In today’s data-driven era, organizations expect more than static dashboards or descriptive analytics. They demand forecasts, predictive insights, and intelligent decision-making support. Traditionally, delivering this requires piecing together multiple tools, data lakes for storage, notebooks for model training, separate platforms for deployment, and BI tools for visualization.
Microsoft Fabric reimagines this workflow. It brings every stage of the machine learning lifecycle, from data ingestion and preparation to model training, deployment, and visualization, into a single, governed environment. In this blog, we’ll explore how Microsoft Fabric empowers data scientists to streamline the end-to-end ML process and unlock predictive intelligence at scale.
Why Choose Microsoft Fabric for Modern Data Science Workflows?
End-to-End Unification
One platform for data ingestion, preparation, model training, deployment, and data visualization. A wide range of activities are offered in Microsoft Fabric across the entire data science process, empowering users to build end-to-end data science workflows within a single platform.
Scalability
Spark-based distributed compute, enabling seamless handling of large datasets and complex machine learning models. With built-in support for Apache Spark in Microsoft Fabric, you can utilize the efficiency of Spark through Spark batch job definitions or with interactive Fabric notebooks.
MLflow integration
Allows autologging runs, metrics, and parameters for easy comparison of different models and experiments without requiring manual tracking.
AutoML (low-code)
With Fabric’s low-code AutoML interface, users can easily get started with machine learning tasks, while the platform automates most of the workflow with minimal manual effort.
AI-powered Copilot
With AI support in Microsoft Fabric, it saves time and effort for data scientist and makes data science accessible to everyone. It offers helpful suggestions, assists in writing and fixing code, and helps you analyse and visualize data.
Governance & Compliance
Features like role-based access, lineage tracking, and model versioning in Microsoft Fabric enable teams to reproduce models, trace issues efficiently, and maintain full transparency across the data science lifecycle.
Advanced Machine Learning Lifecycle in Microsoft Fabric
Microsoft Fabric offers capabilities to support every step of the machine learning lifecycle in one governed environment. Let’s explore how each step is supported by powerful features in Fabric:
source: learn.microsoft.com
1. Data Ingestion & Exploration
OneLake acts as the single source of truth, storing all data in Delta format with support for versioning, schema evolution, and ACID transactions. Fabric is standardized on Delta Lake which means all Fabric engines can interact with the same dataset stored in a Lakehouse. This eliminates the overhead of managing separate data lakes and warehouses.
Fabric notebooks with Spark pools provide distributed compute for profiling, visualization, and correlations at scale.
Lakehouse: Fabric notebooks allow you to ingest data from various sources, such as Lakehouse, Data Warehouses or Semantic mode. You can simply store your data in Lakehouse that can be attached to the Notebook and then you can read or write to this Lakehouse using a local path in your Notebook.
Environments: You can create an environment and enable it for multiple notebooks. It ensures reproducibility by packaging runtimes, libraries, and dependencies.
Pandas on Spark lets data scientists apply familiar syntax while scaling workloads across Spark clusters to prepare data for training. You can perform data profiling and visualization efficiently on large amount of data.
Data Wrangler offers an interactive interface to impute missing values, and with GenAI in Data Wrangler, reusable PySpark code is generated for auditability. It also gives you AI-powered suggestions to apply transformations.
Feature Engineering can also be easily performed using Data Wrangler. It offers direct options to perform encoding and normalize features without requiring you to write any code.
Copilot integration accelerates preprocessing with AI-powered suggestions and code generation.
Processed features can be written back into OneLake as Delta tables, sharable across projects and teams.
MLFlow Autologging can be enabled so that it automatically captures the values of input parameters and output metrics of a machine learning model as it is being trained. This information is then logged to your workspace, where it can be accessed and visualized using the MLflow APIs or the corresponding experiment in your workspace, reducing manual effort and ensuring consistency.
Frameworks: Choose Spark MLlib for distributed training, scikit-learn or XGBoost for tabular tasks, or PyTorch/TensorFlow for deep learning.
Hyperparameter tuning: The FLAML library supports lightweight, cost-efficient tuning strategies. SynapseML, a distributed machine learning library can also be used in Microsoft Fabric Notebooks to identify the best combination of hyperparameters
Experiments & Runs: Microsoft Fabric integrates MLflow for experiment tracking.
Within Experiment, there is a collection of runs for simplified tracking and comparison. Data scientists can compare those runs to select the model with best performing parameters. Runs can be visualized, searched, and compared, with full metadata available for export or further analysis.
