Sravya Thavidishetty’s Post

𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐯𝐬 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐅𝐚𝐛𝐫𝐢𝐜: 𝐂𝐨𝐦𝐩𝐮𝐭𝐞 𝐂𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐖𝐨𝐫𝐤𝐥𝐨𝐚𝐝𝐬 When working with PySpark notebooks, one key decision is how your compute is configured behind the scenes. Comparing Azure Databricks and Microsoft Fabric reveals two very different approaches. 🔹 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 uses a 𝐜𝐥𝐮𝐬𝐭𝐞𝐫-𝐛𝐚𝐬𝐞𝐝 𝐦𝐨𝐝𝐞𝐥, where you define VM types, scaling, and workload isolation. What makes it powerful is advanced optimizations like 𝐏𝐡𝐨𝐭𝐨𝐧 𝐞𝐧𝐠𝐢𝐧𝐞 a vectorized execution engine that significantly boosts SQL and Spark performance. Another key innovation is 𝑳𝒊𝒒𝒖𝒊𝒅 𝑪𝒍𝒖𝒔𝒕𝒆𝒓𝒊𝒏𝒈, which replaces traditional partitioning and reduces the need for manual tuning. This is especially useful for large-scale PySpark workloads with evolving data patterns. 🔹 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐅𝐚𝐛𝐫𝐢𝐜 follows a 𝐟𝐮𝐥𝐥𝐲 𝐒𝐚𝐚𝐒 𝐚𝐧𝐝 𝐬𝐞𝐫𝐯𝐞𝐫𝐥𝐞𝐬𝐬 𝐦𝐨𝐝𝐞𝐥. You don’t manage clusters instead, compute is abstracted through capacity (F-SKUs). Features like 𝐁𝐮𝐫𝐬𝐭𝐢𝐧𝐠 (temporary compute boost) and 𝑺𝒎𝒐𝒐𝒕𝒉𝒊𝒏𝒈 (balancing usage over time) simplify performance management. 💡 Key Difference: Databricks = Performance tuning + deep control Fabric = Simplicity + fully managed compute #Databricks #MicrosoftFabric #PySpark #DataEngineering #BigData #Azure #DataArchitecture #Analytics #CloudComputing #DataPlatform #ETL #DataAnalytics #Lakehouse #AI #MachineLearning #TechCareers #Hiring #Recruiters #DataJobs #DataEngineering #AzureDataEngineer #SparkSQL #MicrosoftPartner #RecruitingInTech #CloudComputing #ETL #DataLakehouse

Most ML models fail not because of algorithms… but because of data. I realized this while building a scalable data pipeline using AWS EMR & PySpark on NYC taxi data 🚖 Even after training multiple models, the accuracy was still limited — and that’s when it clicked: 👉 Real-world data is messy 👉 Missing features matter more than models 👉 Scalability matters more than complexity 🔧 What I built: • End-to-end pipeline: S3 → EMR → PySpark → MLlib → Visualization • Processed large-scale taxi data using distributed computing • Trained ML models (Linear Regression, Random Forest, Gradient Boosted Trees) 📊 Key insight: Trip duration is heavily influenced by distance and time-of-day, but hard to predict due to missing real-world factors like traffic and weather. 📌 Architecture diagram attached below 👇 Still learning, but this project gave me real exposure to big data systems and ML at scale. Would love feedback from the community! #AWS #PySpark #DataEngineering #MachineLearning #BigData #Analytics #LearningInPublic #OpenToWork

## **7. Azure Databricks** Azure Databricks is a unified analytics platform that combines big data processing and machine learning. Built on Apache Spark, it provides a collaborative environment for data engineers, data scientists, and analysts. One of the key strengths of Azure Databricks is its performance. It enables fast data processing and supports large-scale analytics workloads. The platform offers a collaborative workspace where teams can share notebooks, run experiments, and build models together. This enhances productivity and innovation. Azure Databricks integrates seamlessly with other Azure services, such as Data Lake and Data Factory. This allows organizations to build end-to-end data pipelines. It supports multiple programming languages, including Python, SQL, and Scala, making it versatile for different use cases. Machine learning capabilities are also a major highlight. Users can build, train, and deploy models efficiently using integrated tools. Security and governance are built into the platform, ensuring data is protected and compliant with industry standards. Azure Databricks is a powerful tool for modern data analytics. It enables organizations to process data at scale and derive meaningful insights. #AzureDatabricks #BigData #MachineLearning #DataEngineering #ApacheSpark #CloudAnalytics #Azure #DataScience

