Database Management Systems

Tools are the fashion; Data Modeling is the skeleton. You can swap Airflow for Prefect, or Spark for DuckDB. But you can’t swap "bad logic" for a faster engine and expect it to work. In one project, I used Airflow. In another, Spark. Lately, it’s all dbt. But 100% of the time, the win came down to Data Modeling fundamentals. Building a data platform without modeling is like building a skyscraper on a swamp. It doesn't matter how expensive your gold-plated elevators (tools) are if the foundation is sinking. Here's what actually matters: 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 = 𝗦𝗽𝗲𝗲𝗱 Star schemas make queries fast. Facts and dimensions separated = happy analysts. 𝗦𝗖𝗗𝘀 𝗪𝗶𝗹𝗹 𝗕𝗶𝘁𝗲 𝗬𝗼𝘂 Skip SCD Type 2 tracking? Debug why historical reports show wrong data at 2 AM. 𝗡𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗜𝘀𝗻'𝘁 𝗥𝗲𝗹𝗶𝗴𝗶𝗼𝗻 OLTP systems? Normalize for integrity. OLAP systems? Denormalize for speed. Know your world. Design accordingly. 𝗗𝗮𝘁𝗮 𝗩𝗮𝘂𝗹𝘁 = 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 Business requirements changing weekly? Data Vault keeps you sane. Verbose but bulletproof. 👉 Here are the real Non-negotiables:  • Model for how data will be queried, not just stored  • Document your grain—ambiguity kills data trust  • Surrogate keys > natural keys (trust me on this)  • Test your model with real queries before building pipelines My 2 cents: Master data modeling, and every tool becomes easier. Skip it, and you'll spend your career firefighting broken pipelines. Are you willing to upskill❓Explore these resources: → Michael K.'s KahanDataSolutions - https://lnkd.in/g4JSFPph → Benjamin Rogojan's Seattle Data Guy - https://lnkd.in/ghewnvBX → The Data Warehouse Toolkit by Ralph Kimball - https://lnkd.in/dTynC6yD Image Credits: Shubham Srivastava Every pipeline you build will eventually be replaced. A solid data model? That becomes the language of the company. What's one data modeling mistake that cost you hours of debugging? Let's learn together. 👇

It feels great to launch a new data product, but don't forget about the work that follows afterward! Here are steps that will help to keep it relevant for a long time: 1. 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗣𝗲𝗿𝗶𝗼𝗱𝗶𝗰 𝗥𝗲𝘃𝗶𝗲𝘄𝘀: Business goals and data needs change over time. Establish a routine for reviewing your data product’s usage and relevance. Is it still meeting the needs of your users? 2. 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸: Create channels for ongoing feedback and encourage users to report issues or suggest improvements. 3. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁𝘀: Use feedback and review outcomes to make relevant improvements. This could mean refining visualizations, adding new data points, or optimizing performance. Most data products are never truly finished. 4. 𝗘𝗱𝘂𝗰𝗮𝘁𝗲 𝗮𝗻𝗱 𝗘𝗻𝗮𝗯𝗹𝗲 𝗨𝘀𝗲𝗿𝘀: Offer training sessions for new features or changes. Enable users to fully utilize the data product, ensuring it remains a valuable tool that gets regularly used. 5. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗖𝗵𝗮𝗻𝗴𝗲𝘀: Keep a changelog or documentation of updates and modifications. This transparency helps manage expectations and provides a history of the product’s progression. 6. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Continuously monitor the data product’s performance and reliability to ensure it functions well under changing conditions. Identify and address issues before they impact your stakeholders. 7. 𝗧𝗮𝗿𝗴𝗲𝘁 𝗡𝗲𝘄 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀: Regularly check for opportunities to expand your data product's functionality or apply it to new business use cases. Staying proactive and anticipating needs will keep your work results relevant for a long time.     8. 𝗞𝗻𝗼𝘄 𝗪𝗵𝗲𝗻 𝘁𝗼 𝗦𝗮𝘆 𝗚𝗼𝗼𝗱𝗯𝘆𝗲: Not all data products are meant to last forever. Recognize when a product no longer serves its purpose and plan for its retirement or replacement. This decision ensures resources are focused on tools that continue to deliver value to the business. Handling the post-launch lifecycle is an important task. Continuous improvement and alignment with changing needs will ensure your data products stay relevant for the business. What’s your experience with maintaining data products post-launch? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find this post useful ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field #dataanalytics #datascience #dataproducts #productmanagement #careergrowth

