Mory Kaba’s Post

🚀 Diving Deep into Data Warehousing and Data Architecture! 🚀 As a Data Engineer, mastering the fundamentals of data storage and management is key to success. Here are some critical topics I've been exploring and applying in my work: 🔹 Data Lake vs Data Warehouse vs Database – Understanding the differences and selecting the right storage strategy based on the needs of the business. 🔹 Data Warehouse Layers – From staging and integration to analytics, each layer plays a vital role in delivering clean, actionable data. 🔹 OLAP CUBE – Efficiently analyzing data across multiple dimensions for fast and flexible reporting. 🔹 Dimensional Modeling – Creating intuitive and scalable models using Facts and Dimensions for robust data analysis. 🔹 Types of Facts and Dimension Tables – Delving into different types of fact tables (transactional, snapshot, and accumulating) and dimensions (SCD types) for optimized modeling. 🔹 Star vs Snowflake Schema – Balancing simplicity and normalization to suit the complexity of the data. 🔹 Bitmap vs B-tree Indexes – Choosing the right index strategy for performance optimization in specific use cases. 🔹 Keys in Data Warehousing – Understanding primary, foreign, surrogate, and composite keys to maintain relationships and integrity. 🔹 Normalization vs Denormalization – Striking the balance between minimizing redundancy and improving query performance. 🔹 Types of Normalization – First, second, and third normal forms (1NF, 2NF, 3NF) and beyond to ensure efficient database design. 🔹 Layers of Data Modeling – Conceptual, logical, and physical layers for structured and comprehensive data models. 🔹 Masking and Tokenization – Essential techniques for securing sensitive data and ensuring privacy compliance. 🔹 Semantic Views vs Non-semantic Views – Creating meaningful data abstractions for business users while maintaining technical accuracy. 🔹 Tables vs Views – The differences between physical data storage and virtualized data representation. 🔹 Cardinality – Understanding data relationships to optimize queries and database design. Data warehousing and data architecture are crucial for building efficient, scalable, and secure data systems. I'm continuously learning and evolving in this exciting space! #DataEngineering #DataWarehouse #DataArchitecture #DataModeling #OLAP #FactsAndDimensions #DataSecurity #Normalization #BigData #SQL #DataLake #Database

Database Normalization for Data Engineering: From No Normalization to 3NF What is Database Normalization? Database normalization is the process of organizing your data to reduce redundancy and improve integrity by breaking down large, complex tables into smaller, more structured ones. This is done by applying a series of rules called Normal Forms (NFs), which minimize dependencies between tables and lead to fewer data inconsistencies. Normalization typically involves dividing tables into smaller ones and linking them using foreign key constraints. The most common Normal Forms are 1NF, 2NF, and 3NF, though more advanced forms exist for specific use cases. Why is Normalization Important for Data Engineers? As a data engineer, whether you’re modeling your data as One Big Table (OBT), using a star schema, snowflake schema, or even a Data Vault, understanding normalization is crucial. Each of these approaches follows different degrees of normalization: • OBT (One Big Table) is the least normalized, often in 1NF, and combines both fact and dimension tables into one. • Star schemas and snowflake schemas typically keep fact tables in 3NF for data integrity, while dimension tables are often in 2NF. • Data Vault is highly normalized and can follow even stricter rules. Understanding these differences allows you to apply the correct normalization form depending on your use case, balancing complexity with performance: 1. Unnormalized Form (UNF): Contains duplicate data and repeating groups, not divided into separate tables. 2. First Normal Form (1NF): Ensures that all values are atomic (indivisible) and each record is unique. 3. Second Normal Form (2NF): Removes partial dependencies, meaning attributes should only depend on the entire primary key (not a subset of it). 4. Third Normal Form (3NF): Removes transitive dependencies, where non-key attributes depend only on the primary key and not on other non-key attributes. Lets look at a simple example on how to take a table from no normalization to 3NF: For more content, follow Anuj Shrivastav. 💡📈 Feel free to reshare ♻️ this post if you find it helpful! 🔁 #DatabaseNormalization #DataEngineering #DataModeling #SQL #DataIntegrity #3NF #DataArchitecture #ETL #DataWarehouse #StarSchema #DataVault #RelationalDatabase #DBDesign #DatabaseOptimization

