Brandon Jackson, MBA’s Post

Excited to delve into the world of data modeling with you, Ravena O! Your breakdown of different modeling types is insightful and helpful for anyone interested in this field.

𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞 𝐢𝐬 𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐭𝐡𝐞 𝐠𝐚𝐦𝐞 𝐟𝐨𝐫 𝐝𝐚𝐭𝐚 𝐬𝐭𝐨𝐫𝐚𝐠𝐞 𝐚𝐧𝐝 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐦𝐚𝐤𝐢𝐧𝐠 𝐢𝐭 𝐟𝐚𝐬𝐭𝐞𝐫, 𝐦𝐨𝐫𝐞 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞, 𝐚𝐧𝐝 𝐦𝐨𝐫𝐞 𝐜𝐨𝐬𝐭-𝐞𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞. 🚀💡 🔴 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐁𝐞𝐟𝐨𝐫𝐞 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞: 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐈𝐬𝐬𝐮𝐞𝐬: Traditional data warehouses struggled to scale efficiently as data grew exponentially. 𝐒𝐥𝐨𝐰 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: Large volumes of data took days or weeks to process, delaying critical insights. 𝐇𝐢𝐠𝐡 𝐂𝐨𝐬𝐭𝐬: Managing infrastructure and scaling resources was expensive and resource-intensive. 𝐄𝐓𝐋 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: Extracting, transforming, and loading data was a lengthy and complex process. 𝐋𝐢𝐦𝐢𝐭𝐞𝐝 𝐃𝐚𝐭𝐚 𝐓𝐲𝐩𝐞𝐬: Only structured data could be stored, limiting flexibility in handling diverse data sources. ✅𝐇𝐨𝐰 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞 𝐒𝐨𝐥𝐯𝐞𝐝 𝐓𝐡𝐞𝐬𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬: 𝐄𝐟𝐟𝐨𝐫𝐭𝐥𝐞𝐬𝐬 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Snowflake separates compute and storage, allowing automatic scaling without manual intervention. 𝐅𝐚𝐬𝐭𝐞𝐫 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: With efficient data processing, queries run in seconds instead of days, enabling real-time decision-making. 𝐂𝐨𝐬𝐭 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: Pay-as-you-go model reduces infrastructure costs, allowing businesses to scale as needed without breaking the bank. 𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐝 𝐃𝐚𝐭𝐚 𝐋𝐨𝐚𝐝𝐢𝐧𝐠: Snowflake eliminates complex ETL processes, letting you load raw data and transform it using SQL, Python, or Spark. 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐟𝐨𝐫 𝐀𝐥𝐥 𝐃𝐚𝐭𝐚 𝐓𝐲𝐩𝐞𝐬: Store and analyze both structured and unstructured data, giving businesses the flexibility to handle diverse datasets. #DataSolutions #Snowflake #CloudData #BigData #BusinessIntelligence #DataEngineering

Excellent Breakdown of different data modeling types along with their basic features!!

🚀 Big Milestone Alert! I'm thrilled to announce that I've completed not one, but two pivotal courses on DataCamp: 𝐈𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝐒𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 𝐈𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝐃𝗮𝘁𝗮 𝐌𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗶𝗻 𝐒𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 This dual accomplishment has significantly deepened my understanding and skills in managing and analyzing data with Snowflake. 📊✨ The "Introduction to Snowflake" course was an immersive dive into Snowflake's architecture and advanced SQL techniques. Here’s what I explored: • The unique aspects of Snowflake’s architecture. • Advanced SQL operations including joins, subqueries, and query optimization. • Handling semi-structured data, particularly JSON. Following that, the "Introduction to Data Modeling in Snowflake" course transformed my approach to data, turning complex datasets into neatly organized, insightful models: • Core principles of data modeling, engaging hands-on with entity-relationship, dimensional modeling, and data vault techniques. • Techniques to optimize data retrieval and analytics using snowflake’s massively parallel processing capabilities. These courses not only enhanced my technical skills but also equipped me with strategic insights to make data-driven decisions effectively. Whether you are an analyst, data engineer, or a tech enthusiast looking to navigate the vast landscape of data, these learning experiences are invaluable. I’m excited to apply this knowledge in real-world scenarios and contribute more effectively to data-driven projects. #DataScience #Snowflake #DataAnalytics #DataModeling #SQL #DataCamp #CareerDevelopment #DataWarehouse #StarSchema #DataVault