Have you ever wondered how to manage a Data Pipeline efficiently? This detailed visual breaks down the architecture into five essential stages: Collect, Ingest, Store, Compute, and Use. Each stage ensures a smooth and efficient data lifecycle, from gathering data to transforming it into actionable insights. Collect: Data is gathered from a variety of internal and external sources, including: -- Mobile Applications and Web Apps: Data generated from user interactions. -- Microservices: Capturing microservice interactions and transactions. -- IoT Devices: Collecting sensor data through MQTT protocols. -- Batch Data: Historical data collected in batches. Ingest: In this stage, the collected data is ingested into the system through batch jobs or streaming methods: -- Event Queue: Manages and queues incoming data streams. -- Extracting Raw Event Stream: Moving data to a data lake or warehouse. -- Tools Used: MQTT for real-time streaming, Kafka for managing data streams, and Airbyte or Gobblin for data integration. Store: The ingested data is then stored in a structured manner for efficient access and processing: -- Data Lake: Storing raw data in its native format. -- Data Warehouse: Structured storage for easy querying and analysis. -- Technologies Used: MinIO for object storage, Iceberg, and Delta Lake for managing large datasets. Compute: This stage involves processing the stored data to generate meaningful insights: -- Batch Processing: Handling large volumes of data in batches using tools like Apache Spark. -- Stream Processing: Real-time data processing with Flink and Beam. -- ML Feature Engineering: Preparing data for machine learning models. -- Caching: Using technologies like Ignite to speed up data access. Use: Finally, the processed data is utilized in various applications: -- Dashboards: Visualizing data for business insights using tools like Metabase and Superset. -- Data Science Projects: Conducting complex analyses and building predictive models using Jupyter notebooks. -- Real-Time Analytics: Providing immediate insights for decision-making. -- ML Services: Deploying machine learning models to provide AI-driven solutions. Key supporting functions such as: -- Orchestration: Managed by tools like Airflow to automate and schedule tasks. -- Data Quality: Ensuring the accuracy and reliability of data throughout the pipeline. -- Cataloging: Maintaining an organized inventory of data assets. -- Governance: Enforcing policies and ensuring compliance with frameworks like Apache Atlas. This comprehensive guide illustrates how each component fits into the overall pipeline, showcasing the integration of various tools and technologies. Check out this detailed breakdown and see how these elements can enhance your data management strategies. How are you currently handling your data pipeline architecture? Let's discuss and share best practices! #data #ai #datapipeline #dataengineering #theravitshow

Your database has 50 indexes. And it's SLOWER than having 5. Here's what most engineers get wrong about indexing: We treat indexes like free performance boosts. But every index you add is a hidden contract: - Every INSERT now updates N+1 data structures - Every UPDATE potentially rewrites multiple B-trees - The query planner gets confused with too many choices - Your working set no longer fits in memory I learned this the hard way at scale. We had a table with 34 indexes. Reads were fast. Writes were dying. P99 latency on inserts hit 1.2 seconds. The fix? We dropped 28 indexes. But here's the part nobody talks about: We replaced them with 3 composite indexes that covered 94% of our query patterns. The trick was analyzing pg_stat_user_indexes. Most of our indexes had ZERO scans in 30 days. They were dead weight burning I/O on every write. Here's the framework I now use: 1. Audit index usage monthly (pg_stat_user_indexes) 2. Every index must justify its write amplification cost 3. Composite indexes > single-column indexes (almost always) 4. Covering indexes eliminate heap lookups entirely 5. Partial indexes for queries that filter on a constant The result after cleanup: • Write latency dropped 73% • Storage shrank by 40% • Read performance stayed identical The best performance optimization isn't adding something new. It's removing what shouldn't be there. 💬 What's the worst index bloat you've seen? #SystemDesign #DatabaseEngineering #SoftwareEngineering #PostgreSQL #Performance

