Monolith vs Microservices: Choosing Your Architectural Champion
Introduction
In the ever-evolving landscape of software development, a constant quest for the optimal architecture persists. Two prominent contenders vie for dominance: the monolithic champion and the legion of microservices. Today, we delve into the “Monolith vs Microservices” battleground, meticulously dissecting the strengths and weaknesses of each approach. Through this exploration, we aim to empower you to choose the architectural champion that best suits your next project.
Understanding Monoliths
Visualize a majestic cathedral – a monolithic structure, unified and self-contained. Similarly, a monolithic application is a single, unified codebase encompassing all functionalities. This approach offers familiarity, ease of understanding, and advantages like swift development and streamlined deployments. However, as the application matures and its complexity grows, maintaining and scaling a monolith becomes cumbersome. Changes in one area can have ripple effects throughout the entire codebase, hindering agility and innovation.
The Rise of Microservices
Microservices, the antithesis of monoliths, represent a distributed system architecture. Here, independent services, each with a well-defined purpose, collaborate through APIs. Imagine a well-rehearsed orchestra, where each instrument plays its part to create a harmonious whole. Microservices offer the benefits of modularity, facilitating independent development, deployment, and scaling of functionalities. This fosters agility, flexibility, and technology independence. However, the microservices approach introduces complexities like distributed system management, increased infrastructure demands, and potential challenges in debugging and monitoring.
The Monolith’s Strengths
Simplicity: Monoliths reign supreme in terms of initial development, testing, and deployment. Everything resides within a single codebase, making it a compelling choice for smaller projects or those with well-defined scopes.
Performance: Due to the absence of network overhead involved in microservice communication, monolithic applications can exhibit faster performance, especially for straightforward operations.
Centralized Data: Data management becomes a simpler endeavor in a monolith, as everything resides within a single database. This simplifies data access and consistency management.
The Monolith’s Weaknesses
Scalability Challenges: Scaling a monolithic application becomes increasingly difficult as its size and complexity balloon. Changes in one area necessitate modifications to the entire codebase, leading to intricate deployments that can disrupt application functionality.
Limited Agility: Development speed can become sluggish with a large, monolithic codebase. Introducing new features or modifying existing ones can be time-consuming due to potential dependencies across the entire application. This hinders innovation and responsiveness to changing market demands.
Technology Lock-in: The entire application is tethered to the chosen technology stack. Shifting to a new technology necessitates rewriting significant portions of the code, a time-consuming and resource-intensive undertaking.
The Microservices’ Strengths
Scalability and Agility: Microservices excel at scaling individual functionalities. Need to handle a surge in user traffic for a specific feature? Simply scale the corresponding microservice. Development flourishes in an agile environment, as teams can work on independent services without impacting others. This fosters faster development cycles and quicker time-to-market for new features.
Technology Independence: Different microservices can leverage distinct technologies, fostering innovation and allowing teams to choose the best tool for the job. This empowers developers to utilize the latest advancements without being constrained by the limitations of a single technology stack.
Fault Isolation: Issues within one microservice are less likely to cascade and cripple the entire system. This improves reliability and simplifies troubleshooting efforts, as developers can pinpoint the source of problems more efficiently.
The Microservices’ Weaknesses
Increased Complexity: Distributed systems introduce inherent complexities. Network communication management, service discovery, and distributed tracing become essential considerations. Debugging and monitoring become more intricate, requiring additional tooling and expertise.
Infrastructure Overhead: Running and managing numerous independent services necessitates additional infrastructure resources compared to a monolith. This can translate to higher operational costs.
Development Overhead: Implementing APIs, communication protocols, and distributed system management adds complexity to the development process compared to a monolithic approach. The initial investment in setting up the infrastructure and tooling for microservices can be significant.
Conclusion
There’s no single victor in the Monolith vs Microservices battle. The ideal choice hinges on your project’s specific requirements. Consider factors like application size, desired scalability, development team structure, and technology needs. For smaller projects with a well-defined scope and a focus on rapid development, a monolith might be a good fit. However, for complex, evolving applications with a large user base and a need for continuous innovation, microservices offer greater flexibility and scalability.
The real world rarely adheres to strict binaries. Hybrid approaches are gaining traction. You can begin with a monolith for core functionalities and gradually migrate specific features to microservices as needed. This allows you to leverage the strengths of both approaches and tailor your architecture to your project’s specific requirements.
Ultimately, the goal is not to crown a single champion. It’s about understanding the strengths and weaknesses of each architectural approach and making an informed decision that best empowers your team to develop, deploy, and maintain a successful application.
Osama Saeed
Junior Consultant
Modern Data Warehouse with Azure Synapse Analytics
Before the availability of Azure Synapse Analytics:
Before the availability of Azure Synapse Analytics, data warehousing on Azure typically involved multiple services working together.
Ingest:
Azure Data Factory is a superior tool when it comes to data integration. It provides connectors to 90 plus sources ranging from On-premises, multi-cloud and software as a service application.
Store:
Azure Data Lake Storage Gen2 is undoubtedly the best choice with its hierarchical namespace, low cost and better security.
Prep & Train:
- ADF Data Flows
(ADF data flows gives us code free development capability. But they lack the ability to process complex data structures and unstructured data.) - Azure Databricks (Databricks with its spark notebooks is a great choice for processing complex data structures and unstructured data.)
Model & Server:
Azure SQL data warehouse is a massively parallel processing engine which can handle large volumes of data, suitable for handling large scale analytic workload.
Visualize:
Use Power BI to visualize the data to gain insights.
Issues with this Architecture:
While effective, this architecture has several drawbacks:
- Multiple Interfaces: Developers must navigate through multiple tabs and workspaces to monitor and manage these services.
- Integration Challenges: There is a lot of management in terms of being able to get these services to talk to each other and set up security.
- Provisioned Resources: All compute resources are provisioned, meaning they take time to become available and incur charges while running.
- Lack of Serverless Query Engine: This solution lacks a serverless query engine. The three compute engines (ADF Data Flows, Azure Databricks, and Azure SQL Data Warehouse) do not share metadata, resulting in isolated operations. For example, Spark tables created in Databricks are not directly accessible by Azure SQL Data Warehouse.
Microsoft’s solution to this problem is Azure Synapse Analytics
Ingest:
Azure Data Factory is a superior tool when it comes to data integration. It provides connectors to 90 plus sources ranging from On-premises, multi-cloud and software as a service application.
Store:
Azure Data Lake Storage Gen2 is undoubtedly the best choice with its hierarchical namespace, low cost and better security.
Prep & Train:
- ADF Data Flows
(ADF data flows gives us code free development capability. But they lack the ability to process complex data structures and unstructured data.) - Azure Databricks (Databricks with its spark notebooks is a great choice for processing complex data structures and unstructured data.)
Model & Server:
Azure SQL data warehouse is a massively parallel processing engine which can handle large volumes of data, suitable for handling large scale analytic workload.
Visualize:
Use Power BI to visualize the data to gain insights.
Issues with this Architecture:
While effective, this architecture has several drawbacks:
- Multiple Interfaces: Developers must navigate through multiple tabs and workspaces to monitor and manage these services.
- Integration Challenges: There is a lot of management in terms of being able to get these services to talk to each other and set up security.
- Provisioned Resources: All compute resources are provisioned, meaning they take time to become available and incur charges while running.
- Lack of Serverless Query Engine: This solution lacks a serverless query engine. The three compute engines (ADF Data Flows, Azure Databricks, and Azure SQL Data Warehouse) do not share metadata, resulting in isolated operations. For example, Spark tables created in Databricks are not directly accessible by Azure SQL Data Warehouse.