Model versioning; model run Iterations can be registered with tags and metadata, providing traceability and governance across versions.
AutoML; a low-code interface generates preconfigured notebooks for tasks like classification, regression, or forecasting. It performs all the Machine Learning steps automatically from data transformation, model definition to training. These notebooks also leverage MLflow logging to capture parameters and metrics automatically. Therefore, completely automating the Machine Learning lifecycle.
4. Model Evaluation & Selection
Notebook visualizations such as ROC curves, confusion matrix, and regression error plots provide immediate insights.
Experiment dashboards make it simple to compare models’ side-by-side, highlighting the best-performing candidate.
PREDICT function can be used during evaluation to generate test predictions at scale. You can use this function to generate batch predictions directly from a Microsoft Fabric notebook or from the item page of a given ML model.
You can simply select the specific model version you need to score and copy generated code template into a notebook and customize the parameters yourself.
Another way is to use the GUI experience to generate PREDICTcode by selecting ‘apply this model to wizard’.
Power BI integration makes predictions stored in OneLake available to analysts with no extra data movement.
Direct Lake mode ensures low latency querying of large Delta tables, keeping dashboards fast and responsive even at enterprise scale.
Semantic Link is a feature that allows you to establish a connection between semantic models and Synapse Data Science in Microsoft Fabric. Through the Semantic link (preview), data scientists can use PowerBI sematic models in Notebooks using the SemPy Python library or Spark (in Python, R, SQL, and Scala) to perform tasks such as in-depth statistical analysis and predictive modelling with machine learning. The output data can then be stored in the OneLake which can be used by PowerBI.
source: learn.microsoft.com
6. Monitoring & Control
Models are assets that require governance and continuous maintenance.
Automated retraining pipelines can be triggered on a schedule or in response to specific metric drop.
Versioning and lineage tracking make it clear which combination of data, code, and parameters produced any given model and the dependency of each ML item.
Machine learning experiments and models are integrated with the lifecycle management capabilities in Microsoft Fabric.
Microsoft Fabric deployment pipeline can track ML artifacts across development, test, and production workspaces while preserving experiment runs and model versions. Metadata, Lineage between notebooks, experiments, and models is maintained.
In Microsoft Fabric, ML experiments and models are also synced via Git Integration, but experiment runs, and model versions remain in workspace storage and aren’t versioned in Git. Git tracks only artifact metadata, not data. which includes display name, version, and dependencies. Lineage between notebooks, experiments, and models is preserved across Git-connected workspaces, ensuring traceability.
Access controls in Fabric provide fine-grained permissions for models, experiments, and workspaces, ensuring responsible collaboration. You can grant controlled access to teams to access the items and data that is useful only for their department context.
Beyond ML: Other Data Science Capabilities in Microsoft Fabric
Besides ML workflows, Fabric also empowers organizations to build AI-driven solutions:
Data Agents: A newly introduced feature, Data Agents let you create conversational Q&A systems tailored to your organization’s data in OneLake. They are powered by Azure OpenAI Assistant APIs, and can access multiple sources such as Lakehouse, Warehouse, Power BI datasets, and KQL databases. You can customize them with specific instructions, and examples, so they align with organizational needs. The process is iterative: as you refine performance, you can publish the agent, generating a read-only version to share across teams.
source: learn.microsoft.com
LLM-powered Applications: Fabric integrates seamlessly with Azure OpenAIService and SynapseML, making it possible to run large-scale natural language workflows directly on Spark. Instead of handling prompts one by one, Fabric enables distributed processing of millions of prompts in parallel. This makes it practical to deploy LLMs for enterprise-scale use cases such as summarization, classification, and question answering.
Conclusion: Unlocking Predictive Intelligence with Fabric
Microsoft Fabric isn’t just another data platform, it’s a game-changer for data science teams. By eliminating silos between storage, experimentation, deployment, and visualization, Fabric empowers organizations to move faster from raw data to business impact. Whether you’re a data scientist building custom models or an analyst looking to leverage interactive, Fabric provides the tools to scale predictive insights across your enterprise.
The future of data science is unified, governed, and intelligent, and Microsoft Fabric is paving the way.
In many enterprise scenarios, SharePoint-hosted Excel files serve as the bridge between raw data and business operations. But keeping them up to date, especially when your data lives in Azure Synapse, can be surprisingly difficult due to limitations in native connectors.