𝗛𝗼𝘄 𝘁𝗼 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲? Here are the most important ways to improve your database performance: 𝟭. 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 Add indexes to columns you frequently search, filter, or join. Think of indexes as the book's table of contents - they help the database find information without scanning every record. But remember: too many indexes slow down write operations. 💡 𝗕𝗼𝗻𝘂𝘀 𝘁𝗶𝗽: Regularly drop unused indexes. They waste space and slow down writing without providing any benefit. 𝟮. 𝗠𝗮𝘁𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗲𝗱 𝗩𝗶𝗲𝘄𝘀 Pre-compute and store complex query results. This saves processing time when users need the data again. Schedule regular refreshes to keep the data current. 𝟯. 𝗩𝗲𝗿𝘁𝗶𝗰𝗮𝗹 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 Add more CPU, RAM, or faster storage to your database server. This is the most straightforward approach, but has physical and cost limitations. 𝟰. 𝗗𝗲𝗻𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Duplicate some data to reduce joins. This technique trades storage space for speed and works well when reads outnumber writes significantly. 𝟱. 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 Store frequently accessed data in memory. This reduces disk I/O and dramatically speeds up read operations. Popular options include Redis and Memcached. 𝟲. 𝗥𝗲𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Create copies of your database to distribute read operations. This works well for read-heavy workloads but requires managing data consistency. 𝟳. 𝗦𝗵𝗮𝗿𝗱𝗶𝗻𝗴 Split your database horizontally across multiple servers. Each shard contains a subset of your data based on a key like user_id or geography. This distributes both read and write loads. 𝟴. 𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴 Divide large tables into smaller, more manageable pieces within the same database. This improves query and maintenance operations on huge tables. 🎁 𝗕𝗼𝗻𝘂𝘀: 🔹 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗽𝗹𝗮𝗻𝘀. Use EXPLAIN ANALYZE to see precisely how your database executes queries. This reveals hidden bottlenecks and helps you target optimization efforts where they matter most. 🔹 𝗔𝘃𝗼𝗶𝗱 𝗰𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗲𝗱 𝘀𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀. These run once for every row the outer query returns, creating a performance nightmare. Rewrite them as JOINs for dramatic speed improvements. 🔹 𝗖𝗵𝗼𝗼𝘀𝗲 𝗮𝗽𝗽𝗿𝗼𝗽𝗿𝗶𝗮𝘁𝗲 𝗱𝗮𝘁𝗮 𝘁𝘆𝗽𝗲𝘀. Using VARCHAR(4000) when VARCHAR(40) would work wastes space and slows performance. Right-size your data types to match what you're storing. #technology #systemdesign #databases #sql #programming

As we close out the year (and on the back of my 'Best of 2024' article yesterday), I'll post my top Data Ecosystem infographics and posts for the next few weeks. And let's start with Data Modelling! Data success in business does not start with an AI product, a well-constructed pipeline, or a frequently used dashboard. Success starts with the business model. This, then helps inform the org data model. Your 𝐝𝐚𝐭𝐚 𝐦𝐨𝐝𝐞𝐥 𝐰𝐢𝐥𝐥 𝐭𝐮𝐫𝐧 𝐭𝐡𝐞 𝐨𝐫𝐠𝐚𝐧𝐢𝐬𝐚𝐭𝐢𝐨𝐧’𝐬 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 𝐢𝐧𝐭𝐨 𝐭𝐚𝐧𝐠𝐢𝐛𝐥𝐞 𝐝𝐚𝐭𝐚 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 𝐚𝐧𝐝 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 to drive business results. Unfortunately most organisations skip this step and get into a mess of data engineering quick fixes Here’s a breakdown of the process from business model to each part of the data model: 1. Start with the 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐌𝐨𝐝𝐞𝐥 🏢 Map out how your organisation creates, delivers, and captures value. For example, company creates product x, ships to store y, sells to customer z and makes money. Within that, what are the data points to track and understand? You map this out, you understand the process of how the organization makes money. 2. Extending it to the 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐮𝐚𝐥 𝐃𝐚𝐭𝐚 𝐌𝐨𝐝𝐞𝐥 🗺️ The conceptual data model then builds on top of this, rewriting how the business operates using a high-level representation of organisational data. This phase involves identifying key data entities & domains and mapping the relationships between them. This should be understandable by business stakeholders as this is the bridge between business processes and data development. 3. Adding structure with the 𝐋𝐨𝐠𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐌𝐨𝐝𝐞𝐥 🏙️ Next, we refine into a logical data model. Here, we delve deeper to define the data types, attributes of each entity and the nature of the relationships. This model is still independent of technical implementation but sets out a clear structure (often through ERDs or UML) for how data is related and organised. 4. Building the 𝐏𝐡𝐲𝐬𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐌𝐨𝐝𝐞𝐥 foundations 🏗️ Finally, we arrive at the physical data model. It’s the actual database schema design, complete with tables, columns, data types, and constraints. It also takes into account the performance requirements, optimization techniques, and the physical storage of the data. Obviously this is a broad simplification of the process. Each step of this journey requires collaboration between business leaders, data architects, and engineers to ensure the data models align with the strategic goals, is technically feasible and is understood by all relevant stakeholders. Building this properly helps everybody understand how data actually delivers value. Too often we engineer without the architecture to structure it. Fix this and you will be way better off in the long-term. #DataModeling #DataEngineering #BusinessModel #DataArchitecture #DataStrategy #DylanDecodes

