Sateesh Pabbathi’s Post

Streamlining Data Integration with Microsoft Fabric and Azure Data Factory This powerful combination allows you to create robust data pipelines that automate data movement and transformation, making your data integration process more efficient. Step-by-Step Guide: 1. Set Up Azure Data Factory: • Navigate to the Azure portal and create a new Azure Data Factory instance. • Configure the necessary settings, including the region and resource group. 2. Create Data Pipelines: • In Azure Data Factory, create a new pipeline to define the workflow for data movement and transformation. • Add activities to the pipeline, such as copy activities to move data between sources and destinations, and data flow activities to transform data. 3. Connect Data Sources: • Define your data sources in Azure Data Factory by creating linked services for each source, such as SQL databases, Blob storage, or REST APIs. • Ensure that the necessary permissions and authentication methods are configured for each data source. 4. Transform Data: • Use Data Flow activities within your pipeline to apply transformations to your data. • You can perform operations like filtering, joining, aggregating, and mapping data to ensure it’s in the desired format. 5. Schedule and Monitor Pipelines: • Set up triggers to schedule your pipelines to run at specific times or in response to certain events. • Use the monitoring tools in Azure Data Factory to track the progress of your pipelines, review logs, and handle any errors that occur. 6. Integrate with Power BI: • Output the transformed data to a destination that Power BI can access, such as Azure SQL Database or Azure Data Lake. • Connect Power BI to this data source to create interactive reports and dashboards. Benefits of Using Azure Data Factory with Microsoft Fabric: • Automate the process of moving and transforming data, reducing manual effort and increasing efficiency. • Handle large volumes of data and complex transformations with ease, thanks to the scalability of Azure Data Factory. • Easily integrate data from multiple sources into Power BI for comprehensive analysis and reporting. Pro Tips: • Parameterize your pipelines to make them more flexible and reusable across different data sources and scenarios. • Set up robust error handling and logging mechanisms to ensure you can quickly identify and resolve any issues in your pipelines. • Use Azure Integration Runtime or Self-hosted Integration Runtime to optimize data movement based on your network environment and data locality. Streamlining data integration with Microsoft Fabric and Azure Data Factory can transform your data management process, enabling you to efficiently move and transform data for deeper insights and better decision-making. #MicrosoftFabric #AzureDataFactory #DataIntegration #DataPipelines #BusinessIntelligence Toronto Fabric User Group 🍁 Microsoft Fabric

##OCTOBERLEARNS #AZUREDATAFACTORY 🌟 Unlock the Power of Data with #AzureDataFactory! 🌟 Are you looking to streamline your data integration and workflow automation? Here are compelling features of Azure Data Factory that you should know about:- 💡 What is Azure Data Factory? 🌐 A cloud-based ETL service enabling seamless data integration and workflow automation. ➡️ Ideal for organizations seeking to manage data at scale. 🚀 Data Pipeline Creation:- Easily create data pipelines to orchestrate movement and transformation across various sources and destinations. ➡️ Supports both batch and real-time data processing. 🔗 Hybrid Data Integration:- Connect to both on-premises and cloud data sources, facilitating a versatile data strategy. ➡️ Use the self-hosted integration runtime for secure on-premise connectivity. 📊 Support for Diverse Data Sources:- Integrate data from a wide range of sources, including Azure Blob Storage, SQL databases, and SaaS applications. ➡️ Provides connectors for over 90 different data sources. 🎨 Visual Interface:- Utilize an intuitive drag-and-drop interface for designing workflows—no coding needed! ➡️ Quick setup reduces the learning curve for new users. 🔄 Mapping Data Flows:- Transform data visually with mapping data flows, enhancing productivity and simplifying complexity. ➡️ Leverage built-in transformations like joins, aggregations, and conditional splits. 📈 Scalability:- Automatically scale resources to handle varying workloads efficiently. ➡️ Supports massive parallel processing for large data volumes. ⏰ Data Orchestration:- Orchestrate complex workflows with triggers and scheduling for automated data handling. ➡️ Create dependency chains to ensure tasks run in the correct order. 📊 Monitoring and Management:- Built-in tools provide real-time insights into pipeline performance for quick troubleshooting. ➡️ Set up alerts to notify stakeholders of pipeline failures or issues. 🌌 Integration with Azure Ecosystem:- Seamlessly connect with Azure services like Synapse Analytics, Functions, and Power BI for holistic data solutions. ➡️ Enable end-to-end analytics with integrated data flows into BI tools. 🔒 Security Features:- Implement robust security measures, including data encryption and role-based access control for secure handling. ➡️ Utilize Azure Active Directory for enhanced identity management. 🚀 Future-Proof Technology:- Regular updates from Microsoft ensure Azure Data Factory stays at the forefront of industry trends! ➡️ Take advantage of new features and enhancements as they are released. Ready to elevate your data strategy? Let’s connect and share insights! 💡 #AzureDataFactory #DataIntegration #ETL #CloudComputing #BigData #DataAnalytics #MicrosoftAzure #DataPipeline #MachineLearning #BusinessIntelligence #DataEngineering #Analytics #DataManagement #AI #DigitalTransformation #CloudSolutions #DataScience #DataStrategy #DataGovernance #TechTrends #SeekhoSeekho Bigdata Institute#Karthik K.