Microsoft’s solution to this problem is Azure Synapse Analytics
Azure Synapse Analytics addresses these challenges by integrating the above components into a unified platform. Here’s how it transforms the data warehousing landscape:
Data Integration:
Within Synapse, instead of Azure Data Factory you’ll see Synapse Pipelines. (Both are similar)
Compute:
- Synapse Data Flows:
- ADF mapping data flow in synapse are called Synapse Data flows.(Both provide the same functionality)
- Spark Pool:
- Instead of Databricks, there is a Spark Pool within Synapse to perform big data analytics.
- This is a new service rather than renaming Databricks to Spark. In this case, Microsoft has taken the Vanilla Apache Spark and Delta Lake and then built this service from the ground up. So, we won’t have the optimized version of Spark and the Delta Lake that comes with Databricks instead, you get the Vanilla Spark.
- Dedicated SQL Pool:
- Dedicated SQL Pool is a provisioned resource that stores data in a relational table format with columnar storage. It also uses a massively parallel processing or MPP Architecture to leverage up to 60 nodes to run your queries in parallel. (Azure SQL Data Warehouse in Synapse is now called Dedicated SQL Pool.)
- Serverless SQL Pool:
- This is a service which is available on demand. We don’t need to provision any resources; this lets us query data in the Data Lake using the familiar T-SQL syntax. Azure allocates the resources and runs the queries as required and returns the results.
Storage:
- Shared Meta store across various compute engines:
- Serverless SQL Pool can access the Spark tables created by the Spark Pool, thanks to the shared Meta store. Meaning tables created using Spark Pool can be accessed using Serverless SQL Pool in T-SQL.
- Synapse Link:
- Enables replication of the transactional data in Cosmos DB and Azure Dataverse and then query the analytical store directly from Synapse without impacting the transactional systems. This allows near-real-time operational reporting without the need to perform any ETL to bring the data from Cosmos DB or Dataverse into Synapse
Data Visualization:
- Integration with Power BI within Azure Synapse Analytics.
Development / Monitoring / Management & Security:
- There is one development studio or workspace for all our services rather than having separate workspace for each service. So, we can perform monitoring, development, management, and everything from one single studio, which is great for a developer.
Mian Ali Shah
Associate Consultant
Data-Driven Innovation in Enterprise Operations
Introduction
In the contemporary digital landscape, the exponential growth of data has catalyzed transformative changes in enterprise operations. Businesses are inundated with data from a plethora of sources, including customer interactions, market trends, social media, and IoT devices. This deluge of information, often referred to as “big data,” presents both challenges and unprecedented opportunities for those capable of leveraging it effectively.
Data-driven innovation is at the forefront of this transformation, enabling organizations to harness vast datasets to drive strategic decision-making, optimize processes, and foster innovation. Unlike traditional business strategies that often rely on intuition and historical trends, data-driven approaches provide a robust foundation for making informed decisions based on real-time insights and predictive analytics. This paradigm shift not only enhances the accuracy and efficacy of decisions but also empowers businesses to anticipate market changes and customer needs proactively.
This comprehensive analysis explores the multifaceted impact of data-driven approaches on enterprise operations. It delves into how advanced analytics, machine learning, and AI are revolutionizing business processes, driving efficiencies, and uncovering new revenue streams. By examining key areas such as enhanced decision-making, operational efficiency, customer insights and personalization, and the development of innovative business models, we can understand the profound implications of data-driven strategies.
Moreover, this exploration will highlight the underlying technologies that enable these transformations, including big data analytics, IoT, cloud computing, and AI. These technologies form the backbone of modern data strategies, offering the tools and platforms necessary to collect, process, and analyze vast amounts of data efficiently.
Finally, we will discuss strategic implementation frameworks that guide enterprises in integrating data-driven methodologies into their operations. This involves creating robust data governance policies, fostering a culture of data literacy, and continuously refining strategies based on feedback and technological advancements.
As we delve deeper into these topics, it becomes evident that embracing data-driven innovation is not merely a competitive advantage but a necessity for survival and growth in today’s data-centric world. Businesses that can effectively harness the power of data will be well-positioned to lead in their respective industries, driving sustained innovation and long-term success.
2.1 Enhanced Decision-Making
Data-driven decision-making leverages predictive and prescriptive analytics to forecast outcomes and recommend actions. This approach uses real-time data to inform strategic choices, reducing reliance on intuition and enhancing accuracy.
2.1.1 Predictive Analytics
Leverages statistical algorithms and machine learning techniques to identify patterns in historical data and forecast future events. This involves regression analysis, time series forecasting, and classification models.
2.1.2 Prescriptive Analytics
Goes beyond prediction to suggest actionable recommendations. It combines optimization algorithms, simulation, and heuristics to determine the best course of action under various scenarios.
2.1.3 Stream Processing
Technologies like Apache Kafka and Apache Flink enable the processing of data in real-time, allowing enterprises to react instantly to changes and make informed decisions on the fly.
2.1.4 Dynamic Dashboards
Tools like Power BI and Tableau provide real-time visualization and monitoring of key performance indicators (KPIs), enhancing situational awareness and decision-making agility.
2.2 Operational Efficiency
Data analytics identifies inefficiencies in business processes, enabling streamlining and cost reduction. Techniques like process mining and predictive maintenance optimize operations, leading to significant productivity gains.
2.2.1 Process Mining
Utilizes data from enterprise systems (e.g., ERP, CRM) to map out and analyze business processes. Techniques like conformance checking and performance mining help identify deviations and bottlenecks.
2.2.2 Robotic Process Automation (RPA)
Automates repetitive, rule-based tasks through software robots, improving efficiency and reducing human error. Advanced RPA tools integrate AI to handle more complex tasks.
2.2.3 IoT and Sensor Data
IoT devices collect real-time data from machinery and equipment. Predictive maintenance algorithms analyze this data to predict failures before they occur, using techniques like anomaly detection and degradation modeling.
2.2.4 Digital Twins
Digital twins are virtual replicas of physical assets that simulate real-world conditions and predict outcomes using real-time data and advanced analytics. This enables proactive maintenance and operational optimization.
2.3 Customer Insights and Personalization
By analyzing customer data, businesses can gain insights into preferences and behaviors. This enables personalized marketing and product recommendations, improving customer satisfaction and loyalty.
2.3.1 Data Integration
CDPs consolidates customer data from various touchpoints (web, mobile, social media) into a unified profile. This involves complex data integration, cleansing, and transformation processes.
2.3.2 Behavioral Analytics
Analyzes customer interactions to understand preferences, behaviors, and sentiments. Techniques include clickstream analysis, sentiment analysis using NLP, and customer journey mapping.
2.3.3 Recommendation Engines
Use collaborative filtering, content-based filtering, and hybrid methods to deliver personalized recommendations. These engines leverage deep learning models to enhance accuracy.
2.3.4 Dynamic Personalization
Adapts content and offers in real-time based on user behavior and contextual data. This requires sophisticated algorithms to deliver relevant and timely experiences.
2.4 Innovation and New Business Models
Data uncovers market gaps and emerging trends, driving innovation and new business models. Advanced analytics facilitates experimentation and rapid prototyping, reducing risk and fostering continuous innovation.
2.4.1 Advanced Market Analytics
Employs data mining and machine learning to analyze market trends, competitor strategies, and consumer needs. Techniques like cluster analysis and association rule learning identify market segments and opportunities.
2.4.2 Sentiment and Social Media Analysis
Uses NLP and social listening tools to gauge public sentiment and identify emerging trends from social media platforms.
2.4.3 A/B Testing and Multivariate Testing
Utilizes statistical testing methods to evaluate the impact of changes in products, services, or processes. This involves designing experiments, running tests, and analyzing results using sophisticated statistical techniques.
2.4.4 Lean Startup Methodology
Incorporates data-driven feedback loops to iterate on product development rapidly. This involves building minimum viable products (MVPs), collecting user feedback, and refining based on data insights.
