Basem Moghram

Choosing the Right Data Warehouse Architecture: Inmon vs. Kimball Selecting the best data warehouse architecture is crucial for your organization’s data management. Let’s dive into two popular approaches—Inmon’s and Kimball’s—and see which one might be the best fit for you. 🏛️ Inmon’s Approach (Top-Down) 🔹 Overview: Inmon’s approach emphasizes building a centralized, normalized data warehouse that excels in handling complex queries and ensuring long-term data integrity. 🔹 Key Steps: Extract data from various sources. Load it into a staging area. Transform and load into a normalized Data Warehouse (3NF). Create Data Marts tailored for specific business needs. 🔹 Structure: Centralized Data Warehouse 🔹 Advantages: Ideal for complex queries and maintaining data integrity over time. 🔹 Considerations: The initial setup can be complex and time-consuming, delaying immediate benefits. 🚀 Kimball’s Approach (Bottom-Up) 🔹 Overview: Kimball’s approach focuses on speed and ease of use by creating decentralized data marts that come together to form a data warehouse. 🔹 Key Steps: Extract data from operational sources. Load it into a staging area. Transform and load directly into Data Marts. Integrate these Data Marts to build a Data Warehouse (Star/Snowflake Schema). 🔹 Structure: Decentralized Data Marts 🔹 Advantages: Faster implementation, offering quick insights, and user-friendly for business teams. 🔹 Considerations: Potential for data redundancy and integration challenges in the long run. CC: Deepak #DataWarehouse #InmonVsKimball #DataArchitecture #BusinessIntelligence #DataStrategy #DataManagement #BigData #DataEngineering #AI #Analytics #TechTrend

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#Data_governance Data lineage and traceability Problem A manufacturing company struggles to trace the root cause of a product defect due to unclear data lineage. Solution Data governance provides clear documentation of data origins, transformations, and flows, enabling the company to trace the defect’s source and implement corrective actions.

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#Knowledgesharing3 BigQuery Best Practices -Estimate the queries before running them. -Use clustering and partitioning for your tables. -Avoid streaming data inserts when possible (loading data in bulk is free but streaming is not free) - Expire data Automatically. - Consider the Nearline/ Coldline storage option for your data access need. -Optimize bigquery usage using audit logs using BiqQueryAuditMetadata #gcp #googlecloudservices #google #bigquery #storage #jobs #googlecloud

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Myths of Data Governance & Security : Data governance and security are important priorities. Even with high-performance data pipelines, rapid data transformation, and superior data processing tools, their value decreases if data is not properly stored and controlled, data stored and accessed according to local regulations meet is important, and is organized covered to ensure identifiable and a valuable to the organization.

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As a data engineer you must be able to differentiate from Data Lake and Datawarehouse. Data lakes store all types of raw data, which data scientists/analyst may then use for a variety of projects. Data warehouses store cleaned and processed data, which can then be used to source analytic or operational reporting, as well as specific Business Intelligence use cases. A data warehouse and a data lake are two related but fundamentally different technologies. While data warehouses store structured data, a lake is a centralized repository that allows you to store any data at any scale Data format requirements - If your data exists primarily in Delta format with relational structure, Warehouse seamlessly handles your needs. However, if you work with diverse formats like CSV, Parquet, or JSON, or you are using non-structured data, Lakehouse proves to be the more versatile solution thank you #solaDataEngineeringJourneyWith10alytics #DataEngineeringWith10alytics Gbenga Banso 10Alytics

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## My Azure Data Factory Journey: A few weeks ago, I decided to learn Azure Data Factory (ADF), and it's been a transformative experience! Although I haven't integrated it into my workflow yet, the learning process has been eye-opening. Having used SQL Server Management Studio (SSMS) and SQL Server Integration Services (SSIS) , I wanted to share some key insights and advantages I've found with ADF. Real-Life Benefits Over SSMS and SSIS 1. Cloud Power   - Scalability: ADF scales effortlessly with your needs, no more worrying about server limits   -Managed Service: Less maintenance, more focus on actual data tasks 2. Cost Efficiency   -Pay-As-You-Go: Only pay for what you use, which is much more economical than maintaining on-prem infrastructure 3. Integration Made Easy   - Wide Range of Connectors: Easily connect to both cloud and on-premises data sources   - Hybrid Integration: Seamlessly combine data from different environments 4. Advanced Features   - Data Flows: Visual, code-free data transformations   - Pipeline Orchestration: Intuitive drag-and-drop interface with excellent monitoring tools 5.Security and Compliance   - Built-In Security: Robust security features like data encryption and Azure Active Directory integration ### Final Thoughts Learning ADF has been a game-changer. The process has not only expanded my skill set but also opened up new possibilities for future projects. If you're thinking about learning ADF or just getting started, dive in. It is worth it. #AzureDataFactory #DataIntegration #CloudComputing #TechJourney #DataTransformation CapItAll