In this guide, you’ll learn a step-by-step method to build a no-code/low-code Azure Synapse to SharePoint Excel automation using Power BI and Power Automate. This method ensures your data is always up-to-date, with zero manual refreshes.
The Business Problem
Recently, I faced a real-world challenge:
A client needed a solution that automatically updates an Excel workbook on SharePoint with data from an Azure Synapse pipeline, as the Excel file was being used as a data source for Smartsheet reports.
The critical requirement?
End-to-end automation with no manual intervention ever.
That meant the Excel workbook needed to be continuously and reliably updated with data from an Azure Synapse view, without anyone having to open or refresh the file manually.
Key Challenges
While the problem sounded simple, setting up direct integration between Azure Synapse and Excel on SharePoint revealed several roadblocks:
No SharePoint Excel connector in Azure Synapse.
Synapse lacks a native way to push or refresh data directly into an Excel file on SharePoint.
SharePoint Excel doesn’t support direct refresh from SQL Server or Synapse.
You can’t natively connect an Excel file on SharePoint to a SQL-based backend and have it auto-refresh.
Even if connected to a Power BI semantic model, Excel doesn’t auto-refresh.
SharePoint Excel can connect to a Power BI dataset (semantic model), but it won’t pull the latest data unless manually refreshed, a blocker for our automation goal.
To build a robust Azure Synapse to SharePoint Excel automation, I developed a no-code/low-code automation using a combination of:
Azure Synapse Analytics (as the data source)
Power BI Semantic Model (as the bridge)
Power Automate (to refresh Excel connections on SharePoint)
This approach keeps the SharePoint Excel workbook continuously in sync with Synapse, enabling downstream use in Smartsheet.
Step-by-Step Implementation
Here’s how you can replicate this approach:
Create a Power BI Semantic Model
In Power BI Desktop, create a dataset that pulls data from your Azure Synapse or SQL Server view/table.
This model will act as the source for the Excel file.
Publish the Model to Power BI Service
Publish the semantic model to your workspace in the Power BI Service.
Set Up Power BI Semantic Model Refresh
Configure a Power BI Service to refresh the semantic model on a schedule (e.g., hourly/daily).
This ensures the model always reflects the latest data from Synapse.
Create the Excel File in SharePoint
In the target SharePoint location, create or upload a new Excel workbook.
Inside the workbook, go to Data > Data from Power BI and connect to your semantic model.
Add an Office Script to Refresh Connections
In Excel Online, go to Auomate Tab and create a new Office Script with the following code:
Name the script something like Refresh All.
Automate It with Power Automate
Create a new Power Automate flow.
Add a Recurrence trigger to define how often it should run.
Add the “Run Script” action.
Specify the SharePoint file location and the Refresh All script you created.
Coordinating Refresh Timings and Triggers
Timing and synchronization are critical to avoid partial or stale data. Here’s how each component should be aligned:
Azure Synapse: Scheduled View/ETL Triggers
Use AzureSynapse Pipelines with scheduled triggers to refresh your views or underlying datasets.
If you’re using serverless SQL views, ensure the logic behind them is updated and ready before the Power BI gateway refresh runs.
Power BI Gateway: Semantic Model Refresh
Schedule your Power BI gateway refresh to run after your Synapse views have completed refreshing.
This ensures that the semantic model reflects the latest data before Excel attempts to pull it.
Power Automate: Excel Workbook Refresh
Schedule the Power Automate recurrence trigger to run after the Power BI semantic model refresh completes.
Important: Always provide a safe buffer time (e.g., 5–10 minutes) between Power BI refresh and the Excel refresh via Power Automate to avoid syncing stale or partially updated data.
Example Timing Setup:
Azure Synapse pipeline runs at 2:00 AM
Power BI semantic model refreshes at 2:15 AM
Power Automate script runs at 2:45 AM
This sequencing ensures data consistency across all layers.
What Happens End to End
Synapse updates or refreshes the data in the SQL views.
Power BI semantic model (connected to Azure Synapse) is refreshed via scheduled refresh.
SharePoint Excel workbook, connected to that semantic model, is refreshed by a scheduled Power Automate flow running an Office Script.
Smartsheet, connected to the Excel workbook, always sees the most up-to-date data, fully automated.
Example Use Case: Automating Sales Reporting for Smartsheet Dashboards
Scenario:
A sales operations team needs daily reports in Smartsheet, which relies on data pulled from an Excel workbook stored in SharePoint. This Excel file should reflect the latest sales transaction data from Azure Synapse Analytics.
Solution Implementation:
Sales data is stored in Synapse view, updated nightly via a Synapse pipeline.