3. Enabling Technologies
3.1 Big Data Analytics
Big data analytics involves processing and analyzing large datasets to extract meaningful insights. Technologies like Hadoop and Spark enable handling of massive data volumes, supporting complex analyses and decision-making.
3.1.1 Data Warehousing
Centralized repositories like Amazon Redshift and Google BigQuery store structured data for analysis. These platforms support SQL-based querying and integration with BI tools.
3.1.2 Data Lakes
Store unstructured and semi-structured data in its raw form. Technologies like Apache Hadoop and Amazon S3 provide scalable storage and processing capabilities for big data analytics.
3.1.3 Apache Spark
An open-source unified analytics engine for big data processing, featuring built-in modules for streaming, SQL, machine learning, and graph processing.
3.1.4 Hadoop Ecosystem
Includes tools like HDFS for distributed storage, MapReduce for distributed processing, and Hive for data warehousing.
3.2 Machine Learning and AI
Machine learning and AI use algorithms to identify patterns and make predictions from data. These technologies automate tasks, enhance data analysis, and enable advanced applications like natural language processing and computer vision.
3.2.1 TensorFlow and PyTorch
Popular frameworks for building and training deep learning models. They support a wide range of neural network architectures and offer tools for model optimization and deployment.
3.2.2 AutoML
Automated machine learning platforms like Google Cloud AutoML and H2O.ai streamline the process of model selection, hyperparameter tuning, and deployment, making advanced ML accessible to non-experts.
3.2.3 Natural Language Processing (NLP)
Uses techniques like BERT, GPT, and transformer models to understand and generate human language. Applications include chatbots, sentiment analysis, and language translation.
3.2.4 Computer Vision
Involves using deep learning models to analyze and interpret visual data. Techniques like convolutional neural networks (CNNs) enable applications in image recognition, video analysis, and augmented reality.
3.3 Internet of Things (IoT)
IoT devices collect real-time data from physical assets, providing insights for monitoring and optimization. IoT analytics platforms process this data, enabling predictive maintenance and operational efficiency.
3.3.1 IoT Edge Computing
Processes data closer to the source using edge devices, reducing latency and bandwidth usage. Frameworks like Azure IoT Edge and AWS IoT Greengrass enable edge analytics and decision-making.
3.3.2 IoT Analytics
Platforms like ThingWorx and Siemens MindSphere provide comprehensive solutions for collecting, analyzing, and acting on IoT data, integrating with enterprise systems for end-to-end visibility.
3.3.3 Smart Sensors
Equipped with embedded processing capabilities to perform initial data filtering and analysis. These sensors support various communication protocols for seamless integration.
3.3.4 Sensor Fusion
Combines data from multiple sensors to enhance accuracy and reliability. Techniques involve complex algorithms to merge data streams and extract meaningful insights.
3.4 Cloud Computing
Cloud computing offers scalable and flexible infrastructure for data storage and processing. Cloud platforms provide powerful analytics tools, facilitating the integration and analysis of large datasets without significant upfront investment.
3.4.1 Elastic Compute and Storage
Cloud services like AWS EC2, Google Cloud Compute Engine, and Microsoft Azure Virtual Machines provide scalable compute resources. Storage solutions like Amazon S3 and Google Cloud Storage offer scalable, secure data storage.
3.4.2 Serverless Computing
Services like AWS Lambda and Azure Functions allow developers to run code without managing servers, enabling efficient scaling and cost optimization.
3.4.3 Cloud-Based Data Warehouses
Solutions like Snowflake and Google BigQuery offer high-performance, scalable data warehousing with built-in analytics capabilities.
3.4.4 Data Integration and ETL Services
Tools like AWS Glue, Google Cloud Dataflow, and Azure Data Factory automate data extraction, transformation, and loading, simplifying data integration and management.
4. Strategic Implementation Framework
4.1 Data Collection and Integration
Effective data strategies require comprehensive data collection and integration from diverse sources. This involves ETL processes, data lakes, and APIs to ensure seamless data flow and accessibility.
4.1.1 Data Sources Identification
Identify and catalog all potential data sources, both internal (ERP, CRM systems) and external (social media, market reports).
4.1.2 Data Integration Framework
Develop a robust framework for integrating disparate data sources. This includes using ETL processes, data lakes, and APIs to ensure seamless data flow.
4.1.3 Data Governance Policies
Establish policies for data quality, security, and compliance. This includes defining data ownership, access controls, and audit trails.
4.1.4 Data Cleansing Tools
Implement tools and processes for data cleaning, deduplication, and enrichment to maintain high data quality.
4.2 Advanced Analytics
Advanced analytics encompasses predictive and prescriptive techniques to derive actionable insights. This involves using sophisticated statistical models and machine learning to inform decision-making and strategic planning.
4.2.1 Descriptive to Prescriptive Analytics
Progress through stages of analytics maturity, starting with descriptive analytics (what happened) to diagnostic analytics (why it happened), predictive analytics (what will happen), and prescriptive analytics (what should be done).
4.2.2 Advanced Modeling Techniques
Employ sophisticated statistical and machine learning models, including ensemble methods, deep learning, and reinforcement learning, to derive insights and drive decision-making.
4.2.3 Interactive Dashboards
Develop interactive dashboards using tools like Tableau, Power BI, and Looker to visualize complex data and facilitate data-driven discussions.
4.2.4 Data Storytelling
Utilize data storytelling techniques to present insights compellingly, combining visuals with narrative to enhance understanding and engagement.
4.3 Data-Driven Culture
Fostering a data-driven culture involves promoting data literacy and encouraging the use of data in decision-making at all organizational levels. Training programs and leadership support are critical to embedding this culture.
4.3.1 Executive Sponsorship
Secure commitment from top leadership to champion data-driven initiatives and allocate necessary resources.
4.3.2 Data Governance Framework
Establish a governance framework to oversee data management, quality, and security, ensuring alignment with organizational goals.
4.3.3 Skill Development Programs
Provide continuous training and development programs to enhance data literacy across the organization. This includes workshops, online courses, and certifications.
4.3.4 Cross-Functional Teams
Create cross-functional teams comprising data scientists, analysts, and domain experts to drive data-driven projects and foster collaboration.
4.4 Continuous Improvement
Continuous improvement in data strategies involves regular performance monitoring and adaptation to technological advancements. Feedback loops and innovation hubs help maintain and enhance the effectiveness of data-driven approaches.
4.4.1 Key Metrics and KPIs
Define and monitor key metrics and KPIs to evaluate the performance and impact of data-driven initiatives.
4.4.2 Feedback Loops
Establish mechanisms for collecting feedback and iterating on data strategies. This involves regular reviews, stakeholder consultations, and adapting to emerging trends and technologies.
4.4.3 Innovation Hubs and Labs
Set up innovation hubs or labs to experiment with emerging technologies and approaches, fostering a culture of continuous innovation.
4.4.4 Technology Partnerships
Form strategic partnerships with technology vendors, startups, and academic institutions to stay abreast of the latest developments and incorporate cutting-edge solutions.
5. Conclusion
Data-driven innovation is a powerful catalyst for transforming enterprise operations in the digital age. By leveraging advanced analytics, machine learning, IoT, and cloud computing, businesses can harness the full potential of their data. This transformation enables enhanced decision-making, operational efficiency, personalized customer experiences, and the development of new business models.
The impact of data-driven approaches extends beyond mere efficiency gains. Enhanced decision-making, fueled by real-time insights and predictive analytics, allows businesses to make strategic choices with unprecedented precision. Operational efficiency is not just about cost reduction; it involves reimagining processes to create more agile, responsive, and resilient organizations. Personalized customer experiences, enabled by deep data insights, foster stronger customer relationships and drive brand loyalty. Furthermore, the ability to innovate and develop new business models ensures that enterprises can adapt to and anticipate market changes, securing long-term competitiveness.
