How MyData Insights solved data challenges for a healthcare provider with a modern data warehouse.

Which costs more: investing time in effective data architecture or paying for the inefficiencies it spawns? Think about that for a moment. Many companies still stick to outdated architectures hoping to flexibly scale, only to find their cloud bills spiraling out of control due to under-optimized ETL processes. I’ve seen firsthand how poorly designed pipelines can create bottlenecks, lead to late reporting, and frustrate teams chasing insights. Take, for example, a finance startup I worked with. They relied on manual data aggregation in their BI tool, resulting in a 30% increase in operational costs due to wasted hours and lost opportunities. It wasn't just the technology; it was a deep-rooted culture of hesitance to innovate and streamline processes. Moving to a more efficient, cloud-native architecture transformed their operational efficiency—reducing their data processing time by 70% and cutting costs significantly. 🚀 As CTOs and data leaders, we face the difficult challenge of not just adopting new tools, but fostering a mindset that prioritizes data ownership and efficiency. Are your current BI tools aligning with your strategic vision? Or are they hindering you? Let’s discuss! #DataAnalytics #CloudEngineering #BusinessIntelligence #ETL #DataLeadership #Efficiency #DataStrategy #Analytics Disclaimer: This is an AI-generated post. Can make mistakes.

This diagram illustrates the key components of Azure Data Factory (ADF) and how they interact to build and manage data integration workflows. Let’s go through it step by step: 1. Triggers Triggers define when a pipeline should run. Examples: Schedule-based (e.g., run every night at midnight) Event-based (e.g., new file uploaded to storage) Manual (triggered by a user or external system) They automate the execution of data pipelines. 2. Pipelines A pipeline is a logical grouping of activities that together perform a task. It acts like a container that defines the flow of data. Pipelines orchestrate activities such as moving, transforming, or processing data. 3. Activities Activities are the actual tasks executed inside a pipeline. Two key activities shown in the diagram: Copy Data → Moves data from a source to a destination. Transform Data (Data Flow) → Cleans, aggregates, or transforms the data before storing it. You can combine multiple activities in one pipeline to build complex workflows. 4. Datasets A dataset represents the structure of data (like a schema or table). It points to the data you want to use inside an activity. For example: A dataset could represent a CSV file in Blob storage. Or a table in SQL Database. 5. Linked Services Linked Services are connection strings that define how ADF connects to external resources. They store authentication details (like keys, credentials). Examples in the diagram: HTTP service Databases Storage accounts Datasets use linked services to know where the actual data lives. 6. Flow of Execution A Trigger starts the pipeline. The Pipeline executes a sequence of Activities. Activities rely on Datasets to know what data they are working with. Datasets use Linked Services to connect to external systems. Data is either copied from source to destination, or transformed along the way. ✅ In short: This diagram shows that Azure Data Factory integrates Triggers → Pipelines → Activities → Datasets → Linked Services to move and transform data. Triggers start pipelines, pipelines contain activities, activities work on datasets, and datasets rely on linked services to connect to external data sources.

𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼-𝗕𝗮𝘀𝗲𝗱 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 : 𝗣𝗮𝗿𝘁 𝟴 🔥 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿 🕵🏻♀️: You are designing a pipeline in Microsoft Fabric. How would you decide when to use Dataflows Gen2 vs Data Pipelines? 𝗖𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲 👩🏻💻: Use Dataflows Gen2 for low-code data transformation scenarios where Power Query is enough and Data Pipelines for orchestration of complex ETLs involving multiple sources, job scheduling and monitoring. 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿 🕵🏻♀️: Your Lakehouse in Fabric is growing rapidly and queries are slowing down. How would you optimize it? 𝗖𝗮𝗻𝗱𝗮𝘁𝗲 👩🏻💻: Will partition data based on query patterns, utilizes Delta commands (OPTIMIZE, VACUUM, ZORDER) and configure caching in Fabric. Also, consider Materialized Views in the Warehouse for frequently accessed datasets. 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿 🕵🏻♀️: How do you handle real-time streaming ingestion in Fabric from IoT devices? 𝗖𝗮𝗻𝗱𝗮𝘁𝗲 👩🏻💻: Ingest events into Eventstream in Fabric, apply real-time transformations ....place the data into a Lakehouse table and connect it to a Power BI DirectLake dataset for near real-time dashboards. 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿 🕵🏻♀️: You need to migrate on-prem SQL Server data to Fabric Lakehouse. How would you do it? 𝗖𝗮𝗻𝗱𝗮𝘁𝗲 👩🏻💻: Use Data Pipeline copy activities or Data Factory integration in Fabric with parallelism, compress data during transfer.......place it into ADLS Gen2-backed Lakehouse and validate with row counts and checksums. 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿 🕵🏻♀️: Your Fabric Notebook Spark job is failing due to data skew. What’s your approach? 𝗖𝗮𝗻𝗱𝗮𝘁𝗲 👩🏻💻: Identify skewed keys, apply salting/repartitioning and use broadcast joins for small tables. If required, switch to bucketed tables in Delta for better performance. 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿 🕵🏻♀️: How would you secure PII data like SSNs in Fabric pipelines? 𝗖𝗮𝗻𝗱𝗮𝘁𝗲 👩🏻💻: Encrypt or hash sensitive columns at ingestion, use column-level security in Fabric Warehouse, enable data masking for reporting layers and manage secrets through Azure Key Vault integration. ________________________________________________ Join 170+ candidates who’ve already been upskilled with these DE programs by me : https://lnkd.in/dt5qchck • Databricks + ADF : https://lnkd.in/du2irvWy #MicrosoftFabric #AzureDataEngineering #DataEngineering

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞𝐬, 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞𝐬, 𝐚𝐧𝐝 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞 Every organization deals with the same question: where and how should we store our data? The best option varies by company, so let's explore what each one does well. 𝐃𝐚𝐭𝐚 𝐰𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞: Think of it as a highly organized digital filing cabinet in a corporate office. Everything is structured, labelled, and stored in specific folders (tables) with strict rules about what goes where. 𝘛𝘦𝘤𝘩𝘯𝘪𝘤𝘢𝘭 𝘱𝘦𝘳𝘴𝘱𝘦𝘤𝘵𝘪𝘷𝘦: A centralized repository stores structured data from multiple sources using a predefined schema (𝘴𝘤𝘩𝘦𝘮𝘢-𝘰𝘯-𝘸𝘳𝘪𝘵𝘦). Data goes through ETL (Extract, Transform, and Load) processes before storage, ensuring high data quality and fast query performance for business intelligence and reporting. 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞: Imagine it as a massive digital storage warehouse where you can dump any type of file - documents, photos, videos, spreadsheets, emails - without organizing them first. 𝘛𝘦𝘤𝘩𝘯𝘪𝘤𝘢𝘭 𝘱𝘦𝘳𝘴𝘱𝘦𝘤𝘵𝘪𝘷𝘦: A storage repository that can hold vast amounts of raw data in its native format - structured, semi-structured, and unstructured. It uses 𝘴𝘤𝘩𝘦𝘮𝘢-𝘰𝘯-𝘳𝘦𝘢𝘥, meaning you define the structure when you analyze the data, not when you store it. 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞: Think of it as a smart storage system that combines the best of both worlds. 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐩𝐞𝐫𝐬𝐩𝐞𝐜𝐭𝐢𝐯𝐞: An architecture that provides the flexibility and cost-effectiveness of data lakes with the data management and performance capabilities of data warehouses. It supports 𝘈𝘊𝘐𝘋 (𝘈𝘵𝘰𝘮𝘪𝘤𝘪𝘵𝘺, 𝘊𝘰𝘯𝘴𝘪𝘴𝘵𝘦𝘯𝘤𝘺, 𝘐𝘴𝘰𝘭𝘢𝘵𝘪𝘰𝘯, 𝘢𝘯𝘥 𝘋𝘶𝘳𝘢𝘣𝘪𝘭𝘪𝘵𝘺) transactions, schema enforcement, and governance while handling diverse data types and enabling both batch and streaming workloads. #Datawarehouse #Datalake #Lakehouse Durga Srinivas Perisetti Barun Kumar Shankar Rajagopalan Uma A. Sreejesh Nair