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As data engineers, one of our key responsibilities is to manage data efficiently and cost-effectively. Here are some valuable tips to minimize storage costs while implementing batch datasets in Parquet format using Azure technologies: 𝗞𝗲𝘆 𝗧𝗶𝗽𝘀: 𝗖𝗵𝗼𝗼𝘀𝗲 𝗣𝗮𝗿𝗾𝘂𝗲𝘁 𝗙𝗼𝗿𝗺𝗮𝘁: Parquet is a columnar storage file format that is highly efficient for both data storage and retrieval, particularly for analytical queries. It reduces storage costs by offering better compression and faster query performance. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗗𝗮𝘁𝗮 𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴: Effective partitioning of your data can significantly reduce the amount of data scanned during queries, leading to cost savings. Partition your data based on high cardinality columns, such as date or category. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻: Parquet supports multiple compression algorithms. Use efficient compression methods like Snappy, which offers a good balance between compression ratio and speed, thereby reducing storage costs and improving performance. 𝗨𝘀𝗲 𝗗𝗲𝗹𝘁𝗮 𝗟𝗮𝗸𝗲: Delta Lake on Azure Data Lake Storage Gen2 can further optimize storage and transaction management. It provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing, reducing the need for data duplication and thus storage costs. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗙𝗶𝗹𝗲 𝗦𝗶𝘇𝗲𝘀: Large numbers of small files can lead to higher storage and processing costs. Aim to optimize the file size by adjusting the number of records per file to balance between processing speed and storage efficiency. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗮𝗻𝗱 𝗠𝗮𝗻𝗮𝗴𝗲 𝗦𝘁𝗼𝗿𝗮𝗴𝗲: Regularly monitor your storage usage with Azure Cost Management and make adjustments as needed. Consider lifecycle policies to automatically move older data to cooler storage tiers to save costs. By following these tips, you can not only minimize storage costs but also enhance the efficiency and performance of your data pipelines. Feel free to share your own tips and experiences in the comments below! Let's empower our data engineering community together. #DataEngineering #Azure #BigData #Parquet #CloudComputing #DataLake #AzureSynapse #CostOptimization

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Dear BI Community, Exciting to share my experience with upgrading from OBIEE to Oracle Analytics Server -OAS. Like others Oracle installations, it wasn't without its challenges because the conflicting of installation media and documentation available on Oracle's official site can be daunting. But with patience and a flexible timeline finally my team successfully made the transition to OAS. Overall Experience has been best so far, In fact, we found OAS to be even more powerful than other visualization tools like Power BI and Tableau, because OAS encompasses all the features of these tools and more, it covers all the reporting and dashboards needs by using single platform, ie 1st “Analytics” component can be used for all types of dashboard development, 2nd“Xmlpserver” is for all type transaction reports , bursting (automatic data delivery ) and scheduling of any data requirements and the third component “DV (data visualization)” support all types of data transformation, data security, combine data from multiple sources and make available on dashboards by using modern visuals . OAS Best features. Empowerment of end-users with advanced visualization tools ·      Separation of Admin-level and application-level security ·      Availability and manageability of catalogue, data and ML separately on DV ·      Independence of RPD through DV ·   Flexibility in using different data sources for authentication and authorization   Installation Summary: Installation was a comprehensive process involving key components such as JDK-8, WebLogic 12c, infrastructure 12.1* four patches, OAS media, and Oracle 19c DB. It took approximately 10 to 12 working days to ensure stability and functionality. Key components like Console, EM, Analytics, Xmlpserver (Bi Publisher), and DV (Data Visualization) were seamlessly integrated, setting the stage for enhanced BI capabilities. Cheers to the journey ahead with OAS, where data meets innovation! 🙂 #bi #OAS #OAC #powerBI #Tableau #dataanalytics #oracle #bigdata