Here's a short guide to bronze, silver, and gold data layers, what they are, and how to build them. Like most frameworks, bronze-silver-gold is vendor-developed and designed to simplify the ingestion and storage of big data. I believe Databricks created the concept, but I could be incorrect. Please correct me if I'm wrong. Here's how it applies to structured data: Your bronze data layer is data as originally ingested. It will often be in the form of JSON, parquet, or CSV files. It will be terribly hard for anyone except a data engineer to make sense of it, so you should restrict access to data engineers. Data in the bronze layer's purpose is to be used to make silver data. Your silver data layer takes that bronze-level data and turns it into either tabular data or more structured JSON. This will be done by combining files to make tables, flattening data, applying insert/delete/update/upsert logic, and joining tables together where it makes sense. Commonly, silver-level data is in the Star Schema and One Big Table (OBT, often delta lake) format. Data in the silver layer's purpose is to be used to make gold data and for data scientists / data analysts to putz around in to figure out what data they need. Your gold data layer takes the silver data, applies business logic to it, and uses it to make tables that analysts can query, that developers can use to make dashboards, and that data scientists can use to train and inference machine learning models. Your goal with gold tables should be simplicity and usability; ten people with the same business question should be able to get the same answer when using gold tables. Biggest mistake is data analyst/scientists working off silver layer tables and thinking they know both the data and business logic well enough to produce accurate results. You want to grant them access to silver data so they can tell you what they need from those tables only. A basic example is, "I want sales data, but without cancellations while keeping returns." You can then make that gold table for them. #datamodeling #dataengineering #starschema #databricks #obt

Excited to dive into the world of data modeling? Let's explore the diverse landscape of data modeling types! 💡 1️⃣ Conceptual Data Modeling: Lay the foundation with high-level, business-focused models to capture entities and relationships. 2️⃣ Logical Data Modeling: Get into the nitty-gritty of database design, crafting detailed structures using entities, attributes, and relationships. 3️⃣ Physical Data Modeling: Time to bring your models to life! Design databases, tables, and indexes, optimizing for performance and storage efficiency. 4️⃣ Relational Modeling: Harness the power of relational databases, organizing data into tables with defined relationships, enforcing integrity constraints, and enabling SQL querying. 5️⃣ Entity-Relationship Modeling: Visualize the relationships between entities in a system, mapping out the connections and cardinalities to understand data dependencies. 6️⃣ Object-Oriented Modeling: Bridge the gap between software engineering and data modeling, representing data as objects with properties and behaviors for object-oriented databases. 7️⃣ Star Schema: Dive into dimensional modeling for data warehousing, organizing data into a central fact table surrounded by dimension tables, enabling efficient analytical queries. 8️⃣ Dimensional Modeling: Navigate the complexities of data warehousing with star schemas, snowflake schemas, and fact tables for analytical insights. 9️⃣ NoSQL Data Modeling: Embrace the flexibility of NoSQL databases, designing schemas tailored to document, key-value, column-family, or graph data models. Each type of data modeling brings its unique strengths to the table, empowering organizations to effectively manage and derive value from their data assets. 💼💻 #DataModeling #DataManagement #DatabaseDesign #Analytics #NoSQL #TechTrends

A great graphical representation of Data models, Shared by Sheikh Izhar, what do you think, can you perceive any other layout, Let's connect and discuss how we can drive innovation and success together. #TechLeadership #Innovation #Mentorship

I’ve recently completed the "Introduction to Data Modeling in Snowflake" course on DataCamp. This course was incredibly informative and gave me a solid foundation in data modeling, while providing plenty of practical exercises to put the theory into action. Throughout the course, I explored: - The fundamentals of data modeling and how it can help organize data more efficiently - How to manage data relationships and apply normalization techniques - Different data modeling approaches like Entity–Relationship, Dimensional Modeling, and Data Vault - Snowflake’s architecture and how it optimizes query performance for faster, more efficient data processing I’m looking forward to applying these new skills and further exploring how data modeling can improve decision-making in a cloud-based environment. #DataScience #CloudComputing #DataEngineering #TechSkills #SnowflakeDataWarehouse #BigData #Analytics #SQL #DataArchitecture #TechLearning #ProfessionalDevelopment #DataStrategy #DataDriven #DigitalTransformation #CareerGrowth #CloudData

Introduction to Data Modeling in Snowflake