𝟮𝟬 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗲𝗿𝗺𝘀 𝗘𝘃𝗲𝗿𝘆 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗦𝗵𝗼𝘂𝗹𝗱 𝗞𝗻𝗼𝘄 1️⃣ Data Pipeline: An automated set of processes that moves data from various sources (think databases, APIs, logs) to destinations such as data warehouses or lakes. 2️⃣ ETL (Extract, Transform, Load) Extract data from diverse sources Transform (clean, enrich, or reshape) for consistency Load into analytical systems for reporting or ML 3️⃣ Data Lake A central repository designed to store raw, unstructured, or semi-structured data at scale. Ideal for big data, advanced analytics, and machine learning use cases. 4️⃣ Data Warehouse Optimized for storing structured, organized data—think rows and columns. 5️⃣ Data Governance A framework of policies and standards to ensure data is accurate, secure, compliant, and used responsibly. 6️⃣ Data Quality A measure of data’s accuracy, completeness, consistency, and reliability. 7️⃣ Data Cleansing The process of detecting and correcting errors and inconsistencies in datasets. 8️⃣ Data Modeling Structuring and organizing data into logical formats—schemas, tables, relationships. 9️⃣ Data Integration Combining data from multiple sources—databases, files, SaaS apps—into a unified view for analysis or operational use. 🔟 Data Orchestration Automating, scheduling, and managing complex workflows across multiple data pipelines, tools, and platforms. 1️⃣1️⃣ Data Transformation Converting data from its raw form into a format suitable for analysis or integration, such as normalizing values, aggregating, or encoding. 1️⃣2️⃣ Real-Time Processing Analyzing and acting on data as it’s generated, enabling immediate insights and responses—vital for use cases like fraud detection and IoT. 1️⃣3️⃣ Batch Processing Processing large volumes of data in predefined chunks or intervals, rather than continuously. Suitable for reporting, analytics, and data refreshes. 1️⃣4️⃣ Cloud Data Platform Leveraging cloud-based solutions for scalable, flexible, and cost-effective data storage, processing, and analytics 1️⃣5️⃣ Data Sharding Breaking a large database into smaller, more manageable pieces (shards), each running on a separate server to improve performance and scalability. 1️⃣6️⃣ Data Partitioning Dividing datasets into segments or partitions (e.g., by date, region) to speed up query performance and enable parallel processing. 1️⃣7️⃣ Data Source The origin point of your raw data—could be APIs, files, databases, sensors, or external platforms. 1️⃣8️⃣ Data Schema A blueprint that defines how data is organized—what fields exist, their types, and relationships—crucial for consistency and validation. 1️⃣9️⃣ DWA (Data Warehouse Automation) Tools and technologies that automate the design, deployment, and management of data warehouses—reducing manual effort and time-to-value. 2️⃣0️⃣ Metadata Data about data—providing essential context like data types, definitions, lineage, and relationships. Follow Riya Khandelwal