## **7. Azure Databricks** Azure Databricks is a unified analytics platform that combines big data processing and machine learning. Built on Apache Spark, it provides a collaborative environment for data engineers, data scientists, and analysts. One of the key strengths of Azure Databricks is its performance. It enables fast data processing and supports large-scale analytics workloads. The platform offers a collaborative workspace where teams can share notebooks, run experiments, and build models together. This enhances productivity and innovation. Azure Databricks integrates seamlessly with other Azure services, such as Data Lake and Data Factory. This allows organizations to build end-to-end data pipelines. It supports multiple programming languages, including Python, SQL, and Scala, making it versatile for different use cases. Machine learning capabilities are also a major highlight. Users can build, train, and deploy models efficiently using integrated tools. Security and governance are built into the platform, ensuring data is protected and compliant with industry standards. Azure Databricks is a powerful tool for modern data analytics. It enables organizations to process data at scale and derive meaningful insights. #AzureDatabricks #BigData #MachineLearning #DataEngineering #ApacheSpark #CloudAnalytics #Azure #DataScience

## **7. Azure Databricks** Azure Databricks is a unified analytics platform that combines big data processing and machine learning. Built on Apache Spark, it provides a collaborative environment for data engineers, data scientists, and analysts. One of the key strengths of Azure Databricks is its performance. It enables fast data processing and supports large-scale analytics workloads. The platform offers a collaborative workspace where teams can share notebooks, run experiments, and build models together. This enhances productivity and innovation. Azure Databricks integrates seamlessly with other Azure services, such as Data Lake and Data Factory. This allows organizations to build end-to-end data pipelines. It supports multiple programming languages, including Python, SQL, and Scala, making it versatile for different use cases. Machine learning capabilities are also a major highlight. Users can build, train, and deploy models efficiently using integrated tools. Security and governance are built into the platform, ensuring data is protected and compliant with industry standards. Azure Databricks is a powerful tool for modern data analytics. It enables organizations to process data at scale and derive meaningful insights. #AzureDatabricks #BigData #MachineLearning #DataEngineering #ApacheSpark #CloudAnalytics #Azure #DataScience

## **7. Azure Databricks** Azure Databricks is a unified analytics platform that combines big data processing and machine learning. Built on Apache Spark, it provides a collaborative environment for data engineers, data scientists, and analysts. One of the key strengths of Azure Databricks is its performance. It enables fast data processing and supports large-scale analytics workloads. The platform offers a collaborative workspace where teams can share notebooks, run experiments, and build models together. This enhances productivity and innovation. Azure Databricks integrates seamlessly with other Azure services, such as Data Lake and Data Factory. This allows organizations to build end-to-end data pipelines. It supports multiple programming languages, including Python, SQL, and Scala, making it versatile for different use cases. Machine learning capabilities are also a major highlight. Users can build, train, and deploy models efficiently using integrated tools. Security and governance are built into the platform, ensuring data is protected and compliant with industry standards. Azure Databricks is a powerful tool for modern data analytics. It enables organizations to process data at scale and derive meaningful insights. #AzureDatabricks #BigData #MachineLearning #DataEngineering #ApacheSpark #CloudAnalytics #Azure #DataScience

## **7. Azure Databricks** Azure Databricks is a unified analytics platform that combines big data processing and machine learning. Built on Apache Spark, it provides a collaborative environment for data engineers, data scientists, and analysts. One of the key strengths of Azure Databricks is its performance. It enables fast data processing and supports large-scale analytics workloads. The platform offers a collaborative workspace where teams can share notebooks, run experiments, and build models together. This enhances productivity and innovation. Azure Databricks integrates seamlessly with other Azure services, such as Data Lake and Data Factory. This allows organizations to build end-to-end data pipelines. It supports multiple programming languages, including Python, SQL, and Scala, making it versatile for different use cases. Machine learning capabilities are also a major highlight. Users can build, train, and deploy models efficiently using integrated tools. Security and governance are built into the platform, ensuring data is protected and compliant with industry standards. Azure Databricks is a powerful tool for modern data analytics. It enables organizations to process data at scale and derive meaningful insights. #AzureDatabricks #BigData #MachineLearning #DataEngineering #ApacheSpark #CloudAnalytics #Azure #DataScience