As enterprises continue to integrate data-centric approaches, fostering a culture that values and utilizes data effectively will be crucial. This cultural shift requires commitment from leadership and a concerted effort to enhance data literacy across the organization. Training programs, data governance policies, and a clear vision for data use are essential components of this transformation. By embedding data-driven thinking into the organizational DNA, businesses can ensure that data is not just a tool, but a core element of their strategic framework.
The journey towards data-driven excellence is ongoing. It involves continuous learning, adaptation, and innovation. Technological advancements such as AI, edge computing, and blockchain will further expand the possibilities for data-driven strategies. Enterprises must remain agile, embracing these new technologies and integrating them into their operations to stay ahead of the curve.
Those who embrace data-driven innovation will be well-positioned to lead in their respective industries. They will be able to anticipate customer needs, respond to market dynamics swiftly, and drive sustainable growth. In a world where data is a critical asset, the ability to leverage it effectively will distinguish the leaders from the laggards.
The transformative power of data-driven innovation cannot be overstated. As we move further into the digital age, the enterprises that prioritize data-centric approaches will not only survive but thrive. By continuing to invest in data capabilities and fostering a culture of innovation, businesses can unlock new opportunities and achieve remarkable success in an increasingly competitive landscape. The future belongs to those who are data-driven, and the potential for growth and innovation is boundless.
M Hanzla
Associate Consultant
Level Up Your Skills with Azure DevOps in 2024
You’re ready to take your skills to the next level in 2024, and what better way to do that than with Azure DevOps? This powerful set of services and tools can skyrocket your career-if you know how to use them right. Stick with me, and I’ll walk you through everything you need to get started with Azure DevOps this year. We’ll cover the basics, like boards, pipelines, repos, and more. I’ll share tips to help you stand out, as well as mistakes to avoid. You’ll learn real-world examples and practical steps to start leveraging Azure DevOps on your projects. Whether you’re new to DevOps or looking to improve, this guide will help you master Azure DevOps and become an Azure hero in 2024. Let’s level up!
Introduction to Azure DevOps
Azure DevOps is Microsoft’s cloud service for collaborating on code development and deploying software. It provides developer services to support teams to plan work, collaborate on code development, and build and deploy applications.
Manage your code
Azure DevOps provides Git repositories to store your source code, allowing teams to collaborate on code. You get free unlimited private Git repos to store any code for your projects.
Build and release pipelines!
Set up build and release pipelines to automatically build, test, and deploy your apps. Build pipelines compile your source code and run tests, while release pipelines deploy your app to environments like development, staging, and production.
Agile tools
Use Azure Boards to plan, track, and discuss work across your teams. Create Kanban boards, backlogs, sprint boards, and queries to visualize your work. Stay on top of priorities, assign work, and update statuses to keep your team in sync.
Continuous integration
Set up continuous integration with Azure Pipelines to automatically build and test code after every commit. This helps catch issues early and ensures quality. Every time you push an update, Azure DevOps will build your app, run tests, and validate the changes before deploying to production.
Reports and dashboards
Gain insight into your development projects with interactive reports and dashboards. View summaries of work tracking, builds, releases, tests, and code. Create custom queries and charts to monitor key metrics for your teams.
With a robust set of features for planning, coding, building, testing, and deploying apps, Azure DevOps has everything you need to level up your skills and build better software. Ready to get started? Sign up for free and kick off your first project today!
Key Azure DevOps Concepts: CI/CD, AKS, IAM Roles
To level up your Azure DevOps skills, you need to understand some key concepts. Continuous Integration and Continuous Deployment (CI/CD) CI/CD is the process of automating your software delivery pipeline. When you make changes to your code, Cl automatically builds and tests it. If it passes, the CD can automatically deploy it to your environment. This speeds up your development cycle and reduces errors.
Azure Kubernetes Service (AKS)
AKS lets you easily deploy a managed Kubernetes cluster in Azure. Kubernetes is an open- source system for automating deployment, scaling, and management of containerized applications. Using AKS, you can focus on your apps rather than infrastructure.
Top 5 Benefits of Using Azure DevOps
Enhanced Collaboration
Azure DevOps allows cross-functional teams to work together seamlessly. Developers, testers, designers, and project managers can collaborate on projects in real-time. Using Azure DevOps, you can easily track project schedules, share documents, view each other’s work items, and get updates on the project’s progress. This enhanced collaboration significantly improves productivity and accelerates product delivery.
Continuous Integration and Continuous Delivery
Azure DevOps provides built-in CI/CD pipelines to automate your build, test, and deployment processes. You can set up pipelines to automatically build and test code after every commit. This allows you to detect bugs and issues early on. You can also configure deployment pipelines to automatically deploy your app after it passes all tests. This speeds up product delivery and ensures high quality.
Agile Project Management
Azure DevOps has a full set of Agile tools to help you implement Scrum, Kanban, and Scrumban methodologies. You can create interactive backlogs, boards, and burndown charts to plan, organize, and track your work. Teams can break down features into user stories, prioritize them, assign them to sprints, and track their progress. This gives you end-to-end visibility into your projects and helps ensure work is delivered on schedule.
Built-in Version Control
Azure DevOps includes Git repositories for managing your source code. Developers can commit, branch, merge, and push code in a centralized location. This makes it easy to track changes, revert to previous versions, and collaborate with teammates. Git also Integrates with Azure DevOps build and release pipelines to deploy the latest code changes automatically.
Reporting and Analytics
Azure DevOps provides over 100 built-in reports and dashboards to gain insights into your projects. You can view burndown charts, velocity reports, test results, build success rates, and more. Teams can make data-driven decisions by analyzing key metrics and KPIs. The reports also provide visibility to stakeholders and help keep projects on track.
Step-by-Step Guide to Setting Up Azure DevOps
Create an organization!
The first thing you’ll need to do is create an Azure DevOps organization. This is where you’ll manage all your Azure DevOps projects and teams. Head to dev.azure.com and click “Start free” to create a new organization. Choose a unique name for your organization and the location where you want to store your data.
Add a project!
Within your new organization, you’ll want to create at least one project. A project is where you’ll manage the code, work items, builds, releases, and test plans for your software. Click “New project” and choose a name for your project, select the version control system you want to use (Git is a great option), and choose a process template to get started with. The “Agile” template is a good all-purpose choice.
Set up team members!
No DevOps environment is complete without people! Add members to your organization and specify which projects they have access to. You can create teams to organize members, set permissions, and manage notifications. Adding members is easy- just enter their email address and they’ll receive an invitation to join the organization.
Connect to code!
The next step is connecting a code repository to your project. If you chose Git for version control, you’ll need to push your code to a repository in Azure DevOps, GitHub, or another provider. Then, connect that repository to your Azure DevOps project so you can build, test, and release the code. Azure DevOps has built-in Git repositories, or you can connect to an external repo with the click of a button.
Set up a build pipeline!
A build pipeline compiles your source code into a package that can be deployed. In Azure DevOps, set up a build pipeline that points to your code repository. Choose a template to get started, then customize the build stops. At a minimum, you’ll want to restore dependencies, compile the code, and publish an artifact to use for releasing.
Create a release pipeline!
The final step is setting up a release pipeline to deploy your build artifacts. A release pipeline takes the output of a build and deploys it to an environment like development, testing, or production. Set up release triggers to automatically create a new release when a build completes. Add deployments to your release pipeline for each environment, with the proper approvals and checks in place.
With these basics set up, you’ll be well on your way to managing your entire DevOps workflow in Azure DevOps! Let me know if you have any other questions.
Azure DevOps in 2023 and Beyond – What’s Next?