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𝑨𝒑𝒂𝒄𝒉𝒆 𝑺𝒑𝒂𝒓𝒌 𝑺𝒄𝒆𝒏𝒂𝒓𝒊𝒐 𝑩𝒂𝒔𝒆𝒅 𝑰𝒏𝒕𝒆𝒓𝒗𝒊𝒆𝒘 𝑸𝒖𝒆𝒔𝒕𝒊𝒐𝒏𝒔 𝒂𝒏𝒅 𝑨𝒏𝒔𝒘𝒆𝒓 𝑺𝒆𝒓𝒊𝒆𝒔: 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝟑 (𝐏𝐚𝐫𝐭 - 𝟐) : 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐓𝐞𝐫𝐦𝐢𝐧𝐨𝐥𝐠𝐢𝐞𝐬 𝐢𝐧 𝐒𝐩𝐚𝐫𝐤. 𝐍𝐞𝐱𝐭 𝟔 𝐃𝐢𝐬𝐜𝐮𝐬𝐬𝐞𝐝. Excited to share my latest YouTube video where I break down some of the most important Apache Spark terminologies every Data Engineer should know: ✨ JVM (Java Virtual Machine): Understanding Spark's execution backbone. ✨ Lazy Evaluation: How Spark delays execution for optimization. ✨ Cache & Persist: Techniques to supercharge performance. ✨ Checkpointing: Building fault tolerance in distributed applications. ✨ Broadcast Variables: Sharing large datasets efficiently across nodes. ✨ Accumulators: Handling parallel aggregation with ease. To make it even better, I’ve included hands-on code examples to bring these concepts to life. Whether you're prepping for interviews or leveling up your Spark expertise, this video is tailored for YOU! 𝐏𝐚𝐫𝐭 𝟏 (𝐃𝐢𝐬𝐜𝐮𝐬𝐬𝐞𝐝 𝐅𝐢𝐫𝐬𝐭 𝟏𝟓 𝐓𝐞𝐫𝐦𝐢𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬) : https://lnkd.in/dB4FYBZW 📺 Check it out here: https://lnkd.in/dZ9R7Wen #ApacheSpark #DataEngineering #BigData #YouTube #TechTutorials

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𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐬𝐨𝐦𝐞 𝐤𝐞𝐲 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐮𝐩𝐝𝐚𝐭𝐞𝐬 𝐟𝐫𝐨𝐦 𝟐𝟎𝟐𝟏 𝐭𝐨 𝟐𝟎𝟐𝟒! 𝙋𝙤𝙬𝙚𝙧 𝘽𝙄 𝙐𝙥𝙙𝙖𝙩𝙚𝙨 𝙞𝙣 2021: 1️⃣ Premium Per User (PPU) Licensing: Power BI introduced Premium Per User (PPU) licensing, allowing individuals to access premium features at a lower cost. 2️⃣ DirectQuery for Power BI Datasets & Azure Analysis Services: This enabled live connections to Power BI datasets and Azure Analysis Services, enhancing data model flexibility. 3️⃣ Paginated Reports in Power BI Pro: Paginated reports became available to Power BI Pro users, previously exclusive to Premium users. Small Multiples: A new visual feature that enables the display of multiple instances of a visual for various values in a category. 4️⃣ New Model View: Power BI updated the model view interface with diagram layouts, customized properties, and enhanced relationship handling. 𝙋𝙤𝙬𝙚𝙧 𝘽𝙄 𝙐𝙥𝙙𝙖𝙩𝙚𝙨 𝙞𝙣 2022: 1️⃣ Field Parameters: Field parameters allowed users to dynamically select fields or metrics in visuals, simplifying the creation of interactive reports. 3️⃣ Metrics (previously Goals): Power BI Goals evolved into Metrics, offering improved goal-setting and tracking features integrated with Power BI dashboards. 4️⃣ Dataflows Enhancements: Improved performance for dataflows, including DirectQuery support for Datamart and easier management of large datasets. 5️⃣ Error Bars for Line Charts: Error bars were added for line charts, making it easier to display statistical data uncertainties. 6️⃣ Sensitivity Labels for Data Loss Prevention (DLP): Improved security features with the ability to apply sensitivity labels to reports and datasets for better governance. 𝙋𝙤𝙬𝙚𝙧 𝘽𝙄 𝙐𝙥𝙙𝙖𝙩𝙚𝙨 𝙞𝙣 2023: 1️⃣ On-Object Interaction: An intuitive interface to make adjustments directly on visuals, reducing clicks needed to configure reports. 2️⃣ Data Model Improvements: Composite models on Power BI Datasets and Analysis Services allowed more flexible data modeling. 3️⃣ Dynamic M Query Parameters: Allowed the use of parameters to control queries directly in Power BI models, enhancing real-time filtering capabilities. 4️⃣ Data Connectivity Updates: Integration with more data sources, including Google Analytics 4 and Adobe Analytics. 𝙋𝙤𝙬𝙚𝙧 𝘽𝙄 𝙐𝙥𝙙𝙖𝙩𝙚𝙨 𝙞𝙣 2024: 1️⃣ AI-Powered Visuals: Enhanced AI capabilities such as smart narratives and improved AI-driven visuals like Key Influencers and Decomposition Trees. 2️⃣ Expanded Support for Large Datasets: Further improvements for handling large datasets across Premium and Embedded capacities, including autoscale for enhanced elasticity. 3️⃣ Fabric Integration: Integration with Microsoft Fabric for streamlined data integration, governance, and AI analytics across the Power Platform. 4️⃣ These updates reflect Power BI’s continuous focus on usability, AI integration, data security, and performance enhancements.

