OpenText Analytics & AI’s Post

I have always been envious of the data base administrators and data professionals who have the in-depth skills and capabilities to write SQL queries to perform complex data analytics. I'm excited to see OpenText is changing that paradigm with their Data Discovery (Vertica) solution. Read this blog to learn how Data Discovery is making it easier for all users of all skill levels to manipulate and analyze data.

How can AnalyticsCreator help me to store and manage metadata for my data warehouse? Struggling with how to store and manage metadata for your data warehouse? AnalyticsCreator's Open Repository allows data engineers to store and manage metadata in a way that best suits their needs, improving efficiency and effectiveness. #datawarehouse #metadata #metadataframework Ensure your data warehouse is well-organized and easy to understand. Leveraging AnalyticsCreator for Building a Powerful Data Warehouse Metadata Framework https://hubs.ly/Q02qKKwm0

Want to learn more about OpenText Data Discovery for Analytics Database: Come visit us at Booth#108 for a live demo and deep dive at OpenText World 2024. #Analytics #OTW2024 #AdvancedAnalytics

Unlock the Power of a Metadata-Driven Framework Discover how a well-designed data warehouse metadata framework can dynamically manage your data warehouse or data lakehouse, ensuring efficient data organization and management. #DataManagement #Metadata #DataWarehouse AnalyticsCreator: Enhancing Data Warehouse Metadata Framework https://hubs.ly/Q02MJ_Sf0

Essential Components of a Metadata Framework Explore the importance of a data dictionary and data catalog in maintaining consistency and providing a single source of truth. #DataDictionary #DataCatalog #DataGovernance AnalyticsCreator: Enhancing Data Warehouse Metadata Framework https://hubs.ly/Q02MK0WF0

Enterprise Data Strategy – Data Warehouse vs. Data Mart vs. Data Lake vs. Data LakeHouse vs. Data Mesh vs. Data Fabric: What to use and how to explain these concepts. (If you are not a "medium" member, click this link to read the whole story) https://lnkd.in/g3rFbmH2 #dataanalytics #datawarehouse #cloudarchitecture #enterprisearchitecture #solutionsarchitect #datagovernance #dataquality #awscloud https://lnkd.in/gA2Y4-2d

AnalyticsCreator for seamless data source integration and robust metadata repositories. Drive informed decisions and business growth! #DataIntegration #BusinessIntelligence AnalyticsCreator: Enhancing Data Warehouse Metadata Framework https://hubs.ly/Q02Bx_kG0

Discover how AnalyticsCreator revolutionizes data management with a dynamic metadata framework. Say goodbye to manual edits and hello to efficiency! #DataWarehouse #MetadataManagement AnalyticsCreator: Enhancing Data Warehouse Metadata Framework https://hubs.ly/Q02By6nz0

𝐂𝐨𝐦𝐦𝐨𝐧 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐏𝐨𝐬𝐞𝐝 𝐭𝐨 𝐁𝐈 𝐀𝐧𝐚𝐥𝐲𝐬𝐭𝐬 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭𝐬: 1. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞? Provide a basic definition of a data warehouse and its purpose. 2.𝐃𝐞𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐡𝐞 𝐄𝐓𝐋 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐢𝐧𝐠. Articulate the Extract, Transform, Load (ETL) process and its significance in the realm of data warehousing. 3. 𝐃𝐢𝐬𝐭𝐢𝐧𝐠𝐮𝐢𝐬𝐡 𝐁𝐞𝐭𝐰𝐞𝐞𝐧 𝐚 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞, 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞, 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞? Illuminate the key distinctions between data warehouses, databases, and data lakes. 4. 𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐃𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧 𝐚𝐧𝐝 𝐅𝐚𝐜𝐭 𝐓𝐚𝐛𝐥𝐞𝐬 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐢𝐧𝐠 𝐚𝐧𝐝 𝐭𝐡𝐞𝐫𝐞 𝐭𝐲𝐩𝐞𝐬? Provide definitions for dimension and fact tables and expound and each types mostly frequently questions asked. 5. 𝐂𝐥𝐚𝐫𝐢𝐟𝐲 𝐭𝐡𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬 𝐢𝐧 𝐒𝐥𝐨𝐰𝐥𝐲 𝐂𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐃𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐬 (𝐒𝐂𝐃) 𝐚𝐧𝐝 𝐢𝐭𝐬 𝐓𝐲𝐩𝐞𝐬. Compare Star Schemas and Snowflake Schemas.Outline the fundamental structure and purpose of a star schema and snowflake 6. 𝐃𝐞𝐟𝐢𝐧𝐞 𝐚 𝐃𝐚𝐭𝐚 𝐌𝐚𝐫𝐭 𝐚𝐧𝐝 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐢𝐚𝐭𝐞 𝐢𝐭 𝐟𝐫𝐨𝐦 𝐚 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞. Offer a straightforward explanation of what constitutes a data mart and how it varies from a data warehouse. 7. 𝐄𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐞 𝐨𝐧 𝐭𝐡𝐞 𝐒𝐢𝐠𝐧𝐢𝐟𝐢𝐜𝐚𝐧𝐜𝐞 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐢𝐧𝐠. Discuss the importance of maintaining high data quality standards within a data warehouse. 8. 𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐌𝐞𝐭𝐚𝐝𝐚𝐭𝐚 𝐢𝐧 𝐭𝐡𝐞 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐢𝐧𝐠. Provide insights into the purpose and function of metadata within a data warehouse. 9. 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐢𝐚𝐭𝐞 𝐁𝐞𝐭𝐰𝐞𝐞𝐧 𝐎𝐋𝐀𝐏 𝐚𝐧𝐝 𝐎𝐋𝐓𝐏. Define OLAP and OLTP briefly explain its role in data analysis.Questions may cover its characteristics, advantages, and applications in decision support. 10. 𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐇𝐨𝐰 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐢𝐧𝐠 𝐒𝐮𝐩𝐩𝐨𝐫𝐭𝐬 𝐑𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬. Explore the ways in which data warehousing facilitates reporting and analysis processes for organizations. #DataWarehouseInsights #DWAnalytics #Warehousing

Hands-on code for Data Quality checks in Snowflake. While this has most important statistical data quality checks, certainly not a comprehensive data quality initiative. However, this can get one started with most fundamental checks that should be built into any data quality program. #Data #dataDiaries #Snowflake #DataQuality #DataManagement https://lnkd.in/gR7uAuWx