Continued Growth
Azure DevOps will only continue to grow in popularity and capability over the next year. More and more companies are adopting DevOps practices and Azure DevOps provides a robust, integrated set of tools to enable that. You’ll see constant improvements to the UI, new features added, and tighter integration with other Microsoft products.
Increased Automation
There will be a bigger push towards increased automation in the DevOps process. Things like automated testing, continuous integration and deployment, infrastructure as code, and auto-remediation of issues will become more prominent. This allows for faster development cycles, reduced errors, and less manual intervention needed. Azure DevOps has many of these capabilities built-in already, but they will continue to expand and improve them over time.
Enhanced Security
As the adoption of DevOps grows, security is becoming an increasing concern. Azure DevOps will focus more on baking security into the platform and process. Things like automated security scanning, policy enforcement, and compliance management will be emphasized. Microsoft wants customers to feel that the Azure platform, and all services Including DevOps, meet the highest security standards.
Al and ML integration
Artificial intelligence and machine learning will start to play a bigger role in Azure DevOps. Al can help with test automation, detecting vulnerabilities in code, managing Infrastructure, and more. Microsoft is investing heavily in Al and ML so you can expect these capabilities to start making their way into Azure DevOps, allowing for smarter and more optimized DevOps processes.
The future looks bright for Azure DevOps. Microsoft is committed to constant improvement to help companies achieve a mature DevOps practice. Using Azure DevOps will give you access to the latest tools and technologies to optimize your software development lifecycle. Innovations in automation, security, Al and more will transform how you build and deploy software. Overall, Azure DevOps is poised to remain an industry leader in enabling DevOps.
Conclusion
And there you have it! By starting to get familiar with Azure DevOps now, you’ll be in a great spot for when demand really takes off. The tools are only going to get better too. Stick with it through any early frustrations, join some online communities to connect with fellow learners, and you’ll be on your way to becoming an Azure DevOps expert! Keep learning, stay curious, and you can land that dream DevOps job. The hard work will pay off if you start preparing today. You got this!
Mohammad Saad Ahmad
Assosiate Consultant
Exploratory Data Analysis: Unlocking Insights
Exploratory data analysis can guide you through the process, transforming those inscrutable figures into invaluable discoveries. With the right techniques, you’ll go from tables and columns to detailed analysis. In just a few simple steps, you’ll uncover the hidden value in your data and gain the skills to tackle any dataset.
Exploratory Data Analysis or EDA is the process of analyzing data to uncover patterns, insights, and relationships. It helps you get familiar with your data to generate ideas and hypotheses to guide modeling and analysis.
Looking for trends
EDA is like detective work where you dig deeper and deeper and find new things. You’re searching for clues in the data that point to trends, outliers, and relationships. Things like steadily increasing sales over time, purchases peaking around the holidays, or product preferences varying by region. Spotting these trends can lead to a clear understanding of the data.
Generating questions
As you explore the data, you’ll come up with questions about what you’re seeing.
- Why are sales dropping for a certain product?
- Why do some customers buy more frequently than others?
EDA is all about generating these questions and perform analysis. With more Questions comes great analysis.
EDA is a crucial first step in any data analysis to get acquainted with your data. While it can feel unstructured, remember that the goal is to find clues, generate questions, and summarize key attributes. So get curious, identify the trends, and see what you can uncover in your data!
EDA Techniques and Tools
Visualization
One of the most useful EDA techniques is data visualization. Creating charts, graphs, and plots allows you to spot patterns, trends, and outliers in your data. Some options include:
- Scatter plots to show the relationship between two variables. Look for clusters, curves, and outliers.
- Histograms to see the distribution and shape of a single variable. Check for normality, skewness, and outliers.
- Box plots also display the distribution of a variable. The box shows the middle 50% of values, the median is the line inside the box, and the whiskers show the minimum and maximum values.
- Heatmaps show the relationship between multiple variables in a grid format using color coding.
Summary Statistics
Calculate summary stats like the mean, median, mode, standard deviation, variance, minimum, maximum, and quartiles. These give you a high-level sense of your data and can reveal skewness or heavy tails.
Correlation Analysis
Check for relationships between variables using the correlation coefficient. Values range from -1 to 1, indicating negative to positive linear relationships. Be aware that correlation does not imply causation. Variables can be correlated without a direct causal relationship.
Hypothesis Testing
Use statistical tests like the t-test, ANOVA, and chi-square to determine if differences between groups or relationships between variables are statistically significant. Set a significance level (like 0.05) and check if your p-value is below that level. If so, you can reject the null hypothesis that there is no difference or relationship.
Exploratory data analysis is an iterative process. Visualize your data, calculate summaries, check for correlations and test hypotheses. Then go back and do it all again. Reveal the layers of your data one by one to reveal key insights that can drive business decisions and guide your modeling approaches. The tools and techniques of EDA are simple but extremely powerful for understanding what your data can tell you.
Step-by-Step EDA Process
Exploratory data analysis is an iterative process. As a Data analyst, you get to know your data by diving in and exploring, then coming up for air and evaluating what you’ve learned. The key is not to get overwhelmed by the details. Follow these broad steps to conduct EDA:
Look at the Big Picture
Start by importing your data and checking the basic attributes like number of rows and columns, data types, and missing values. Look for any obvious errors or inconsistencies. This helps you get the lay of the land before zooming in.
Analyze Each Variable
Now examine each variable individually. Look at summaries like mean, median, mode, minimum and maximum to understand the distribution. Check for outliers or skewness. See how each variable relates to your target or dependent variable. This will help determine which factors may be most important in your analysis.
Find Patterns and Relationships
Next look for relationships between variables. Try creating scatterplots, heatmaps or correlation matrices to visualize connections. Strong correlations may indicate redundancy or confounding factors in your data. Look for interesting patterns that provide insights into your research questions.
Test Your Assumptions
EDA is also about challenging any assumptions you have about the data. Try segmenting the data in new ways, stratifying by certain attributes or running analyzes on subsets. See if the patterns hold or if new insights emerge. Let the data speak for itself rather than imposing your preconceptions.
Repeat and Refine
EDA is an iterative process, so keep looping back over your data as new questions arise. Revisit summaries and visualizations, drill down into details or take a step back for a fresh perspective. Each pass may reveal new insights to guide your analysis. With practice, EDA becomes a habit of mind for unlocking the secrets hidden in your data.
Applying EDA: Real-World Examples and Use Cases
Identifying Outliers
EDA is great for spotting outliers, data points that are very different from the rest. These could be errors, or they could point to something interesting. For example, say you have data on customers’ monthly spending at your store. Most people spend between $200 to $500 per month, but you notice one customer spends over $2,000 each month. This could indicate a data entry error, or it could be a highly valuable customer you want to give extra attention. EDA helps you find these outliers so you can investigate further.
Detecting Trends
Looking at data over time is one of the best ways to identify trends. For example, you may plot monthly sales or website traffic over the course of a year. You might notice an upward trend, indicating growth, or a downward trend showing a decline. Spotting these trends early on allows you to take action, such as ramping up marketing during slow months or allocating extra resources to support increasing demand.
Exploring Relationships
EDA also helps explore relationships between variables in your data. For example, if you have data on customers’ locations and purchases, you can look for patterns to see if customers from certain areas tend to buy more of a particular product. Or you may find that higher income customers have larger order sizes. Uncovering these relationships can help you tailor your marketing and product offerings.
EDA is a powerful first step in understanding your data and unlocking valuable insights. By applying EDA techniques to your own data, you’ll gain a deeper understanding of the trends, outliers, and relationships that drive your business—and be able to take action on the findings. The key is exploring with an open and curious mind, not being afraid to ask questions, and letting the data guide you to new discoveries.