## **7. Azure Databricks** Azure Databricks is a unified analytics platform that combines big data processing and machine learning. Built on Apache Spark, it provides a collaborative environment for data engineers, data scientists, and analysts. One of the key strengths of Azure Databricks is its performance. It enables fast data processing and supports large-scale analytics workloads. The platform offers a collaborative workspace where teams can share notebooks, run experiments, and build models together. This enhances productivity and innovation. Azure Databricks integrates seamlessly with other Azure services, such as Data Lake and Data Factory. This allows organizations to build end-to-end data pipelines. It supports multiple programming languages, including Python, SQL, and Scala, making it versatile for different use cases. Machine learning capabilities are also a major highlight. Users can build, train, and deploy models efficiently using integrated tools. Security and governance are built into the platform, ensuring data is protected and compliant with industry standards. Azure Databricks is a powerful tool for modern data analytics. It enables organizations to process data at scale and derive meaningful insights. #AzureDatabricks #BigData #MachineLearning #DataEngineering #ApacheSpark #CloudAnalytics #Azure #DataScience

🚀 Are Your Azure Data Pipelines Truly Scalable — or Just Working? In today’s data-driven world, building pipelines is easy… But building reliable, scalable, and production-grade Azure data pipelines? That’s where real engineering begins. Over the past few years working with Azure Data Factory, Azure Databricks, and Azure Synapse, one thing has become very clear: 👉 Pipelines don’t fail in development — they fail in production scale. Here are a few key lessons I’ve learned while building enterprise-grade data pipelines: 🔹 Design for Failure, Not Success Always assume pipelines will break — implement retries, alerts, and fallback mechanisms from day one. 🔹 Partitioning & Incremental Loads are Game-Changers Full loads don’t scale. Smart partitioning + CDC = massive performance gains. 🔹 Data Quality is NOT Optional Bad data pipelines = bad business decisions. Validation layers are just as important as transformations. 🔹 Performance Tuning ≠ Afterthought Optimizing Spark jobs (parallelism, caching, file sizes) can reduce processing time by 50%+. 🔹 Orchestration Matters More Than Tools ADF is powerful — but how you design dependencies, triggers, and modular pipelines defines success. 🔹 Security & Governance = Production Readiness Key Vault, RBAC, and proper data lineage tracking are no longer “nice to have”. 💡 The real shift? We’re no longer just building pipelines… We’re building data platforms that power AI, analytics, and real-time decision-making. 🔍 I’m actively exploring and working on modern Azure data architectures, real-time pipelines, and scalable data engineering solutions. If you're a recruiter, hiring manager, or fellow data engineer, let’s connect 🤝 Always open to discussing Data Engineering, Azure, Databricks, and Big Data innovations. #DataEngineering #Azure #AzureDataFactory #Databricks #BigData #DataPipelines #DataArchitecture #CloudComputing #ETL #ELT #OpenToWork

Recently, I worked on migrating data from azure storage account to gcp storage in Databricks. One key challenge after the transfer was ensuring data integrity. Instead of comparing data row by row (which is not scalable), I used a more efficient approach: • Compared table level statistics (row counts, aggregates) between source and target • Generated row level hashes by concatenating all columns and applying a hash function • Compared hashes across both datasets to validate consistency This approach turned out to be both efficient and reliable for large scale data validation. During this , I asked with a senior. “If we are moving data from one source to another, why do we need validation? How can it even change?” And he replied “We can’t take risks. No matter the scenario, we can’t assume the data is perfectly migrated. Validation is a must in data engineering.” That really stuck with me. A simple technique, but incredibly powerful when working with big data. #DataEngineering #Databricks #BigData #DataQuality #DataIntegrity #ETL #ELT #DataPipeline #DataMigration #DataValidation #DataOps #DataArchitecture #DataLake #CloudComputing #AzureData #Spark #PySpark #DeltaLake #AnalyticsEngineering #DataPlatform #DataTransformation #DataGovernance #DataTesting #DataReliability #ScalableSystems #DistributedSystems #DataProcessing #TechInnovation #AIEngineering #MachineLearning #DataScience #EngineeringLife #TechCommunity #LearningInPublic #CodeNewbie #Developers #WomenInTech #OpenToWork #CareerGrowth #LinkedInTech