𝗦𝗤𝗟 𝗮𝘀 𝗗𝗮𝘁𝗮 𝗠𝗲𝘀𝗵 𝗔𝗣𝗜 𝗨𝘁𝗶𝗹𝗶𝘇𝗶𝗻𝗴 𝗦𝗤𝗟 𝗮𝘀 𝘁𝗵𝗲 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 𝗔𝗣𝗜 In the realm of data engineering, the concept of Data Mesh brings forth unique challenges and opportunities. Here's a breakdown: 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗠𝗲𝘀𝗵: Instead of centralizing data, the Data Mesh approach allows different domains to create and manage their own data products, emphasizing domain-specific knowledge and ownership. 𝗨𝘁𝗶𝗹𝗶𝘇𝗶𝗻𝗴 𝗦𝗤𝗟 𝗮𝘀 𝘁𝗵𝗲 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 𝗔𝗣𝗜: Contrary to using APIs for data delivery, leveraging SQL offers a standardized and familiar language for analytics, addressing concerns regarding cross-domain analysis and governance. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗳𝗼𝗿 𝗦𝘂𝗰𝗰𝗲𝘀𝘀: Tools like Starburst and Immuta provide standardized frameworks for data delivery and governance, ensuring consistency and security across domain-specific data products. By embracing SQL as the core API and implementing robust frameworks, data engineers can effectively navigate the complexities of Data Mesh architecture, fostering collaboration and innovation across diverse industries. 𝗙𝗼𝗿 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗱𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝗰𝗿𝗼𝘀𝘀 𝗱𝗶𝘃𝗲𝗿𝘀𝗲 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀, 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗲 𝘂𝘁𝗶𝗹𝗶𝘇𝗶𝗻𝗴 𝗦𝗤𝗟 𝗮𝘀 𝗮 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲𝗱 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝘀𝗵𝗮𝗿𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 𝘄𝗶𝘁𝗵𝗶𝗻 𝗮 𝗱𝗮𝘁𝗮 𝗺𝗲𝘀𝗵 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲. #SQL #API #DataMesh #DataEngineering #DataIntegration #DataGovernance #DataAnalytics #DataProducts #DataArchitecture #DataManagement #DataScience #TechIndustry #DigitalTransformation #Framework #DataTools #Innovation https://lnkd.in/dMYyAwKT

Many data engineering teams lack the core skills of data modeling. It is time to rediscover the lost art of data modeling. Dave Wells latest article provides a quick and high-level look at data modeling for relational data. It is the most mature of today’s data modeling techniques, and it is important because it is the foundation for more recent techniques. Read to know why ER modeling knowledge and skill is something that is needed in every data engineering team: https://lnkd.in/er5mBpQV #datamodelling #lostart #datamanagement

The 'Big Data Scourge' The challenge with 'big data engineers' today is that they tend to approach all data pipeline builds as big data tasks. But the truth is, most companies don't need a big data solution. Regular SQL data stores work just fine, especially for small to medium scale companies. I've seen several architecture diagrams that have no business with 'modern data tools' using them. This ends up bloating the pipe, increasing possible points of failure, racking up avoidable cost and most importantly, losing the opportunity to deliver effectively. It's important for big data engineers to have experience working with 'small data' at some point in their career. This will enable them to approach data pipeline builds with the right mindset and avoid the 'big data scourge.' Remember, most big data warehouses recommend their solutions shine when petabytes of data are involved. So, let's not overcomplicate things. Overall, my argument is that it's important for companies to carefully consider their data needs and choose the right tools and solutions accordingly, rather than blindly follow the trend by defaulting to big data solutions for all scenarios. #AskPyFesh

📚 Dive into the latest article to download the Data Engineering Cookbook and explore the comprehensive Data Engineering Roadmap. Perfect for both aspiring and seasoned data engineers, this guide will equip you with the tools and insights you need to excel in the field. 🔗 Read the full article and get your copy now! 🏃♂️ https://lnkd.in/exYXATjH #DataEngineering #CareerGrowth #Tech #DataScience #DataEngineeringRoadmap