Batch vs. Real-Time: Crafting High-Impact Data Pipelines for Every Business Use Case Not all data is equal—and neither are the techniques to extract its value. As leaders in the data engineering space, understanding when to leverage batch versus real-time processing, and how to handle structured, semi-structured, and unstructured data, is essential for building pipelines that truly empower your business. Batch vs. Real-Time: Picking the Right Approach - Batch Processing excels in scenarios with large data volumes, periodic updates, compliance reporting, or complex analytics that don’t require instant results. It offers higher throughput and lower costs, making it ideal for warehouse updates, BI dashboards, or historical analysis. - Real-Time Processing is a game-changer when every second counts. Choose this for use cases like fraud detection, personalized recommendations, IoT analytics, or monitoring—unlocking immediate insights and responsiveness across your operations. - Hybrid Approaches are increasingly common, letting you combine real-time responsiveness with cost-efficient batch processing for deep-dive analysis. Matching Techniques to Data Types - Structured Data lives in classic rows and columns—think CRM systems, financial data, and most day-to-day business transactions. Here, relational databases and cloud data warehouses shine. - Semi-Structured Data (JSON, XML, etc.) offers flexibility for evolving data models; NoSQL and hybrid tools are ideal for collecting and analyzing this fast-changing information. - Unstructured Data (images, videos, emails) requires advanced techniques and AI/ML workflows. Data lakes and object storage systems provide the scalable foundation; data quality and governance are key to success. Benefits, Drawbacks, and ROI - Benefits: Reduced manual work, improved data quality, operational efficiency, better decision-making, and direct revenue lift. - Drawbacks: Technical complexity, skill shortages, and data quality challenges can slow progress if not managed proactively. - ROI: Modern pipelines deliver rapid payback—often within 12-18 months—through improved insights, faster time-to-market, and higher revenue growth. The best data engineering strategies are adaptable, marrying the right tools and processing methods with clear business goals. Organizations that tune their infrastructure to the needs of their data—and their users—set themselves up to lead in the digital age. Curious about what architecture fits your business? Need to assess ROI or measure pipeline success? Let’s connect and strategize for your data-driven future. #BatchProcessing #RealTimeAnalytics #DataQuality #AdvancedAnalytics #DataPipelines #BusinessGrowth #DataLeadership #TechStrategy

Managing Large Healthcare Data: A Strategic Approach to SQL Server Partitioning The landscape of healthcare data management demands our immediate attention and strategic consideration. As someone deeply involved in healthcare systems integration, I've witnessed firsthand how critical efficient data partitioning becomes when handling extensive patient histories. Let's break down the core elements that shape effective partitioning strategies: → Table Partitioning: The Foundation Intelligent partitioning transforms how we access and analyze patient data. By segmenting large tables along logical boundaries—typically date ranges for healthcare records—we enable faster queries and more efficient maintenance windows. Three key principles guide successful implementation: 1. Aligned partition schemes 2. Strategic boundary selection 3. Balanced data distribution The research reveals: Healthcare organizations implementing proper partitioning strategies consistently report 40-60% improvements in query performance for large-scale analytics operations. Common misconceptions: Many teams initially worry about partitioning complexity adding overhead. In reality, well-designed partitioning often reduces system load by enabling parallel processing and targeted maintenance. Real-world applications: In my work integrating healthcare systems, we've used partitioning to handle everything from claims history to clinical documentation. The result: dramatically improved response times and better system reliability during peak operations. Looking ahead, teams need to focus on: - Implementing automated partition management - Establishing clear governance frameworks - Maintaining optimal partition alignment - Regular performance monitoring - Comprehensive testing protocols These priorities reflect our commitment to building not just functional, but fundamentally efficient healthcare data systems. The stakes are particularly high in healthcare environments, where rapid access to patient histories directly impacts care delivery. Together, we can build more resilient and performant systems that serve our healthcare providers while maintaining unwavering efficiency standards. The future of healthcare data management isn't just about storage—it's about enabling growth while maintaining exceptional performance.

Database vs Data Warehouse vs Data Lake - what’s the difference???👇 🔹 Database (OLTP) • Designed for day-to-day transactions (app reads/writes). • Best kept small, clean, and fast. • Not meant for heavy analytics. 🔹 Data Warehouse (OLAP) • Stores curated, structured data for analytics and BI. • Perfect for dashboards, reporting, and consistent KPIs. • Think of schemas and joins. 🔹 Data Lake • Stores any kind of data - structured, semi-structured, unstructured. • Cheap and highly scalable. • Great for data science, ML, and future unknown use cases. ⸻ 👉 In short: • Databases run your business. • Warehouses measure your business. • Lakes future-proof your business. 💬 What’s your team relying on more these days??? #DataEngineering #BigData #Analytics #Cloud #ETL #DataAnalyst #BusinessIntelligence

