telemetry analytics seamlessly on this data.

Machine Learning

So now, data engineers and scientists can collaborate seamlessly using code free of the course first experience and train their machine learning model in synapse powered by your machine learning.

Azure Notebook brings collaborative code authoring and experience for predictive complex predictive modelling. It also allows automated machine learning, which takes minutes to set up in Synapse versus many days or months that you can iterate rapidly for more significant insights. This enables a code-first or code-free approach using whether autonomy engine or notebook capabilities.

Where do you find yourself on the curve?

Earlier, we talked about the continuum of analytics regarding technical capabilities. However, we can also think about analytics in a spectrum of time. Are you answering the questions about what happened and why did it happen? Can you predict what will happen and what actions you will take to prevent it?

Think about where your organization is on this curve. As you incorporate more data faster and bring BI analytics closer, you create insights that allow you to anticipate and react to events. Azure Synapse automatically takes you to the next natural step in your analytics journey by natively integrating azure machine learning and Power BI service.

You can use Power BI directly through Synapse Studio. The new Power BI performance accelerator for Synapse automatically automates the creation and optimization of materialized views resulting in lightning-fast query performance. In addition, it simplifies and strengthens the collaboration between your data analyst and data administrator as they can see how to use the same analytics service for querying the data and building reports and dashboards.

Accelerate business value with the robust analytics platform

Power BI and Azure Synapse unlocks the value of big data in an impossible way. It is not uncommon for companies today to have large amount of data across the organization. Analyzing and extracting insight from such massive amounts of data took hours, if not days, and with Power BI and Azure synapse it has changed.

For example, with the composite model introduced recently, you can build your Power BI data sets which go against the direct query for a large amount of data. You can also run some queries in import mode to get that faster performance, which is the composite model’s idea. Moreover, it extends to another capability called user-defined aggregations.

So, you can define aggregates of the data that run in input mode. In contrast, as you try to drill down to the lower-level data, it automatically goes to the direct query mode to get the data from synapse using those large tables that might contain billions or billions of those. But as you build your Power BI data set, you need to refresh those.

In those queries which are running in import mode, that’s where that’s another feature like incremental refresh adds another value to Power BI matched with Azure Synapse. It allows a use to incrementally refresh your data instead of working and refreshing the data set from scratch.

The concept of a hybrid table allows you to run insights on the same table that might have some data running across any port mode. Some data comes from direct query mode, and some data comes from this incremental refresh mode.

So those are the concept of the hybrid table that is only possible with technologies like Azure Synapse working with Power BI and automated aggregates in another feature, as the queries are running from Power BI on indirect query mode.

Azure Synapse analytics automatically understands and scans those queries and identifies the materialized views that can experience the query performance. Then, it automatically creates those aggregations and keeps them using the performance accelerator feature.

Query fusion is another capability added to fuse the query or get the results faster from Azure Synapse in Power BI. These are the new features that make this combination of Azure synapse analytics and Power BI a truly intelligent analytics solution.