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The #insights are only as good as the #data they're build on #data_cleaning #Data_analysis #data_mining #powerbi #powerquery #sql #business_analysis

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BI / Data strategy ----------------------------- Offering : Domains data products on SQL Lakehouses and DWH ( thanks to DBT) and analytics cubes stacks ( power bi , tableau , looker ..) on strong data quality under strong governance policy ( Big skills , persona , domains ( SQL + analytics together ) owners , sharing , security , rules , tests , cicd ..) with data objects well documented via a data catalog service...is the key of success The way for a silver or gold medal 😜 Most of the time my first as fireman actions target to STOP hundreds of power bi semantic models duplicates ( due to 5 last years a not governed self bi policy)

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𝘐 𝘩𝘢𝘷𝘦𝘯'𝘵 𝘤𝘰𝘮𝘦 𝘢𝘤𝘳𝘰𝘴𝘴 𝘵𝘩𝘦 𝘢𝘮𝘢𝘻𝘪𝘯𝘨 𝘣𝘦𝘯𝘦𝘧𝘪𝘵𝘴 𝘵𝘩𝘢𝘵 #aws R𝘦𝘥𝘴𝘩𝘪𝘧𝘵 𝘱𝘳𝘰𝘷𝘪𝘥𝘦𝘴 𝘪𝘯 𝘰𝘵𝘩𝘦𝘳 𝘴𝘺𝘴𝘵𝘦𝘮𝘴! Having worked on on-prem datawarehouses that were on traditional RDBMS MSSQL Server and also on Massively Parallel Processing ( MPP ) systems such as Teradata, 𝘐 𝘩𝘢𝘷𝘦𝘯'𝘵 𝘤𝘰𝘮𝘦 𝘢𝘤𝘳𝘰𝘴𝘴 𝘵𝘩𝘦 𝘢𝘮𝘢𝘻𝘪𝘯𝘨 𝘣𝘦𝘯𝘦𝘧𝘪𝘵𝘴 𝘵𝘩𝘢𝘵 #aws R𝘦𝘥𝘴𝘩𝘪𝘧𝘵 𝘱𝘳𝘰𝘷𝘪𝘥𝘦𝘴 𝘪𝘯 𝘰𝘵𝘩𝘦𝘳 𝘴𝘺𝘴𝘵𝘦𝘮𝘴! 𝐀𝐦𝐚𝐳𝐨𝐧 𝐑𝐞𝐝𝐬𝐡𝐢𝐟𝐭 𝐢𝐬 𝐚 "𝐬𝐞𝐥𝐟-𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐬𝐞𝐥𝐟-𝐭𝐮𝐧𝐢𝐧𝐠" 𝐌𝐏𝐏 𝐬𝐲𝐬𝐭𝐞𝐦 𝐭𝐡𝐚𝐭 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐬 𝐮𝐩 𝐭𝐨 6𝐱 𝐛𝐞𝐭𝐭𝐞𝐫 𝐩𝐫𝐢𝐜𝐞 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐭𝐡𝐚𝐧 𝐨𝐭𝐡𝐞𝐫 𝐜𝐥𝐨𝐮𝐝 𝐝𝐚𝐭𝐚 𝐰𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞𝐬. It understands the data load and query patterns and then automatically distributes the data in its cluster. No wonder why it's so expensive :) The lowest configuration one dc2.large will cost you $182 if you keep it running for a month. The highest configuration one will be about $20k a month. 