Data Governance – More Than Just Compliance A while back, I was working on a BI project where three different dashboards showed three very different numbers for “active customers.” The pipelines were running fine. The dashboards were neatly designed. But in every meeting, the first 15 minutes were spent arguing about which number was correct. That’s when it hit me: the problem wasn’t BI—it was governance. Each team had its own definition of “active.” Marketing counted everyone who engaged in the last 6 months. Operations counted those with open orders. Finance counted only paying customers. When governance stepped in, we created a common definition, assigned data ownership, and tracked lineage. The confusion vanished. Reports aligned instantly. Leaders finally debated strategy, not data. I’ve seen the same in data migration projects too. Without governance, inconsistent definitions simply move from old systems into new ones. With governance, migrations deliver trusted data that business leaders can use confidently from Day 1. To me, data governance isn’t bureaucracy—it’s the invisible foundation of trust. It’s what transforms BI from being “just reports” into a decision-making compass. And with AI, cloud, and self-service analytics accelerating, clear governance is no longer optional—it’s essential. Here’s my thought: What if we treated governance not as control, but as scaffolding that enables innovation? Would love to hear—how has governance (or the lack of it) shaped your data journey? #DataGovernance #BusinessIntelligence #DataTrust #DataMigration #Analytics

𝗢𝗿𝗮𝗰𝗹𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗹𝗼𝘂𝗱: 𝗕𝗿𝗶𝗱𝗴𝗶𝗻𝗴 𝗟𝗲𝗴𝗮𝗰𝘆 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗻𝗱 𝗡𝗲𝘅𝘁-𝗚𝗲𝗻 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗢𝗿𝗮𝗰𝗹𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗹𝗼𝘂𝗱 (𝗢𝗔𝗖) is a powerful, AI-driven analytics platform that empowers organizations to transform data into actionable insights. With built-in machine learning, natural language querying, and rich visualizations, OAC supports structured, semi-structured, and unstructured data—making it a strategic solution for enterprises across the Northeast Corridor looking to modernize without disrupting mission-critical operations. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗢𝗔𝗖 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀: - Real-time executive dashboards - Predictive analytics for customer retention - Financial forecasting and performance tracking - Operational efficiency and supply chain optimization 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗻𝗴 𝘁𝗼 𝗦𝗤𝗟 𝗦𝗲𝗿𝘃𝗲𝗿 𝟮𝟬𝟮𝟮: OAC connects to SQL Server 2022 using Oracle’s Data Gateway or Remote Data Connector. These tools enable secure, real-time access to on-prem data—ideal for hybrid environments in Boston, NYC, and Philadelphia, where legacy systems still play a vital role. 𝗦𝗤𝗟 𝗦𝗲𝗿𝘃𝗲𝗿 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀: - Visualizing ERP and CRM data - Enhancing legacy reporting with modern dashboards - Integrating financial and operational data into predictive models 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗻𝗴 𝘁𝗼 𝗠𝗼𝗻𝗴𝗼𝗗𝗕: Given a MongoDB connection string, OAC can connect via REST APIs or custom connectors through Oracle’s Data Gateway. This unlocks access to flexible NoSQL data sources, enabling deeper insights from semi-structured datasets. 𝗠𝗼𝗻𝗴𝗼𝗗𝗕 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀: - Analyzing customer behavior from app logs - Visualizing IoT sensor data - Enhancing product analytics with schema-less flexibility 𝗢𝗔𝗖 + 𝗠𝗼𝗻𝗴𝗼𝗗𝗕 𝗳𝗼𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: Together, OAC and MongoDB allow companies to analyze dynamic product usage patterns, personalize user experiences, and iterate faster—especially valuable for tech-driven firms in the Northeast Corridor. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗻𝗴 𝘁𝗼 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝘃𝗶𝗮 𝗝𝗗𝗕𝗖: OAC integrates with Databricks clusters using JDBC, enabling seamless access to big data pipelines and ML models. This is ideal for advanced analytics, real-time decision support, and scalable data lake exploration. 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀: - Real-time fraud detection - Customer segmentation using ML - Scalable analytics across massive datasets At 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 𝗘𝗾𝘂𝗮𝘁𝗶𝗼𝗻𝘀, we help companies modernize their data ecosystems with 𝗢𝗿𝗮𝗰𝗹𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗹𝗼𝘂𝗱. Ready to unlock the full value of your legacy systems? Let’s connect. #OracleAnalyticsCloud #LegacyModernization #NoSQLAnalytics #NortheastTech