Conclusion
Goal is to gain the understanding of the data by EDA so that you can build insights and have a bigger picture on how you should design your reports and dashboards and how that will solve a real world problem. By diving into your data, poking around, slicing and dicing it every which way, you give yourself the chance to see things that were invisible before. The insights are there in your data, just waiting to be discovered.
Gulfam Pervaiz
Consultant
Harnessing Elasticsearch with Python for Efficient Data Indexing and Searching
Introduction:
Elasticsearch, a distributed search and analytics engine, coupled with Python, offers a powerful solution for indexing and searching large volumes of data efficiently. In this comprehensive guide, we’ll explore how to leverage the Elasticsearch Python client to connect to both Elasticsearch Cloud and local instances, create indices, define custom mappings, ingest data from a CSV file, and perform queries. We’ll illustrate each step with practical examples, demonstrating how Elasticsearch can seamlessly integrate into Python applications.
Connecting to Elasticsearch:
The first step in working with Elasticsearch is establishing a connection. Using the Elasticsearch Python client, we can connect to both Elasticsearch Cloud and local instances effortlessly.
rom elasticsearch import Elasticsearch
ENDPOINT="http://localhost:9200"
## to conenct via username password use this
#es = Elasticsearch(hosts=[ENDPOINT], http_auth=(USERNAME, PASSWORD))
es = Elasticsearch(hosts=[ENDPOINT])
#checking if elastic search is connected
es.ping()
Creating an Index and Custom Mappings:
Indices are containers for storing documents in Elasticsearch, and mappings define the structure of these documents. Let’s create an index named “my_index” and define a custom mapping.
#Index Schema
indexMapping={
"properties":{
"id":{
"type":"long"
},
"Book":{
"type":"keyword"
},
"Page_No":{
"type":"long"
},
"Part":{
"type":"text"
},
"Chapter":{
"type":"text"
},
"Sub_Chapter":{
"type":"text"
},
"Article_No":{
"type":"keyword"
},
"Clause_No":{
"type":"keyword"
}
"Text_vector":{
"type":"dense_vector",
"dims":768,
"index":True,
"similarity":"l2_norm"
}
}
}
# create the index and apply the custom mappings
es.indices.create(index=index_name,settings=indexSettings, mappings=indexMapping)
Ingesting Data from CSV:
Elasticsearch allows easy ingestion of structured data from various sources. Let’s demonstrate how to ingest data from a CSV file into our index.
import pandas as pd
df=pd.read_csv("constitution.csv", index_col=False)
df.rename(columns={'Unnamed: 0': 'id'}, inplace=True)
df.head()
After reading the csv into pandas we will add the dataframe to elastic index using
record_list=df.to_dict("records")
#pushing data to elastic search index
for record in record_list:
try:
es.index(index=index_name,document=record)
except Exception as e:
print(e)
Querying Data:
With the data ingested, we can now perform various types of queries to retrieve relevant documents.
ook="Anti Terrorism Act 1997"
# Execute the search
result = es.search(index=index_name, query = {
"match": {
"Book": book}
})
print(result)
# Print the results
for hit in result['hits']['hits']:
print(hit['_source']['Textual_Metadata'])
You can write and design queries according to your needs and data
Conclusion:
In this blog post, we’ve explored the process of utilizing Elasticsearch with Python for efficient data indexing and searching. We’ve covered connecting to Elasticsearch instances, creating indices with custom mappings, ingesting data from CSV files, and executing queries to retrieve relevant information.
By harnessing the power of Elasticsearch alongside Python, developers can build robust search functionalities into their applications, enabling seamless integration with structured data sources like CSV files. Whether working with Elasticsearch Cloud or local instances, the Elasticsearch Python client provides a versatile and intuitive interface, empowering developers to leverage Elasticsearch’s capabilities effectively.
Rawaha Javed
Associate Consultant
Unleashing the Data Goldmine: Mastering Web Scraping with Selenium, BeautifulSoup, and MongoDB
In today’s data-driven world, the ability to extract valuable insights from the vast expanse of the web is a skill that can unlock endless possibilities. Web scraping has emerged as a powerful technique to gather data from websites, and when combined with tools like Selenium, BeautifulSoup, and MongoDB, it becomes a formidable force in the hands of data enthusiasts and professionals.
Harnessing the Power of Selenium and BeautifulSoup
Selenium is a robust automation tool that allows users to interact with web pages programmatically. It simulates a user’s actions on a web browser, enabling tasks such as form submissions, button clicks, and navigation through web pages. This capability is instrumental in web scraping, especially for websites with dynamic content loaded via JavaScript.
BeautifulSoup, on the other hand, is a Python library designed for parsing HTML and XML documents. It provides a simple and intuitive interface for navigating and extracting data from web pages. When combined with Selenium, BeautifulSoup complements the automation aspect by providing powerful data extraction capabilities.
Building a Data Pipeline with MongoDB
MongoDB is a popular NoSQL database that excels in handling unstructured and semi-structured data. It offers flexibility and scalability, making it an ideal choice for storing scraped data. By integrating MongoDB into the web scraping workflow, data can be efficiently stored, managed, and queried, forming a robust data pipeline.
Automating with Python Scheduler and FastAPI
Automation is key to maximizing the efficiency of web scraping tasks. Python Scheduler (also known as cron in Unix-based systems) allows users to schedule Python scripts to run at specified intervals. This feature is invaluable for automating web scraping jobs, ensuring timely updates and data collection.
FastAPI, a modern web framework for building APIs with Python, complements the automation process by providing a streamlined interface for deploying interactive web scrapers. It enables users to create RESTful APIs that interact seamlessly with the scraping logic, facilitating data retrieval and analysis.
The Role of a Data Engineer
In this data ecosystem, the role of a Data Engineer is pivotal. A Data Engineer is responsible for designing, building, and maintaining data pipelines that extract, transform, and load (ETL) data from various sources into storage systems like MongoDB. They work closely with data scientists and analysts to ensure that the data collected is accurate, consistent, and accessible for analysis and decision-making.
Data Engineers leverage their expertise in programming languages like Python and SQL to write efficient and scalable code for data processing tasks. They also possess knowledge of data warehousing concepts, data modeling, and database management systems, which are essential for optimizing data workflows and performance.
Additionally, Data Engineers play a crucial role in implementing privacy and security measures in data handling processes, ensuring compliance with regulatory requirements and industry standards. They collaborate with DevOps teams to automate deployment and monitoring of data pipelines, enabling seamless data flow and reliability.
Empowering Data Enthusiasts and Professionals
The fusion of Selenium, BeautifulSoup, MongoDB, Python Scheduler, FastAPI, and the expertise of Data Engineers empowers data enthusiasts and professionals to unleash the full potential of web scraping. Whether it’s gathering market insights, monitoring competitors, or extracting research data, this powerful combination offers a gateway to a treasure trove of information.
In conclusion, mastering web scraping with Selenium, BeautifulSoup, and MongoDB opens doors to a “Data Goldmine” where valuable insights await discovery. By harnessing automation and modern web technologies, data-driven decision-making becomes more accessible, efficient, and impactful than ever before.
Muhammad Talaal
Associate Consultant
Azure Storage – Types and Uses
Introduction:
The Azure Storage platform is a cloud storage solution provided by Microsoft to cover all modern data storage scenarios. IT provides highly available, scalable, durable and secure storage for various data objects in the cloud – making it easy to access data from anywhere in the world using HTTP or HTTPS via a REST API. Azure Storage also offers client libraries for developers building applications or services with .NET, Java, Python, JavaScript, C++, and Go. Developers and IT professionals can use Azure PowerShell and Azure CLI to write scripts for data management or configuration tasks. The Azure portal and Azure Storage Explorer provide user interface tools for accessing the Azure Storage.