𝐁𝐭𝐰, 𝐝𝐢𝐝 𝐲𝐨𝐮 𝐤𝐧𝐨𝐰 𝐭𝐡𝐚𝐭 𝐑𝐞𝐝𝐬𝐡𝐢𝐟𝐭 𝐝𝐨𝐞𝐬 𝐧𝐨𝐭 𝐝𝐢𝐫𝐞𝐜𝐭𝐥𝐲 𝐞𝐧𝐟𝐨𝐫𝐜𝐞 𝐩𝐫𝐢𝐦𝐚𝐫𝐲 𝐤𝐞𝐲 𝐚𝐧𝐝 𝐟𝐨𝐫𝐞𝐢𝐠𝐧 𝐤𝐞𝐲 𝐜𝐨𝐧𝐬𝐭𝐫𝐚𝐢𝐧𝐭𝐬? This means that you can insert data that violates these constraints without getting an error message during the insertion process. Also, it doesn't have indexes! 𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐭𝐡𝐞 𝐭𝐮𝐧𝐢𝐧𝐠 𝐨𝐫 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐞𝐯𝐞𝐧 𝐰𝐨𝐫𝐤 𝐭𝐡𝐞𝐧? Would leave it up to you to research :) Btw, if you don't really want the hassle of maintaining a cluster, go with Redshift Serverless!!! With this you get zero administration! 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐈 𝐤𝐧𝐨𝐰 𝐝𝐨𝐧'𝐭 𝐞𝐯𝐞𝐧 𝐡𝐢𝐫𝐞 𝐑𝐞𝐝𝐬𝐡𝐢𝐟𝐭 𝐃𝐁𝐀𝐬 𝐰𝐡𝐞𝐧 𝐭𝐡𝐞𝐲 𝐠𝐨 𝐟𝐨𝐫 𝐒𝐞𝐫𝐯𝐞𝐫𝐥𝐞𝐬𝐬. What more can you ask for? P.S ✅Version 3 of 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐛𝐚𝐬𝐞𝐝 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧 𝐑𝐞𝐚𝐥-world AWS Data Engineering ( RADE™) program 𝐰𝐢𝐭𝐡 80+ 𝐚𝐦𝐚𝐳𝐢𝐧𝐠 𝐩𝐞𝐨𝐩𝐥𝐞 completed on Oct 5th. Onboarding for the Next one to begin in November. ✅𝐈𝐟 𝐲𝐨𝐮 𝐚𝐫𝐞 𝐚𝐜𝐭𝐢𝐨𝐧-𝐨𝐫𝐢𝐞𝐧𝐭𝐞𝐝 and want to get in the next batch, fill the form below to be get invited for the webinar for RADE™ program V4 Click https://lnkd.in/ervbN-zt 𝐔𝐒𝐏: 𝐑𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐛𝐚𝐬𝐞𝐝 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧 program ! doc credit - Myself #aws #dataengineering

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ETL vs. ELT: Key Differences ETL (Extract, Transform, Load): - Process: Extract data, transform it, then load it into the data warehouse. - Best For: On-premises systems and complex transformations. - Advantage: Ensures clean, formatted data before storage. ELT (Extract, Load, Transform): - Process: Extract data, load it into the target system, then transform it using the system's resources. - Best For: Cloud-based platforms and large data volumes. - Advantage: Utilizes scalable cloud resources for efficient processing. Key Difference: ETL transforms data before loading, while ELT does so after loading. Choose ETL for traditional data warehousing and ELT for modern, scalable data solutions. #DataIntegration #ETL #ELT #DataAnalytics #CloudComputing #DataAnalysis #Data

