Mohit Agarwal’s Post

Thrilled to announce I’ve passed the Databricks azure architect course - Azure Databricks is a very easy to use platform. It is designed to scale quickly as it is suitable for small jobs and can also process very large massive files without splitting them, saving effort and time for development of other projects. This makes it the ideal platform for organizations with large amounts of data, providing greater productivity and collaboration. #DatabricksCertified #BigDataSolutions #AzurePlatform

🚀 Exciting News! 🚀 I am thrilled to share that I have successfully completed the Azure Databricks Platform Architecture certification! What I’ve Learned: First-Party Service Relationship: Understanding the unique collaboration between Databricks and Microsoft, which leverages the best of both platforms. Platform Administration & Architecture: Gaining insights into the roles and responsibilities of a Platform Administrator/Architect in implementing Azure Databricks. Azure Cloud Ecosystem: Mastering foundational concepts and how Databricks integrates within the Azure ecosystem. Cost Management & Planning: Learning to manage and plan for platform costs effectively. Implementation Decisions: Review critical decisions for implementing Azure Databricks in various architectures. Resource Identification & Deployment: Identifying the resources needed and deploying Azure Databricks workspaces using default methods and VNet Injection. Automation with Terraform: Exploring how Terraform can streamline the deployment process of Azure Databricks. Why This Matters: This certification is a significant milestone in my journey as a Data Engineer. It solidifies my understanding of Azure Databricks and enhances my ability to implement and manage robust data solutions in the cloud. The skills and knowledge gained through this course are crucial for driving data-driven decision-making and optimizing data workflows in any organization. What’s Next? I am excited to apply these new skills in real-world scenarios and continue my learning journey in data engineering and cloud computing. Stay tuned for more updates on my projects and further certifications! #AzureDatabricks #DataEngineering #CloudComputing #Certification #LearningJourney #DataScience #Azure #Databricks #ProfessionalDevelopment #TechSkills

Databricks Platform Architect #2 - Azure Even though Databricks is available as a first party service in Azure, there are lot of design patterns to deploy Databricks on Azure in Safe and secured manner. This track covers various aspects like - Key considerations while selecting DBU's, Cost saving features, Default deployments, VNet injection deployments, VNet injection + Secure cluster connectivity with No Public IP(NPIP), Private Link connections(Private Endpoints), Service Endpoints, Deploying Databricks using Terraform, AAD-SCIM, B2B, B2C, Unity Catalog deployments, Software defined Networks(SDN) implications, IP Access lists, User defined Routes(UDR's), Data loss prevention methods, DNS, Azure Cloud integrations with ADF, External storage locations, Power BI etc. Again another complicated exam with 80% as pass percentage. Many thanks to Tiger Analytics for the guidance and support. #tigertribe #databrickslearning #databricksplatformarchitect #databricksplatform #databricksplatformarchitectazure

As the year comes to a close, thrilled to announce the completion of the Databricks Azure Platform Architecture certification! 🎓 Grateful for the growth and learning in 2023. Ready to kick off the new year by applying these skills to drive impactful solutions. Wishing everyone a Happy New Year filled with success and endless possibilities! 🌟 #CertificationComplete #NewYearNewSkills #Azure #Databricks #DataAnalytics #databrickslearning #databrickscertified

🚀 Exciting News! Thrilled to share that I've achieved a new milestone in my journey by obtaining the Azure Databricks Architect Certification! 🎉 🔍 About the Certification: This certification, closely aligned with my main focus on Microsoft Azure, has empowered me with in-depth knowledge in architecting solutions using Azure Databricks. 💡 Key Learnings: - Optimizing Workloads: I gained insights into optimizing and managing big data workloads efficiently. - Scalable Architectures: Explored scalable architectures to handle diverse data processing requirements. - Integration with Azure Services: Learned to seamlessly integrate Azure Databricks with other Azure services for comprehensive solutions. - Advanced Analytics: Enhanced skills in performing advanced analytics and machine learning within the Databricks environment. - Data Engineering Best Practices: Acquired best practices in data engineering for robust and reliable data pipelines. A heartfelt thanks to the Databricks - Capgemini #partnership for providing this incredible opportunity for growth. Special gratitude to Pablo Hidalgo for being instrumental in providing all the resources throughout this certification journey. This certification not only strengthens my expertise in #Azure but also demonstrates a commitment to delivering high-quality solutions in the dynamic realm of #data and #analytics. Excited to apply this knowledge to drive innovation and efficiency in future projects! 🚀💻 #DatabricksArchitect #MicrosoftAzure #DataEngineering #ContinuousLearning #DataAnalytics #InnovationInTech #GetTheFutureYouWant #Databricks #Capgemini

I’m excited to announce that I’ve achieved the Academy Accreditation for Azure Databricks Platform Architect from #Databricks! 🎉🌟 Here’s a concise summary of the key differences between AWS Databricks and Azure Databricks 1. Control Plane and Compute Plane: - Both platforms have a control plane (backend services) managed by Databricks. - In AWS, compute resources (classic compute plane) are in your account. - In Azure, compute resources (VMs) handle most computation. - Azure Databricks also has a serverless compute plane for specific workloads. 2. Data Storage and Results: - AWS: Data lake remains in your AWS account; job results are stored there. - Azure: Data lake is stored in your Azure account; similar storage pattern for results. 3. High-Level Architecture: - Architectural details vary, but both platforms follow a similar structure. - Azure Databricks integrates seamlessly with other Azure services. #databricks #dataengineering #azure #architect #learning #perpetualLearner

"Transform your data chaos into a symphony of insights! 🎶 Explore the creative power of Azure Data Factory in our latest blog. 🚀 #Azure #DataFactory #DataOrchestration #TechInnovation #DataManagement"

Finally, I have earned the DP-203: Data Engineering on Microsoft Azure certification! Throughout my certification journey, I delved deep into various aspects of data engineering, including data ingestion, transformation, storage, and analysis using Azure services such as Azure Data Factory, Azure Databricks, and Azure Synapse Analytics. I am excited to leverage my newly acquired skills and knowledge to collaborate with organizations and teams on innovative data projects. Let's connect and explore how I can contribute to your data initiatives! #DP203 #MicrosoftCertification #DataEngineering #Azure #Certified #CareerDevelopment #DataSolutions #DataInsights

Azure Data Factory pipelines serve as the backbone for orchestrating data integration workflows within the Azure ecosystem. These pipelines are composed of interconnected activities that facilitate the movement, transformation, and processing of data from diverse sources to desired destinations. Pipelines in Azure Data Factory provide a visual representation of the entire data workflow, enabling users to design, monitor, and manage data integration tasks efficiently. They support various activities such as data copying, transformation, and control flow operations, allowing for complex data processing scenarios. Furthermore, Azure Data Factory pipelines offer flexibility and scalability, enabling organizations to handle large volumes of data seamlessly. With features like parameterization and dynamic content, pipelines can adapt to changing data requirements and scenarios, ensuring robustness and reliability in data workflows. Overall, Azure Data Factory pipelines empower businesses to streamline their data integration processes, accelerate decision-making, and derive valuable insights from their data assets with ease and efficiency. #azure #aws #Python #SQL #cloud #dataengineering