Azure Storage has many benefits for example, it is durable and highly available by providing redundancy. Making sure that the data is safe in case of hardware failure. One can also choose to replicate data across data centres in different geographical regions to safeguard it in case of a natural disaster or some other issues. The data stored in the Azure storage accounts is encrypted by the service with client having control over who can access the data and in which way. It is also scalable, meaning that it provides real time increase of decrease in storage capacity depending on the performance needs of the application. Azure Storage is managed by Microsoft – means that all the hardware maintenance and updates are handled for the clients. As a storage solution on cloud Azure Storage is highly accessible and can be accessed anywhere from the world with Internet.
All the Azure Storage Services can be accessed via a storage account. There are several types of storage accounts each with its own set of features and pricing models. The detail about the storage accounts can be accessed here.
Types of Storage Service in Azure Storage
The Azure Storage platform includes the following data services:
Azure Blobs: A massively scalable object store for text and binary data including streaming and image data. Also includes support for big data analytics through Data Lake Storage Gen2.
Azure Files: Managed file shares for cloud or on-premises deployments. These file shares can be easily accessed via the industry standards, for example, SMB, NFS and Azure Files REST API.
Azure Elastic SAN: A fully integrated solution that simplifies deploying, scaling, managing, and configuring a SAN in Azure. It is interoperable with multiple types of compute resources such as virtual machines, VMWare Solutions and Kubernetes Service.
Azure Queues: A messaging store for reliable messaging between application components. The messages can be accessed anywhere from the world via authenticated calls using HTTP or HTTPS.
Azure Tables: A NoSQL store for schemaless storage of structured data making it easy to adapt your data as the needs of your application evolve. Accessing Table storage data is fast and economical for many types of applications and is typically lower in cost than traditional SQL for similar volumes of data.
Azure managed Disks: Block-level storage volumes for Azure Virtual Machines. These are like physical disks in an on-premises server but virtualized. They are easy to provision, all you have to do is specify the size and type and Azure will handle the rest.
Azure Container Storage (preview): A volume management, deployment, and orchestration service built natively for containers. It integrates with Kubernetes, allowing the user to provision persistent volumes dynamically and automatically to store data for stateful applications running on Kubernetes clusters.
Which Storage Service to use When?
Azure Blobs: They are best used when the application needs to support streaming and random-access scenarios. It is also best used when you want to build an enterprise data lake on Azure and perform big data analysis. The most common scenario is to store images or documents and access them directly via a browser. Also excellent for storing data for backup and restore, disaster recovery and archiving.
Azure Files: You want to “lift and shift” an application to the cloud that already uses the native file system APIs to share data between it and other applications running in Azure. Azure Files can also be used when you want to scale the on-premises file servers or NAS machines. It can also be used to store tools or set-ups that need to be accessed from multiple machines. File share can be used to write resource logs, metrics, and crash dumps and processed or analysed later.
Azure Elastic SAN: Best use for Azure Elastic SAN is when a large-scale storage is needed that is interoperable with multiple types of compute resources – including SQL and NoSQL databases, virtual machines, and Kubernetes Services.
Azure Queues: Best use to create a backlog od work to process asynchronously, like in the Web-Queue-Worker architectural style. A queue can hold a message up to 64 KB in size and it can contain million of messages, up to the total capacity of the storage account.
Azure Tables: Azure tables can store TBs of structured data that can be accessed by web scale applications. It can also be ideal for storing datasets that do not complex joins, foreign keys and can be denormalized for fast access. Azure tables are available through Azure Cosmos DB for Table, which offers higher performance and available, global distribution, and automatic secondary indexes.
Azure Managed Disks: When you want to “lift and shift” the applications from the on-premises servers to the read and write data to persistent disks. Also, they can be used when the data isn’t required to be accessed from outside the virtual machine to which disk is attached.
Azure Container Storage: If you have highly fluctuating data needs to you want them to be dynamically and automatically met by the system then using Azure Container Storage is an excellent choice. It will provision persistent volumes to store data for stateful applications running on Kubernetes clusters.
Conclusion:
Azure Storage provides various types of storage solutions to cover all our storage needs from storing streaming data to managing our work loads via Azure Queues. These storage solutions are reliable, secure, and highly affordable and can be accessed from anywhere in the world. These storage services provide a high scalability without wait. The need of the hour is to familiarize with the service and choose the one that is best suited for our organizational needs.
Sugandh Wafai
Consultant
Microsoft Fabric
Overview:
Microsoft Fabric is an all-in-one analytics solution for enterprise needs, encompassing data handling, data science, Real-Time Analytics, and business intelligence. Its extensive range of services includes data lake management, data engineering, and data integration, all within a single platform.
Fabric eliminates the need to piece together different services from multiple vendors. You can enjoy a highly integrated, streamlined, and user-friendly system that streamlines analytics workflows.
Microsoft Fabric platform is built on a Software as a Service (SaaS) framework, elevating simplicity, and integration to unprecedented levels.
Component of Microsoft Fabric:
- Data Engineering: The Data Engineering experience offered by Microsoft Fabric provides Spark platform. This empowers data engineers to execute extensive data transformations at a scale and democratize data accessibility through the Lakehouse. Microsoft Fabric Spark seamlessly integrates with Data Factory, allowing for the scheduling and orchestration of notebooks and Spark jobs.
- Data Factory: Azure Data Factory merges the ease of Power Query with the scalability and capabilities of Azure Data Factory. With over 200 native connectors, it facilitates connections to both on-premises and cloud-based data sources, providing flexibility and accessibility for data integration tasks.
- Data Science: The Data Science experience within Microsoft Fabric streamlines the process of building, deploying, and operationalizing machine learning models within the Fabric environment. It seamlessly integrates with Azure Machine Learning, offering built-in capabilities for experiment tracking and model registry. This integration enhances collaboration and efficiency throughout the machine learning lifecycle.
- Data Warehouse: The Data Warehouse experience within Microsoft Fabric offers industry-leading SQL performance and scalability. It features a fully separated compute and storage architecture, allowing for independent scaling of each component. Furthermore, it natively supports storing data in the open Delta Lake format, enhancing data integrity and reliability.
- Real-Time Analytics: Observational data, sourced from various applications, IoT devices, human interactions, and more, represents the fastest-growing category of data. This data is often semi-structured, in JSON or text formats. This data arrives in high volume with changing schemas. Traditional data warehousing platforms struggle to effectively handle such data. Real-Time Analytics is best in class engine for observational data analytics.
- Power BI: Power BI is the world’s leading Business Intelligence platform. It ensures that business owners can access all the data in Fabric quickly and intuitively to make better decisions with data.
OneLake:
OneLake serves as a unified, centralized, logical data lake for comprehensive organizational needs. Similar to OneDrive, OneLake seamlessly accompanies every Microsoft Fabric tenant, offering a singular repository for all analytical data requirements. Its key benefits include:
- One data lake for the entire organization.
- One copy of data for use with multiple analytical engines.
OneLake Features:
- Open at every level: OneLake is an open platform built on Azure Data Lake Storage (ADLS) Gen2, capable of handling any type of data – structured or unstructured. It automatically stores all data in Delta Parquet format. Whether data is loaded into it via Spark by a data engineer or through T-SQL by a SQL developer for a fully transactional data warehouse, it all contributes to the same data lake. OneLake supports ADLS Gen2 APIs and SDKs, ensuring compatibility with existing applications, including Azure Databricks. Essentially, it provides a unified storage solution for the organization, with workspaces appearing as containers and different data items as folders within those containers.
- OneLake file explorer for Windows:OneLake functions as the “OneDrive for data”. It offers a user-friendly experience for Windows users through the OneLake file explorer. This explorer facilitates effortless navigation of workspaces and data items, enabling tasks such as uploading, downloading, and modifying files in a manner like Office applications. With the OneLake file explorer, interacting with data lakes becomes straightforward, catering to both technical and nontechnical business users.