𝗘𝘃𝗲𝗿 𝗯𝗲𝗲𝗻 𝗮𝘀𝗸𝗲𝗱 𝗮 𝗱𝗮𝘁𝗮 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗶𝗻 𝗮𝗻 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 — 𝗹𝗶𝗸𝗲 𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗮 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿–𝗽𝗿𝗼𝗱𝘂𝗰𝘁–𝗼𝗿𝗱𝗲𝗿 𝘀𝗰𝗵𝗲𝗺𝗮 — 𝗮𝗻𝗱 𝘀𝘂𝗱𝗱𝗲𝗻𝗹𝘆 𝗳𝗲𝗹𝘁 𝘀𝘁𝘂𝗰𝗸? You’re not alone. This happens even to experienced data engineers who spend most of their time building pipelines, tuning Spark jobs, or managing cloud infra. Here’s the hard truth I’ve learned over time: 𝗡𝗼 𝗺𝗮𝘁𝘁𝗲𝗿 𝗵𝗼𝘄 𝗺𝗼𝗱𝗲𝗿𝗻 𝘆𝗼𝘂𝗿 𝘀𝘁𝗮𝗰𝗸 𝗶𝘀 — 𝗔𝘇𝘂𝗿𝗲, 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀, 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲, 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 — 𝗶𝗳 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗺𝗼𝗱𝗲𝗹 𝗶𝘀 𝘄𝗲𝗮𝗸, 𝘆𝗼𝘂𝗿 𝗲𝗻𝘁𝗶𝗿𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗶𝘀 𝗳𝗿𝗮𝗴𝗶𝗹𝗲. After multiple interviews and real-world projects, one thing became very clear to me. The strength of a data platform comes down to 𝘁𝘄𝗼 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 👇 1️⃣ 𝗦𝘁𝗿𝗼𝗻𝗴 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 Data modeling isn’t about drawing boxes in an ER diagram. It’s about answering 𝗱𝗲𝘀𝗶𝗴𝗻-𝗱𝗲𝗳𝗶𝗻𝗶𝗻𝗴 questions: • Should this be a 𝗳𝗮𝗰𝘁 or a 𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻? • What’s the 𝗴𝗿𝗮𝗻𝘂𝗹𝗮𝗿𝗶𝘁𝘆 — transaction-level, daily, aggregated? • Are we using 𝗻𝗮𝘁𝘂𝗿𝗮𝗹 𝗸𝗲𝘆𝘀 𝗼𝗿 𝘀𝘂𝗿𝗿𝗼𝗴𝗮𝘁𝗲 𝗸𝗲𝘆𝘀, and why? • How do we handle 𝗦𝗹𝗼𝘄𝗹𝘆 𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀 (𝗦𝗖𝗗𝘀) without breaking history? • Are we trading off 𝗱𝗮𝘁𝗮 𝗿𝗲𝗱𝘂𝗻𝗱𝗮𝗻𝗰𝘆 𝘃𝘀 𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝗶𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 correctly? A well-designed dimensional model: • Simplifies analytics • Enables true self-service BI • Reduces query complexity • Prevents silent data inconsistencies 2️⃣ 𝗦𝗼𝗹𝗶𝗱 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 Even in lakehouse or medallion architectures, warehouse thinking still matters. • Bronze → Silver → Gold layers enforce 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗹𝗶𝗻𝗲𝗮𝗴𝗲, 𝗮𝗻𝗱 𝘁𝗿𝘂𝘀𝘁 • Partitioning & clustering decide whether queries scale or crawl • Star vs Snowflake schema impacts 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗗𝗔𝗫 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 • Denormalization improves read performance — but increases maintenance cost I’ve seen teams spend months building pipelines, only to realize dashboards are slow because: 𝗳𝗶𝗹𝘁𝗲𝗿𝘀 𝗱𝗼𝗻’𝘁 𝘀𝗹𝗶𝗰𝗲 𝗳𝗮𝗰𝘁 𝘁𝗮𝗯𝗹𝗲𝘀 𝗰𝗼𝗿𝗿𝗲𝗰𝘁𝗹𝘆 — 𝗮 𝗽𝘂𝗿𝗲 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗶𝘀𝘀𝘂𝗲. 𝗙𝗶𝗻𝗮𝗹 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 Whether you’re preparing for interviews or already working as a data engineer: 𝗗𝗮𝘁𝗮 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗶𝘀𝗻’𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹. 𝗜𝘁’𝘀 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗲𝗹𝘀𝗲 𝘀𝘁𝗮𝗻𝗱𝘀 𝗼𝗻. Pipelines can be rewritten. Infrastructure can be scaled. A broken data model is much harder to fix. Curious to know 👇 𝗛𝗮𝘃𝗲 𝘆𝗼𝘂 𝗯𝗲𝗲𝗻 𝗮𝘀𝗸𝗲𝗱 𝗱𝗮𝘁𝗮 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀? 𝗢𝗿 𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝘁𝗵𝗶𝘀 𝗹𝗲𝘀𝘀𝗼𝗻 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝘄𝗮𝘆 𝗼𝗻 𝗮 𝗽𝗿𝗼𝗷𝗲𝗰𝘁? Follow Aishwarya Pani for more practical, real-world data engineering insights.