Excited to share my latest blog post: "The Data Engineer’s Handbook: Roles, Big Data, 3-Tier Architecture, ETL/ELT, and Career Paths" 📚 Dive into the world of data engineering and explore: 🔍 Who is a Data Engineer? 📊 Roles and Responsibilities 🌐 The intricacies of Big Data 🏗️ Understanding 3-Tier Architecture 🔄 Differences between ETL and ELT Whether you're a seasoned professional or just starting out, this comprehensive guide offers valuable insights and practical knowledge to help you succeed in the data engineering landscape. Don't miss out! 💡 https://lnkd.in/dydrGpZm #DataEngineering #BigData #ETL #ELT #CareerGrowth #TechTrends #100DaysofByte #dataanalyst #DataEngineering #datascientist #DataEngineer

🔍 Optimizing Query Performance: Tips for Data Engineers and Developers As data engineers and developers, optimizing query performance is crucial for ensuring efficient data retrieval and processing. Here are some effective strategies to enhance query performance across various layers of your data architecture: 1. Storage Layer Optimization 🪵 Data Pruning: Reduce the amount of data scanned by queries. Implement data pruning techniques to skip irrelevant data segments, significantly cutting down query execution time. Tools like columnar storage formats (e.g., Parquet) are great for this. 📂 Partitioning: Organize your data into partitions based on specific criteria (e.g., date, region). This allows queries to access only the relevant partitions, improving read efficiency and reducing I/O operations. 🔄 Eventual Consistency: Leverage eventual consistency models to improve write performance. By allowing temporary inconsistencies, you can achieve higher availability and throughput, especially in distributed databases. 2. Application Layer Optimization ⚡ Caching: Use caching mechanisms to store frequently accessed data in memory. Tools like Redis or Memcached can drastically reduce the need to re-query the database, providing faster data access and reducing load. 🔧 Application Tuning: Optimize your application's database interaction by using connection pooling, efficient query construction, and minimizing the number of database hits. Profiling your application can help identify bottlenecks and improve performance. 3. Query Layer Optimization 🔎 Indexing: Create indexes on columns that are frequently used in query conditions (e.g., WHERE clauses). Indexes can speed up data retrieval but be mindful of their impact on write performance and storage requirements. 🗃️ Materialized Views: Use materialized views to store the results of complex queries. This can save processing time by avoiding the need to recompute results for each query execution. Schedule regular updates to keep the views current with underlying data changes. Why It Matters Optimizing queries not only enhances performance but also ensures a better user experience and more efficient resource utilization. By focusing on these layers, you can achieve significant improvements in your data handling capabilities. Share Your Thoughts What strategies have you found most effective in optimizing query performance? Share your tips and experiences in the comments below! 👇 #DataEngineering #QueryOptimization #DatabasePerformance #TechTips #BigData #DataScience

📊 Data Engineering Made Simple: Why ‘Partitioning’ is the Key to Faster Queries!🚀 What’s the Secret Sauce Behind Faster Data Processing? Have you ever tried searching for a specific document in a messy drawer? It takes forever, right? Now imagine if that drawer had neatly labeled sections for each category—your search would take seconds! That’s exactly what partitioning does in data engineering. What is Partitioning? Partitioning is a way to divide large datasets into smaller, manageable chunks based on some logical criteria like date, region, or category. These chunks (or “partitions”) allow systems to only scan what’s needed, instead of combing through the entire dataset. Why Does it Matter? Imagine running a query on a 1 TB table to find last week’s sales. Without partitioning, the system scans all 1 TB. But with partitioning by date, it only looks at one week’s data—maybe just a few GBs. Result? Faster query times and lower costs. A Simple, Relatable Example Think of a giant library (your dataset). If books are scattered randomly, you’d spend hours finding one. But if they’re organized into sections (partitions) like Fiction, History, and Science, you’d go straight to the right section. Partitioning does the same for your data. For instance: • Partition by Date: Access only last month’s logs. • Partition by Region: Focus on sales from Australia without scanning global data. How to Use Partitioning in Tools You Know • In BigQuery, use partitioned tables (e.g., partition by a DATE field). • In Hive, you can partition by category or region. • In Spark, specify a partitionBy() column during your write process. Key Takeaways 1. Partitioning reduces the amount of data scanned, saving time and money. 2. It works best for large datasets where queries focus on subsets of data. 3. Tools like BigQuery, Hive, and Spark make partitioning easy to implement. Quick Challenge for You: Next time you run a query, ask yourself—Am I scanning too much data? Would partitioning help? Also check cost of that query and it will give you a lot of insights and you can make use of partitioning to reduce cost and save. #DataEngineering #Partitioning Let me know if you find this helpful or have more questions about partitioning! 🚀