🎛 Day 4: Making Pipelines Dynamic with Parameters and Triggers “Boost Efficiency: Dynamic Pipelines with Parameters in ADF” Welcome to Day 4 of my 7-day series on Azure Data Factory (ADF)! Today, we’ll explore how to make your pipelines dynamic using parameters and triggers. By doing so, we can significantly reduce duplication, improve maintainability, and enhance the overall efficiency of our data workflows. 🧠 Key Components for Dynamic Pipelines: Parameters: Parameters allow you to pass values dynamically into your pipelines, such as file names, dates, or table names. This flexibility enables you to create generic pipelines that can handle different datasets without needing to duplicate the entire pipeline for each scenario. For example, you can set a parameter for a file path and change it each time you run the pipeline to process different files. Variables: Variables are used to store temporary values during the pipeline's runtime. This can be helpful for calculations or holding intermediary values that you want to use later in your pipeline activities. By defining variables, you can control the flow of your pipeline more effectively. Triggers: Triggers allow you to schedule your pipelines to run automatically based on specific criteria. ADF supports several types of triggers: Scheduled Triggers Tumbling Window Triggers Event-Based Triggers ForEach Activity: The ForEach activity lets you loop through a list of items (e.g., files, tables) and perform actions dynamically on each item. This is particularly useful when dealing with multiple files or records, as you can apply the same logic without hardcoding each item. 🔧 Pro Tip: Parameterized Datasets: Use parameterized datasets to create reusable components in your pipelines. This allows you to easily adjust the input and output datasets without duplicating your pipeline logic, making your data workflows more scalable and easier to manage. Benefits of Dynamic Pipelines: Efficiency: Reduces redundancy by allowing the same pipeline to handle multiple scenarios. Maintainability: Makes it easier to update and manage pipelines since you can change parameters and variables rather than the entire pipeline. Flexibility: Adapt to changing data requirements quickly without extensive rework. Automation: Triggers automate the execution of pipelines, ensuring timely data processing without manual intervention. Tomorrow, we’ll delve into data integration with on-premise systems, exploring how ADF can bridge the gap between your cloud and local data environments. Stay tuned! 🔗 👉 If you're finding value in this series, please like this post! 💬 Share your experiences with dynamic pipelines or ask any questions in the comments below. 🔄 Repost to help your network streamline their Azure Data Factory processes! #AzureDataFactory #DynamicPipelines #DataIntegration #DataEngineering #ETL #Automation #CloudData #BigData

IndustryARC™ Big Data Services In Small & Medium Business Market - Forecast(2024 - 2030) 👉 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐒𝐚𝐦𝐩𝐥𝐞 @ https://lnkd.in/gqV8Zd-z Competitive Advantage: SMBs are leveraging Big Data to gain insights into customer behavior, market trends, and competitive landscapes. This enables them to make data-driven decisions and stay competitive in their industries. Cost-Effective Solutions: With advancements in technology and the availability of cloud-based services, Big Data solutions have become more accessible and affordable for SMBs. They can now harness the power of data without significant upfront investments in infrastructure and technology. Scalability: Many Big Data service providers offer scalable solutions that can grow with the business. This scalability allows SMBs to start small and expand their data capabilities as their needs evolve. Improved Decision Making: Big Data analytics provide SMBs with actionable insights that can inform strategic decisions across various business functions, such as marketing, sales, operations, and customer service. This leads to more informed decision-making and better outcomes. Personalized Customer Experiences: SMBs can use Big Data analytics to understand customer preferences and behaviors on a granular level. This enables them to personalize their products, services, and marketing efforts, leading to higher customer satisfaction and loyalty. Risk Management: By analyzing large volumes of data, SMBs can identify potential risks and opportunities early on. Whether it's detecting fraudulent activities, predicting market trends, or assessing operational risks, Big Data analytics help SMBs mitigate risks and make proactive decisions. Operational Efficiency: Big Data services can streamline business operations by optimizing processes, reducing inefficiencies, and identifying areas for improvement. This can lead to cost savings and increased productivity for SMBs. 