Difference between Data Object Graphs and Knowledge Graphs Lot's of people have asked me how Data Object Graphs (DOGs) differ from traditional Knowledge Graphs (KGs). So I thought I share my perspectives: The Key Difference (TL;DR): While Knowledge Graphs excel at representing what things ARE, Data Object Graphs excel at modelling (and executing) what things DO. Specifically, Data Object Graphs (DOGs): - Model direct behaviour and are direct business process implementations - DOGs map 1:1 to actual business operations, enabling rapid translation from modelling to execution - Represent and are the exact business process come to life (e.g., Customer → appliesForLoan → RiskDept → scoresLoan → ReportCreator →createsReport) - Include specific executable steps that are implemented by a specific executable data product containers (e.g. Metrics, ML models, decisions etc..) with interactions directly mapped to business actions (specific API / graph calls). - Have agility - Business changes can be quickly implemented without complex intermediate abstractions - Provide immediate operational value through executable modelling - Faithfully model the real-world - ie what actually happens in your business, not theoretical abstractions - Enables simulation of business ideas with real data and execution capabilities Knowledge Graphs (KGs): - Tend to focus on formal semantic relationships between conceptual entities - Emphasize standardized ontologies and taxonomies - Excel at representing knowledge relationships beyond operational contexts - Provide semantic reasoning capabilities through formalized structures - Designed primarily for knowledge representation rather than process execution The DOG approach allows organizations to model during business process development rather than at an abstract data level, solving brittleness problems while maintaining enterprise-wide connectivity. The DOGs can go from business process modelling directly to execution with remarkable speed and agility. This allows organizations to adapt quickly to changing business requirements without sacrificing enterprise-wide visibility. These are two very valuable approaches, but have different objectives and goals but can be complementary #DataArchitecture #KnowledgeGraphs #DataObjectGraphs #BusinessProcessModeling #EnterpriseAgility #DataProducts

Most teams think data engineering ends at pipelines. It doesn’t. In 2026, data engineering is an end-to-end lifecycle. And missing one layer breaks the entire system. Here’s how mature teams think about it: → 𝐒𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 • Batch and streaming pipelines • APIs, CDC, connectors • Data enters the system with intent → 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦 𝐰𝐢𝐭𝐡 𝐝𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐞 • Clean, model, validate • Apply data contracts and quality checks • Trust is built here → 𝐒𝐞𝐫𝐯𝐞 𝐟𝐨𝐫 𝐫𝐞𝐚𝐥 𝐜𝐨𝐧𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧 • Query layers, APIs, feature stores • Semantic layer for business access • Data must be usable, not just stored → 𝐄𝐧𝐚𝐛𝐥𝐞 𝐀𝐈 𝐚𝐧𝐝 𝐌𝐋 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 • RAG pipelines, embeddings, vector DBs • Prompt-ready, context-aware data • Data becomes intelligence → 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 𝐢𝐬 𝐚 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧 • Lake, warehouse, lakehouse • Open formats and scalability • Cost vs performance trade-offs → 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐞𝐯𝐞𝐫𝐲 𝐬𝐭𝐚𝐠𝐞 𝐰𝐢𝐭𝐡 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 • Security and access control • Data quality and observability • Orchestration and DataOps • Governance and ownership → 𝐂𝐨𝐧𝐧𝐞𝐜𝐭 𝐭𝐨 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬 • Analytics, dashboards, ML models • GenAI applications and automation • Data drives decisions, not reports The mistake most teams make: They optimize one layer and ignore the rest. But: A fast pipeline with poor quality is still a failed system. The shift is clear: From “building pipelines” → “engineering data ecosystems.” P.S. Which stage in this lifecycle causes the most friction in your team today? Follow Ashish Joshi for more insights