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🔄 **Master Recursive CTEs for Advanced Data Queries!** 🔄 In SQL Server, Recursive Common Table Expressions (CTEs) are a powerful tool for handling hierarchical data and performing complex recursive queries. Let's dive into what they are, their benefits, potential drawbacks, and practical use cases. ### What is a Recursive CTE? A Recursive CTE is a CTE that references itself, allowing you to perform recursive operations. It's particularly useful for querying hierarchical data, such as organizational structures, folder hierarchies, or graph data. ### Why Use Recursive CTEs? - **Simplicity**: Simplifies complex hierarchical queries that would otherwise require intricate self-joins or iterative procedures. - **Readability**: Enhances the readability and maintainability of recursive queries. - **Flexibility**: Handles variable-depth hierarchies efficiently. ### Example Scenario Suppose you have an organizational structure and you want to retrieve the entire hierarchy of employees under a specific manager. ### Sample Code ```sql WITH EmployeeCTE (EmployeeID, EmployeeName, ManagerID, Level) AS ( -- Anchor member: select the top-level manager SELECT EmployeeID, EmployeeName, ManagerID, 0 AS Level FROM Employees WHERE ManagerID IS NULL UNION ALL -- Recursive member: select employees reporting to managers in the previous level SELECT e.EmployeeID, e.EmployeeName, e.ManagerID, c.Level + 1 FROM Employees e INNER JOIN EmployeeCTE c ON e.ManagerID = c.EmployeeID ) SELECT EmployeeID, EmployeeName, ManagerID, Level FROM EmployeeCTE ORDER BY Level, EmployeeName; ``` ### Use Cases 1. **Organizational Hierarchies**: Query hierarchical employee structures, reporting lines, and departmental relationships. 2. **Bill of Materials (BOM)**: Manage and query multi-level BOMs in manufacturing. 3. **Folder Structures**: Query nested folder hierarchies in file systems or content management systems. ### Advantages - **Conciseness**: Reduces complex iterative logic to a few lines of code. - **Efficiency**: Handles deep hierarchies efficiently without the need for multiple joins. - **Maintainability**: Makes recursive logic easier to understand and maintain. ### Disadvantages - **Performance**: Can be less performant for very deep or broad hierarchies. Optimization and indexing are crucial. - **Complexity Limits**: SQL Server has a default recursion limit (100 levels). Deep recursions might require adjustments or alternative approaches. - **Debugging**: Recursive CTEs can be challenging to debug, especially with complex logic or large datasets. ### Best Practices - **Indexing**: Ensure proper indexing on columns used in recursive joins to optimize performance. - **Limits**: Use the `OPTION (MAXRECURSION n)` hint to control recursion depth and prevent runaway queries. #SQLServer #Database #DataAnalytics #RecursiveCTE #DataScience #DataManagement

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Who is a Data Engineer? A data engineer is a specialist responsible for designing, building, and maintaining the infrastructure that allows organizations to collect, store, and analyze large volumes of data. The data engineers lay the groundwork by creating pipelines and systems that ensure data flows seamlessly from one part of an organization to another. Core Responsibilities: Designing Data Pipelines: Data engineers architect pipelines that automate the collection, transformation, and loading (ETL) of data from sources into data warehouses. Ensuring Data Quality: Data engineers implement processes to validate and clean data, ensuring that downstream users can trust the data they work with. Supporting Data Analytics: They collaborate with data scientists by providing the structured data they need for advanced analytics, machine learning models, and business intelligence tools. Key Skills and Technologies To excel in this role, a data engineer must possess a diverse skill set that spans programming, database management, cloud computing, and big data technologies. The essential skills are: Programming Languages: Python, Scala, Java, R: For data manipulation, automation, and building ETL pipelines. SQL: The standard language for querying and managing relational databases. ETL Processes: Mastery of ETL processes is crucial for transforming raw data into usable formats. Tools like Apache Nifi, Talend, and custom Python scripts. Cloud Platforms: Familiarity with cloud platforms such as AWS, Google GCP, and Microsoft Azure is essential for data engineering. These platforms offer scalable storage solutions and data processing tools. Big Data Technologies: Apache Hadoop and Spark: Key technologies for distributed data processing. They allow data engineers to process large datasets efficiently across multiple machines. Kafka and Flink: Tools for handling real-time data streams. Data Modeling and Database Design: Understanding how to model data for efficient storage and retrieval is fundamental. This includes relational and NoSQL databases, such as MySQL, PostgreSQL, MongoDB, and Cassandra. Day-to-Day Responsibilities A typical day for a data engineer is dynamic and varies depending on the industry and the client's needs. Everyday tasks include: Managing Data Workflows: Overseeing data flow through various pipelines, ensuring data is processed and delivered on time. Optimizing Data Storage Solutions: Continuously improving data storage to enhance performance and reduce costs, whether on-premises or in the cloud. Collaborating with Data Scientists and Analysts: Working closely with teams to understand their data needs and providing them with clean, structured data. Troubleshooting Data Pipelines: Identifying and resolving issues in data pipelines. Data engineers are the architects and builders of the systems that allow organizations to harness the power of data. Pix Credit: https://lnkd.in/g-ZqVskk #dataengineer #tech