👉 𝐆𝐞𝐭 𝐌𝐨𝐫𝐞 𝐈𝐧𝐟𝐨 @ https://lnkd.in/gzwcxwVG 𝐊𝐞𝐲 𝐏𝐥𝐚𝐲𝐞𝐫𝐬:  Amazon Web Services (AWS) | Google Cloud - Minnesota | Microsoft Azure | IBM Cloud | Cloudera | Databricks | Snowflake | (Hewlett Packard Enterprise) | Qubole | Teradata | Exasol | Kognition AI | Talend | Sisense | Looker | Panoply. | Yellowbrick Data | Progress MarkLogic | Hortonworks NTT DATA | NEC Corporation | Fujitsu Ltd. | Hitachi Vantara | Accenture | Deloitte | PwC | Amazon Web Services (AWS) | Microsoft Azure | SAP | Salesforce | Oracle | SAS | TIBCO Software (SA) Pty Limited | MicroStrategy | Qlik | Teradata | #BigData #CloudComputing #DataAnalytics #AI #MachineLearning #DataScience #AWS #GoogleCloud #Azure #IBMCloud #Cloudera #Databricks #SnowflakeDB #HPE #Qubole #Teradata #DataWarehousing #AnalyticsPlatform #BusinessIntelligence #DigitalTransformation #usa #uk #market #global #report #japan #southkorea

IndustryARC™ Big Data Services In Small & Medium Business Market - Forecast(2024 - 2030) 👉 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐒𝐚𝐦𝐩𝐥𝐞 @ https://lnkd.in/gqV8Zd-z Competitive Advantage: SMBs are leveraging Big Data to gain insights into customer behavior, market trends, and competitive landscapes. This enables them to make data-driven decisions and stay competitive in their industries. Cost-Effective Solutions: With advancements in technology and the availability of cloud-based services, Big Data solutions have become more accessible and affordable for SMBs. They can now harness the power of data without significant upfront investments in infrastructure and technology. Scalability: Many Big Data service providers offer scalable solutions that can grow with the business. This scalability allows SMBs to start small and expand their data capabilities as their needs evolve. Improved Decision Making: Big Data analytics provide SMBs with actionable insights that can inform strategic decisions across various business functions, such as marketing, sales, operations, and customer service. This leads to more informed decision-making and better outcomes. Personalized Customer Experiences: SMBs can use Big Data analytics to understand customer preferences and behaviors on a granular level. This enables them to personalize their products, services, and marketing efforts, leading to higher customer satisfaction and loyalty. Risk Management: By analyzing large volumes of data, SMBs can identify potential risks and opportunities early on. Whether it's detecting fraudulent activities, predicting market trends, or assessing operational risks, Big Data analytics help SMBs mitigate risks and make proactive decisions. Operational Efficiency: Big Data services can streamline business operations by optimizing processes, reducing inefficiencies, and identifying areas for improvement. This can lead to cost savings and increased productivity for SMBs. 👉 𝐆𝐞𝐭 𝐌𝐨𝐫𝐞 𝐈𝐧𝐟𝐨 @ https://lnkd.in/gzwcxwVG 𝐊𝐞𝐲 𝐏𝐥𝐚𝐲𝐞𝐫𝐬:  Amazon Web Services (AWS) | Google Cloud - Minnesota | Microsoft Azure | IBM Cloud | Cloudera | Databricks | Snowflake | (Hewlett Packard Enterprise) | Qubole | Teradata | Exasol | Kognition AI | Talend | Sisense | Looker | Panoply. | Yellowbrick Data | Progress MarkLogic | Hortonworks NTT DATA | NEC Corporation | Fujitsu Ltd. | Hitachi Vantara | Accenture | Deloitte | PwC | Amazon Web Services (AWS) | Microsoft Azure | SAP | Salesforce | Oracle | SAS | TIBCO Software (SA) Pty Limited | MicroStrategy | Qlik | Teradata | #BigData #CloudComputing #DataAnalytics #AI #MachineLearning #DataScience #AWS #GoogleCloud #Azure #IBMCloud #Cloudera #Databricks #SnowflakeDB #HPE #Qubole #Teradata #DataWarehousing #AnalyticsPlatform #BusinessIntelligence #DigitalTransformation #usa #uk #market #global #report #japan #southkorea

🔄 Streamline Your Data Workflows with Azure Data Factory Triggers and Key Vaults! 🔐 In today's data-driven world, automation and security are crucial for efficient data management. Azure Data Factory (ADF) offers powerful tools to achieve just that. Let's dive into how ADF triggers and Azure Key Vaults can enhance your data workflows! 🚀 ### Azure Data Factory Triggers ADF triggers are essential for scheduling and automating data pipelines. They ensure your data integration processes run seamlessly, without manual intervention. Here's a quick overview of the different types of triggers: - **Schedule Trigger** 🕒: Automate pipeline execution based on a specific schedule. Ideal for routine tasks like daily data ingestion or ETL processes.    - **Tumbling Window Trigger** ⏳: Execute pipelines in a recurring, non-overlapping time interval. Perfect for time-series data processing and batch jobs.    - **Event Trigger** 🚨: Trigger pipelines based on events, such as file creation or deletion. Great for real-time data processing and event-driven workflows. ### Azure Key Vault Security is paramount, and Azure Key Vault ensures your secrets, keys, and certificates are safely stored and accessed. Here's why you should integrate Key Vault with ADF: - **Secure Storage** 🔒: Store connection strings, API keys, and other secrets securely.    - **Access Control** 🛡️: Manage access to secrets using Azure Active Directory (AAD).    - **Simplified Management** ⚙️: Centralize management of keys and secrets, reducing the risk of exposure. ### Integration in Action **Set Up a Schedule Trigger:** Create a schedule trigger in ADF to run your pipeline daily at 2 AM. Ensure timely data processing without manual intervention. **Secure Your Pipeline:** Store your database connection string in Azure Key Vault. Use ADF’s Key Vault integration to access the secret securely during pipeline execution. ### Benefits - **Automation**: Triggers enable hands-free operation, reducing manual errors.    - **Security**: Key Vault ensures sensitive information is protected and managed efficiently.    - **Efficiency**: Streamline workflows and improve operational efficiency. Harness the power of Azure Data Factory triggers and Azure Key Vaults to automate and secure your data pipelines today! 🌐🔑 #AzureDataFactory #AzureKeyVault #DataAutomation #DataSecurity #CloudComputing #DataIntegration #ETL #MicrosoftAzure #DataManagement

#ADF Boost Data Pipeline Efficiency with #Azure Data Factory’s #CDC Feature Discover the Simplified Setup and Cost-Effective Solution for Change Data Capture in ADF Azure Data Factory (ADF) Change Data Capture (CDC) feature that streamlines the process of configuring and managing CDC processes. an overview of the recent capabilities, explain how to configure them and go over the advantages of using CDC in your ADF pipelines. What is Change Data Capture? ADF’s Change Data Capture feature allows you to track changes in your data sources and process those changes incrementally rather than processing the entire dataset each time. This can improve the efficiency and speed of your data processing workflows significantly. Previously, ADF users had to create data flows and pipelines in order to implement CDC, but the feature is now available as a top-level Factory resource. Setting up Change Data Capture Creating a new CDC resource, selecting your data source, configuring the mapping, and configuring the latency are all steps in the process. 1.    Create a new CDC resource: In the Factory resources panel, click the ‘+’ button and select ‘Change Data Capture’ to begin creating a new CDC resource. 2. Select your source: Choose a supported data source from the list, such as delimited text, SQL Server, or PostgreSQL. Enter the required connection information (link service) and select the folder or table containing your source data. 3. Configure the mapping: Name the mapping between your source and target and select the destination for your processed data (for example, SQL database or Delta Lake). ADF will automatically map the columns based on matching, but you can manually adjust the mappings if necessary. 4. Configure the latency: Determine how frequently ADF should check for changes in your source data. Options available range from 15 minutes to 2 hours or even real-time. After you’ve finished the setup, you’ll need to publish your CDC configuration, which will create the processes needed to run your CDC workflow. Monitoring Change Data Capture You can track the status of your CDC process by selecting the ‘Monitor’ option from the main CDC resource screen. This will take you to a monitoring section that displays the number of rows read and written, as well as the status of each occurrence. Benefits of using Change Data Capture in ADF Using the new CDC capabilities in ADF offers several benefits, including: 1.    Improved efficiency: CDC allows you to process only the changes in your data, reducing the amount of processing required and improving the overall efficiency of your data pipelines. 2.  Simplified setup: The new CDC resource simplifies the process of configuring and managing CDC workflows, making it easier for users to implement and maintain their data pipelines. 3.   Cost-effective: As you are billed only for the CDC process and not for the pipelines, using the new CDC feature can be a more economical solution for processing your data.