A Power BI semantic model is created on top of this view and refreshed every morning.
The Excel workbook in SharePoint connects to the Power BI model.
A Power Automate flow runs an Office Script daily to refresh all data connections in Excel.
The updated Excel file feeds into Smartsheet automatically, keeping dashboards current, no manual work required.
This use case demonstrates how the automation flow ensures accurate, up-to-date reporting without any manual intervention, even though Synapse cannot natively write to SharePoint Excel.
Conclusion
If you’re trying to output Azure Synapse data into an Excel file stored in SharePoint, and need that file to stay in sync automatically, this is your workaround. While there’s no direct connector from Synapse to SharePoint Excel, Power BI + Power Automate fill the gap with a reliable and reusable pattern.
Imagine effortlessly asking your business intelligence dashboard any question and receiving instant, insightful answers. This is not a futuristic concept but a reality unfolding through the power of Large Language Models (LLMs).
Descriptive analytics is at the core of this transformation, turning raw data into comprehensible narratives. When combined with the advanced capabilities of LLMs, Business Intelligence (BI) dashboards evolve from static displays of numbers into dynamic tools that drive strategic decision-making.
LLMs are changing the way we interact with data. These advanced AI models excel in natural language processing (NLP) and understanding, making them invaluable for enhancing descriptive analytics in Business Intelligence (BI) dashboards.
In this blog, we will explore the power of LLMs in enhancing descriptive analytics and its impact of business intelligence dashboards.
Understanding Descriptive Analytics
Descriptive analytics is the most basic and common type of analytics that focuses on describing, summarizing, and interpreting historical data.
Companies use descriptive analytics to summarize and highlight patterns in current and historical data, enabling them to make sense of vast amounts of raw data to answer the question, “What happened?” through data aggregation and data visualization techniques.
The Evolution of Dashboards: From Static to LLM
Initially, the dashboards served as simplified visual aids, offering a basic overview of key metrics amidst cumbersome and text-heavy reports.
However, as businesses began to demand real-time insights and more nuanced data analysis, the static nature of these dashboards became a limiting factor forcing them to evolve into dynamic, interactive tools. The dashboards transformed into Self-service BI tools with drag-drop functionalities and increased focus on interactive user-friendly visualization.
This is not it, with the realization of increasing data, Business Intelligence (BI) dashboards shifted to cloud-based mobile platforms, facilitating integration to various data sources, and allowing remote collaboration. Finally, the Business Intelligence (BI) dashboard integration with LLMs has unlocked the wonderful potential of analytics.
Role of Descriptive Analytics in Business Intelligence Dashboards and its Limitations
Despite of these shifts, the analysis of dashboards before LLMs remained limited in its ability to provide contextual insights and advanced data interpretations, offering a retrospective view of business performance without predictive or prescriptive capabilities.
The following are the basic capabilities of descriptive analytics:
Defining Visualization
Descriptive analytics explains visualizations like charts, graphs, and tables, helping users quickly grasp key insights. However, this requires manually describing the analyzed insights derived from SQL queries, requiring analytics expertise and knowledge of SQL.
Trend Analysis
By identifying patterns over time, descriptive analytics helps businesses understand historical performance and predict future trends, making it critical for strategic planning and decision-making.
However, traditional analysis of Business Intelligence (BI) dashboards may struggle to identify intricate patterns within vast datasets, providing inaccurate results that can critically impact business decisions.
Reports developed through descriptive analytics summarize business performance. These reports are essential for documenting and communicating insights across the organization.
However, extracting insights from dashboards and presenting them in an understandable format can take time and is prone to human error, particularly when dealing with large volumes of data.
LLMs: A Game-Changer for Business Intelligence Dashboards
Advanced Query Handling
Imagine you would want to know “What were the top-selling products last quarter?” Conventionally, data analysts would write an SQL query, or create a report in a Business Intelligence (BI) tool to find the answer. Wouldn’t it be easier to ask those questions in natural language?
LLMs enable users to interact with dashboards using natural language queries. This innovation acts as a bridge between natural language and complex SQL queries, enabling users to engage in a dialogue, ask follow-up questions, and delve deeper into specific aspects of the data.
Improved Visualization Descriptions
Advanced Business Intelligence (BI) tools integrated with LLMs offer natural language interaction and automatic summarization of key findings. They can automatically generate narrative summaries, identify trends, and answer questions for complex data sets, offering a comprehensive view of business operations and trends without any hustle and minimal effort.