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💻 𝐎𝐋𝐓𝐏 𝐯𝐬. 𝐎𝐋𝐀𝐏: 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 📊 In the world of data management, understanding the roles of OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) is essential for designing efficient systems tailored to specific needs. 🔹 𝐎𝐋𝐓𝐏 (𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐃𝐚𝐭𝐚) is designed to handle day-to-day transactional tasks:   - Transaction-oriented and detail-focused.   - Operates on entity-relationship (ER) models.   - Manages smaller datasets, ranging from 100 MB to a few GB.   - Fully normalized data structure. 🔹 𝐎𝐋𝐀𝐏 (𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐃𝐚𝐭𝐚) focuses on data analysis for decision-making:   - Object and market-oriented, suitable for analysis.   - Manages vast historical datasets using star or snowflake models.   - Supports large data volumes (100 GB to TB).   - Partially normalized structure, operating on millions of files. Choosing the right approach depends on your needs: - 𝐎𝐋𝐓𝐏 for real-time transactions, speed, and detail. - 𝐎𝐋𝐀𝐏 for deep analytics, trends, and historical insights. 🌐 we leverage both to streamline operations and drive insights! Which one does your organization rely on the most? #DataManagement #Analytics #OLTP #OLAP #BigData #DigitalTransformation

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BI tools + Data = Data Analyst 👾 Azure / AWS services + Data = Data Engineer 👾 Python / R+ Data. = Data Scientist 👾 Tools might be different, Roles might be different but, here, most Important is data. when you are dealing with a good amount of data with good quality , your achievement will be most efficient.

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Apache Spark Vs Hadoop MapReduce! Interviewer: Let's delve into the comparison between Apache Spark and Hadoop MapReduce. Can you elaborate on their core differences? Candidate: Certainly. Apache Spark is characterized by its in-memory processing, enabling faster computation by keeping data in memory across multiple processing steps. This contrasts with Hadoop MapReduce, which follows a disk-based processing model, reading data from and writing data to disk after each Map and Reduce phase. This fundamental difference in processing models greatly influences their performance and suitability for various types of workloads. Interviewer: That's insightful. How about their ecosystem support? Candidate: While Hadoop MapReduce benefits from its longstanding presence in the ecosystem, Spark has rapidly gained popularity and built a comprehensive ecosystem of its own. Spark seamlessly integrates with various data sources and storage systems, and its high-level APIs for SQL, streaming, machine learning, and graph processing simplify application development. Additionally, Spark can run both standalone and on existing Hadoop clusters, offering flexibility and compatibility with existing infrastructure. Interviewer: Good points. Now, let's talk about fault tolerance. How do Spark and MapReduce handle failures in distributed environments? Candidate: Both frameworks employ fault tolerance mechanisms, but they differ in their approaches. In MapReduce, fault tolerance is achieved through data replication and re-execution of failed tasks. Intermediate data is persisted to disk after each phase, allowing tasks to be rerun on other nodes in case of failure. On the other hand, Spark leverages lineage and resilient distributed datasets (RDDs) to achieve fault tolerance. RDDs track the lineage of each partition, enabling lost partitions to be recomputed from the original data source. Because Spark primarily operates in-memory, it can recover from failures more quickly compared to MapReduce. Interviewer: That's a comprehensive explanation. Lastly, in what scenarios would you recommend using Apache Spark over Hadoop MapReduce, and vice versa? Candidate: I would recommend using Apache Spark for applications that require real-time processing, iterative algorithms, or interactive analytics. Its in-memory processing capabilities and high-level APIs make it well-suited for these use cases. Conversely, Hadoop MapReduce may be more suitable for batch processing tasks that involve large-scale data processing and do not require real-time or iterative computation. It's essential to consider factors such as performance requirements, processing models, and ecosystem compatibility when choosing between the two frameworks. #spark #hadoop #bigdata

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