Discover the Power of #Microsoft #Fabric Database Mirroring Microsoft Fabric’s new Database Mirroring feature is set to revolutionize data management and analytics. This innovative feature enables seamless data integration and real-time replication, offering unprecedented operational efficiency and insights with just a few clicks. The Need for Data-Driven Insights In today's data-driven world, businesses strive to leverage insights to innovate, make informed decisions, and enhance their products and services. However, disparate data sources and formats often hinder efficient data analysis and cross-referencing, leading to costly and time-consuming migration projects. Introducing Microsoft Fabric Mirroring Microsoft Fabric’s mirroring feature addresses these challenges by simplifying the data integration process. It reduces procedures that traditionally took hours or days to just a few clicks and seconds. This functionality allows continuous and seamless access to data from various sources into OneLake, eliminating the need for pipelines and enabling near real-time insights. How It Works Mirroring in Microsoft Fabric does not require additional software installation or database client changes. By entering connection details and securely logging in, databases can be instantly accessible in Fabric as Mirrored databases. The feature supports Azure SQL Database, Cosmos DB, and Snowflake databases, among others. 1. Name Your Mirrored Database:Assign a name to your mirrored database in Fabric. 2. Configure the Connection: A new connection setup will appear. 3. Select Data to Mirror: Choose to mirror all data or select specific tables Real-Time, Frictionless Data Replication The mirroring process updates data consistently and reliably with the same connection details, eliminating the need for complex ETL setups. Data is synchronized in near real-time, ensuring up-to-date information for decision-making. Using Change Data Capture (CDC) CDC technology in the source database transforms and uploads data into Delta tables within OneLake. This ensures data is always available for any Fabric task, with efficient use of computational resources and control over mirroring operations and data refresh status. SQL Endpoint The SQL Endpoint feature allows running queries on mirrored tables, cross-referencing data, creating semantic models, and building reports. It leverages all Fabric features for enhanced data analysis and decision-making. Benefits - Elimination of Data Silos: Centralize and govern data within Fabric OneLake. - Cost and Time EfficiencySimplified data migration and integration with minimal human intervention. - Enhanced Decision-Making: Access near real-time data insights for informed business decisions. For more insights and updates, follow me on LinkedIn. Your support means the world! 😊 #MicrosoftFabric #DataManagement #DataAnalytics #DatabaseMirroring #OperationalEfficiency #RealTimeData #DataIntegration

You don’t have to rip out all of Fivetran. Just replace the connectors causing the problems. Think about it. Fivetran does point-to-point replication from each source to a single destination. It then uses dbt to transform and merge data. If you turn off a source connector, move the data a different way, and modify your dbt to use the new data, does it really matter? It’s the fastest way to fix two problems with specific Fivetran connectors: cost and CDC reliability. We’ve had several companies do it. In case you didn’t already know, some of Fivetran’s non-relational and SaaS connectors are expensive. Fivetran charges on monthly active rows (MAR), or rows that change at least once a month. But they charge based on their internal format, not your format. Some connectors are expensive because they force you to extract all data in the source. Other non-relational sources cost a lot because Fivetran transforms the data into a highly normalized relational data format that ends up “generating” a lot of MARs. If you’re using one of these connectors, you know. Make sure you benchmark your cost first by measuring your changing rows. Otherwise you’ll see it in your first bill. The other issue is CDC reliability. Fivetran is batch-only CDC that replicates at the rate  you choose. You can theoretically pay a lot more for minute-level latency. But most customers choose an hour or more because that’s what makes sense for the