With the integration of a domain-specific Large Language Model (LLM), dashboard analysis can be expanded to offer predictive insights enabling organizations to leverage data-driven decision-making, optimize outcomes, and gain a competitive edge.
Dashboards supported by Large Language Mode (LLMs) utilize historical data and statistical methods to forecast future events. Hence, descriptive analytics goes beyond “what happened” to “what happens next.”
Prescriptive Insights
Beyond prediction, descriptive analytics powered by LLMs can also offer prescriptive recommendations, moving from “what happens next” to “what to do next.” By considering numerous factors, preferences, and constraints, LLMs can recommend optimal actions to achieve desired outcomes.
The Copilot integration in Power BI offers advanced Business Intelligence (BI) capabilities, allowing you to ask Copilot for summaries, insights, and questions about visuals in natural language. Power BI has truly paved the way for unparalleled data discovery from uncovering insights to highlighting key metrics with the power of Generative AI.
Here is how you can get started using Power BI with Copilot integration;
Step 1
Open Power BI. Create workspace (To use Copilot, you need to select a workspace that uses a Power BI Premium per capacity, or a paid Microsoft Fabric capacity).
Step 2
Upload your business data from various sources. You may need to clean and transform your data as well to gain better insights. For example, a sample ‘sales data for hotels and resorts’ is used here.
Uploading data
Step 3
Use Copilot to unleash the potential insights of your data.
Start by creating reports in the Power BI service/Desktop. Copilot allows the creation of insightful reports for descriptive analytics by just using the requirements that you can provide in natural language.
For example: Here a report is created by using the following prompt:
An example of a report creation prompt using Microsoft Copilot – Source: Copilot in Power BI Demo
Copilot has created a report for the customer profile that includes the requested charts and slicers and is also fully interactive, providing options to conveniently adjust the outputs as needed.
An example of a Power BI report created using Microsoft Copilot – Source: Copilot in Power BI Demo
Not only this, but you can also ask analysis questions about the reports as explained below.
An example of asking analysis question from Microsoft Copilot – Source: Copilot in Power BI Demo
The copilot now responds by adding a new page to the report. It explains the ‘main drivers for repeat customer visits’ by using advanced analysis capabilities to find key influencers for variables in the data. As a result, it can be seen that the ‘Purchased Spa’ service has the biggest influence on customer returns followed ‘Rented Sports Equipment’ service.
An example of asking analysis questions from Microsoft Copilot – Source: Copilot in Power BI Demo
Moreover, you can ask to include, exclude, or summarize any visuals or pages in the generated reports. Other than generating reports, you can even refer to your existing dashboard to question or summarize the insights or to quickly create a narrative for any part of the report using Copilot.
Below you can see how the Copilot has generated a fully dynamic narrative summary for the report, highlighting the useful insights from data along with proper citation from where within the report the data was taken.
An example of narrative generation by Microsoft Power BI Copilot – Source: Copilot in Power BI Demo
Microsoft Copilot simplifies Data Analysis Expressions (DAX) formulas by generating and editing these complex formulas. In Power BI, you can easily navigate to the ‘Quick Measure’ button in the calculations section of the Home tab. (if you do not see ‘suggestions with Copilot,’ then you may enable it from settings.
Otherwise, you may need to get it enabled by your Power BI Administrator).
Quick measures are predefined measures, eliminating the need for creating your own DAX syntax. It’s generated automatically according to the input you provide in Natural Language via the dialog box. They execute a series of DAX commands in the background and display the outcomes for utilization in your report.
Quick Measure – Suggestions with Copilot
In the below example, it can be seen that the copilot gives suggestion for a quick measure based on the data, generating the DAX formula as well. If you find the suggested measure satisfactory, you can simply click the “Add” button to seamlessly incorporate it into your model.
An example of DAX generation using Quick Measure – Source: Microsoft Learn
There can be several other things that you can do with copilot with clear and understandable prompts to questions about your data and generate more insightful reports for your BI dashboards.
Hence, we can say that Power BI with Copilot has proven to be the transformative force in the landscape of data analytics, reshaping how businesses leverage their data’s potential.
Embracing the LLM-led Era in Business Intelligence
Descriptive analytics is fundamental to Business Intelligence (BI) dashboards, providing essential insights through data aggregation, visualization, trend analysis, and reporting.
The integration of Large Language Models enhances these capabilities by enabling advanced query handling, improving visualization descriptions, and reporting, and offering predictive and prescriptive insights.