- One Copy of data:OneLake maximizes data value without duplication or movement. It eliminates the need for copying data across different engines or breaking down silos for comprehensive analysis with data from diverse sources.
- Shortcuts connect data across domains without data movement: Shortcuts in OneLake simplify data sharing across teams and applications without unnecessary duplication. They create references to data stored in various locations, within or outside OneLake, making files and folders appear locally accessible regardless of their actual storage location.
Usama Saleem
Junior Consultant
Benchmarking of Medical LLMs
Medical LLM’s Benchmarking
The evaluation of the Medical LLM’s model using the USMLE Dataset involves assessing the model’s performance and accuracy in responding to medical questions.
Introduction
This section extensively evaluates the performance of Medical LLMS using the USMLE Dataset. Focused on assessing the model’s knowledge in addressing medical queries, our analysis rigorously evaluates the model’s performance. Serving as a benchmark within this specialized domain, the outcomes of this evaluation offer valuable insights to enhance language models for crucial medical applications.
Objectives
The objectives for Benchmarking are as follows.
- Benchmarking of models’ performance.
- Behavior of models on an Out of Distribution dataset US-Mili Dataset.
Dataset
The USMLE dataset, a component of MEDQA [1], comprises medical questions used for training, validating, and testing models. Each question presents four answer options. The dataset includes 10,178 questions for training, 1,272 for development, and 1,273 for testing, totaling 12,723 questions. The 100 recodes Test data is only used to do out of distribution testing.
Model Selection Criteria
In the development of medical applications, selecting the right models is crucial. This section explores the methods and criteria guiding the identification of optimal models, ensuring the Chatbot’s precision and integration within the medical domain. Criteria defining the model requirements for the benchmarking encompass the following key features.
- Fine-tuned on medical datasets.
- Model parameters within the range of 7B to 13B.
- Publicly available weights for the model.
- Results and papers cited in reputable research articles.
- A model trained on a big dataset and featuring a medical benchmark.
Models For the benchmarking
The selected models are listed in the table 1 below:
Models | # Params | Data Scale | Data Source |
ClinicalGPT [2] | 7B | 96k EHRs, 192k med QA, 100k dialogues | MD-EHR, VariousMedQA, MedDialog |
ChatDoctor [3] | 7B | 110k dialogues | HealthCareMagic |
MedAlpaca [4] | 7B/13B | 160k medical QA | Medical Meadow |
AlphaCare [5] | 13B | 52k instructions | MedInstruct-52k |
Meditron [6] | 7B | 48.1B tokens | Clinical Guidelines, PubMed |
LLAMA 2 [7] | 7B/13B | 2.0T | A new mix of publicly available online data |
Prompt Format
It is crucial to emphasize that each fine-tuned LLM model operates with a specific prompt format, generating output accordingly. Therefore, for benchmarking these models, the prompt format specified in their respective papers or repositories is utilized. The prompt format for each model is provided below. The implementation of the experiment can be found in the notebook.
ClinicalGPT
def generate_prompt(prompt: str, system_prompt: str = DEFAULT_SYSTEM_PROMPT) -> str: return f””” Prompt: {system_prompt} {prompt} Response: “””.strip()
|
ChatDoctor
def generate_prompt(Questions: str, options: str = DEFAULT_SYSTEM_PROMPT) -> str: return f””” ### Instruction: You are a doctor, please answer the medical MCQS based on the given Question and options A B C D. ### Input: {Questions} {options} ### Response: “””. strip()
|
MedAlpaca
def generate_prompt(Questions: str, options: str = DEFAULT_SYSTEM_PROMPT) -> str: return f””” Context: Answer the medical MCQS based on the given Question and options A B C D. Question: {Questions} {options}
Answer: “””. strip()
|
AlphaCare
def generate_prompt(Questions: str, options = DEFAULT_SYSTEM_PROMPT) -> str: return f””” Human: Select the one output (a), (b), (c) or (d) that better matches the given instruction. example: # Example: ## Instruction: Give a description of the following job: “ophthalmologist” ## Output (a): An ophthalmologist is a medical doctor who specializes in the diagnosis and treatment of eye diseases and conditions. ## Output (b): An ophthalmologist is a medical doctor who pokes and prods at your eyes while asking you to read letters from a chart. ## Which is the better choice, Output (a) or Output (b), or is it a Tie? Output (a) Here the answer is Output (a) because it provides a comprehensive and accurate description of the job of an ophthalmologist. # Task: Now is the real task, do not explain your answer, just say Output (a) or Output (b). ## Instruction: You are medical expert answer the following question: {Questions} ## Output (a): {options[“A”]} ## Output (b): {options[“B”]} ## Output (c): {options[“C”]} ## Output (d): {options[“D”]} ## Which is the best choice, Output (a), Output (b), Output (c) or Output (d)? Assistant:”. strip()
|
MEDITRON
def generate_prompt(prompt: str, system_prompt: str = DEFAULT_SYSTEM_PROMPT) -> str: return f””” ### User: {system_prompt} {prompt} ### Assistant: “””.strip()
|
LLAMA 2
def generate_prompt(prompt: str, system_prompt: str = DEFAULT_SYSTEM_PROMPT) -> str: return f””” Prompt: {system_prompt} {prompt} Response: “””.strip() |
Testing of model’s performance
The evaluation involved 100 multiple-choice questions (MCQs) pertaining to pharmacy-related topics. The models subjected to testing include ClinicalGPT, MedAlpaca, AlphaCare, LLAMA 2 7B, LLAMA 2 13B and Meditron. Each model’s performance was assessed based on its ability to accurately answer these domain specific MCQs within the pharmacy field.
Evaluation metrics for the models
In evaluating LLMs for MCQs, accuracy was employed as the primary metric. Answers were treated as binary outcomes, either true or false, determined by the available multiple-choice options. This approach measured the model’s success in providing correct responses within the given set of questions.
Results
The evaluation of these models is carried out manually, with each response categorized as either “True,” “False,” or “Not Answered,” based on the ground truth provided in the dataset. The results are presented in Table 2, which showcases the outputs of the LLMs models as “True,” “False,” and “Not Answered,” with detailed data available in excel file. Figure 1 illustrates the performance accuracy of the models on the US-Mili dataset. Notably, LLAMA 2 emerges as the frontrunner, demonstrating superior performance, particularly on Out of Distribution Dataset.
Model Name | TRUE | FALSE | Not Answered |
llama 2 13b | 43 | 46 | 11 |
llama 2 7b | 32 | 56 | 12 |
ChatGPT | 26 | 74 | 0 |
MedAlpaca | 33 | 67 | 0 |
AlphaCare 7B | 23 | 25 | 52 |
ChatDocter_7B | 0 | 0 | 100 |
Meditron | 0 | 0 | 0 |
Table 1: Human Supervised Evaluation of the LLM’s.
Figure 2: Performance of Medical LLM’s on US-Mili Dataset.
Conclusion
In conclusion, the evaluation of Medical Language Models (LLMs) using the USMLE Dataset revealed insights into their performance in responding to medical-related multiple-choice questions. The models, including LLAMA 2 13B, LLAMA 2 7B, ClinicalGPT, MedAlpaca, AlphaCare 7B, and Meditron, demonstrated varying levels of accuracy. LLAMA 2 13B emerged as the most accurate model, although none achieved a majority in correct responses, emphasizing the inherent challenges in medical question answering. Notably, AlphaCare 7B faced difficulties, marked by a substantial proportion of questions categorized as Not Answered. Additionally, LLAMA 2 13B exhibited a higher capability in handling out-of-distribution data compared to LLAMA 2 7B, underlining the importance of evaluating model behavior beyond the training domain.
Hamza Farooq
ML Engineer