data warehouse. The problem is with CDC, longer intervals put more load on the source database, or even cause database failures. We have several customers who have replaced Fivetran CDC connector(s), and some other expensive connectors in a few days. They made their CDC reliable and cut costs 2-5x. Here’s how they did it: - They connected to a database using CDC, or to SaaS sources including Netsuite, Salesforce, Hubspot, ad tech sources, and others. It generally takes a few minutes. - They started to capture the source data in real-time. Estuary Flow stored the data in their own storage. This let them load into their destination(s) at any interval - They modified their dbt to transform the (slightly) different data format to whatever they needed for the data warehouse. Estuary supports dbt. You can’t control the way Fivetran transforms your data, so there will be some work. But with Estuary you can reuse your existing dbt work from Fivetran. It’s been pretty fast for other customers to make these changes. Test it out. Load into a different landing table and compare your Fivetran data side-by-side with Estuary’s. I’ve included links to the Fivetran replacement guide, and also to Estuary, which is free for up to 10GB each month. Also, reach out if you want more info on best practices for replacing Fivetran connections. We’ve captured some best practices that have helped companies migrate faster.

🚀 Unlock the Power of Azure Data Factory (ADF) Triggers! 🚀 ⏰Day 02: ADF Are you ready to elevate your data integration and transformation workflows to the next level? Dive into the realm of ADF Triggers and revolutionize the way you manage your data pipelines. Let's explore what triggers are and their types with some stellar examples: 🎯 Triggers in ADF: Triggers in Azure Data Factory are event-driven mechanisms that initiate the execution of a pipeline. They serve as catalysts for your data workflows, ensuring seamless automation and timely execution. 🔍 Types of Triggers: ➡ Schedule Trigger: Time-based trigger that executes a pipeline on a predefined schedule. 🕒 Example: Running a daily ETL process to extract, transform, and load data from various sources into a data warehouse. ➡Event Trigger: Responds to events such as file arrival in a storage account, a message in a queue, or completion of another pipeline. 📁 Example: Triggering data ingestion pipeline upon the arrival of new files in a cloud storage container. ➡Tumbling Window Trigger: Executes a pipeline at regular intervals, typically for batch processing scenarios. 🔄 Example: Aggregating daily sales data into weekly summaries for reporting purposes. ➡Data Driven Trigger: Dynamically triggers pipeline runs based on data availability or conditions. 📊 Example: Initiating a data validation pipeline upon the availability of new data in a source system. ➡custom event trigger: If you combine pipeline parameters and a custom event trigger, you can parse and reference custom data payloads in pipeline runs. Because the data field in a custom event payload is a free-form, JSON key-value structure, you can control event-driven pipeline runs.🎯 To use the custom event trigger in Data Factory, you need to first set up a custom topic in Event Grid. 🔍 Example Use Case: Scenario: Imagine you're managing an e-commerce platform where customer interactions play a pivotal role in driving business insights. You want to trigger a pipeline whenever a customer leaves a product review, enabling real-time sentiment analysis and feedback aggregation. Custom Event Trigger: In this scenario, you can set up a custom event trigger in Azure Data Factory to monitor a designated database or message queue for new product reviews. Once a new review is detected, the trigger initiates the execution of a sentiment analysis pipeline, extracting valuable insights from customer feedback in real-time. 🌟 Why ADF Triggers? Efficiency: Automate data workflows to run precisely when needed. Reliability: Ensure data pipelines are triggered promptly in response to events. Flexibility: Adapt to diverse data integration scenarios with various trigger types. Harness the power of ADF triggers to streamline your data processes and propel your business towards data-driven success! 💡💼 #DataIntegration #Automation #DataEngineering Ready to take the plunge into ADF Triggers? Explore more: [#GritSetGrow #azuredatafactory ]