This new LLM-led era in Business Intelligence (BI) is transforming the dynamic landscape of data analytics, offering a glimpse into a future where data-driven insights empower organizations to make informed decisions and gain a competitive edge.
Data Science Dojo is offering Apache Superset for FREE on Azure Marketplace packaged with pre-installed SQL lab and interactive visualizations to get started.
What is Business Intelligence?
Business Intelligence (BI) depends on the idea of utilizing information to perform activities. It expects to give business pioneers noteworthy bits of knowledge through data handling and analytics. For instance, a business breaks down the KPIs (Key Performance Indicators) to distinguish its benefits and shortcomings. Hence, the decision-makers can conclude in which department the organization can work to increase efficiency.
Recently two elements in BI have resulted in sensational enhancements in metrics like speed and proficiency. The two elements include:
Automation
Data Visualization
Apache Superset widely focuses on the latter model which has changed the course of business insights.
But what were the challenges faced by analysts before there were popular exploratory tools like Superset?
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Challenges of Data Analysts
Scalability, framework compatibility, and absence of business-explicit customization were a few challenges faced by data analysts. Apart from that exploring petabytes of data and visualizing it would cause the system to collapse or hang at times.
In these circumstances, a tool having the ability to query data as per business needs and envision it in various diagrams and plots was required. Additionally, a system scalable and elastic enough to handle and explore large volumes of data would be an ideal solution.
Data Analytics with Superset
Apache Superset is an open-source tool that equips you with a web-based environment for interactive data analytics, visualization, and exploration. It provides a vast collection of different types of vibrant and interactive visualizations, charts, and tables. It can customize the layouts and the dynamic dashboard elements along with quick filtering, making it flexible and user-friendly. Apache Superset is extremely beneficial for businesses and researchers who want to identify key trends and patterns from raw data to aid in the decision-making process.
Video Game Sales Analytics with different visualizations
It is a powerhouse of SQL as it not only allows connection to several databases but also provides an in-browser SQL editor by the name SQL Lab
SQL Lab: an in-browser powerful SQL editor pre-configured for faster querying
Key attributes
Superset delivers an interactive UI that enriches the plots, charts, and other diagrams. You can customize your dashboard and canvas as per requirement. The hover feature and side-by-side layout make it coherent
An open-source easy-to-use tool with a no-code environment. Drag and drop and one-click alterations make it more user-friendly
Contains a powerful built-in SQL editor to query data from any database quickly
The choice to select from various databases like Druid, Hive, MySQL, SparkSQL, etc., and the ability to connect additional databases makes Superset flexible and adaptable
In-built functionality to create alerts and notifications by setting specific conditions at a particular schedule
Superset provides a section about managing different users and their roles and permissions. It also has a tab for logging the ongoing events
What does Data Science Dojo have for you
Superset instance packaged by Data Science Dojo serves as a web-accessible no-code environment with miscellaneous analysis capabilities without the burden of installation. It has many samples of chart and dataset projects to get started. In our service users can customize dashboards and canvas as per business needs.
It comes with drag-and-drop feasibility which makes it user-friendly and easy to use. Users can create different visualizations to detect key trends in any volume of data.
What is included in this offer:
A VM configured with a web-accessible Superset application
Many sample charts and datasets to get started
In-browser optimized SQL editor called SQL Lab
User access and roles manager
Alert and report feature
Feasibility of drag and drop
In-build functionality of event logging
Our instance supports the following major databases:
Druid
Hive
SparkSQL
MySQL
PostgreSQL
Presto
Oracle
SQLite
Trino
Apart from these any data engine that has Python DB-API driver and a SQL Alchemy dialect can be connected
Conclusion
Efficient resource requirement for exploring and visualizing large volumes of data was one of the areas of concern when working on traditional desktop environments. The other area of concern includes the ad-hoc SQL querying of data from different database connections. With our Superset instance, both concerns are put to rest.
When coupled with Microsoft cloud services and processing speed, it outperforms its traditional counterparts since data-intensive computations aren’t performed locally but in the cloud. It has a lightweight semantic layer and is designed as a cloud-native architecture.
At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free Superset instance dedicated specifically to Data Science & Analytics on Azure Marketplace. Now hurry up and avail this offer by Data Science Dojo, your ideal companion in your journey to learn data science!
Click on the button below to head over to the Azure Marketplace and deploy Apache Superset for FREE by clicking on “Get it now”.
Note: You’ll have to sign up to Azure, for free, if you do not have an existing account.
