Data Analyst Career Growth

The Evolution of Data Architectures: From Warehouses to Meshes As data continues to grow exponentially, our approaches to storing, managing, and extracting value from it have evolved. Let's revisit four key data architectures: 1. Data Warehouse    • Structured, schema-on-write approach    • Optimized for fast querying and analysis    • Excellent for consistent reporting    • Less flexible for unstructured data    • Can be expensive to scale    Best For: Organizations with well-defined reporting needs and structured data sources. 2. Data Lake    • Schema-on-read approach    • Stores raw data in native format    • Highly scalable and flexible    • Supports diverse data types    • Can become a "data swamp" without proper governance    Best For: Organizations dealing with diverse data types and volumes, focusing on data science and advanced analytics. 3. Data Lakehouse    • Hybrid of warehouse and lake    • Supports both SQL analytics and machine learning    • Unified platform for various data workloads    • Better performance than traditional data lakes    • Relatively new concept with evolving best practices    Best For: Organizations looking to consolidate their data platforms while supporting diverse use cases. 4. Data Mesh    • Decentralized, domain-oriented data ownership    • Treats data as a product    • Emphasizes self-serve infrastructure and federated governance    • Aligns data management with organizational structure    • Requires significant organizational changes    Best For: Large enterprises with diverse business domains and a need for agile, scalable data management. Choosing the Right Architecture: Consider factors like: - Data volume, variety, and velocity - Organizational structure and culture - Analytical and operational requirements - Existing technology stack and skills Modern data strategies often involve a combination of these approaches. The key is aligning your data architecture with your organization's goals, culture, and technical capabilities. As data professionals, understanding these architectures, their evolution, and applicability to different scenarios is crucial. What's your experience with these data architectures? Have you successfully implemented or transitioned between them? Share your insights and let's discuss the future of data management!

Building Data Pipelines has levels to it: - level 0 Understand the basic flow: Extract → Transform → Load (ETL) or ELT This is the foundation. - Extract: Pull data from sources (APIs, DBs, files) - Transform: Clean, filter, join, or enrich the data - Load: Store into a warehouse or lake for analysis You’re not a data engineer until you’ve scheduled a job to pull CSVs off an SFTP server at 3AM! level 1 Master the tools: - Airflow for orchestration - dbt for transformations - Spark or PySpark for big data - Snowflake, BigQuery, Redshift for warehouses - Kafka or Kinesis for streaming Understand when to batch vs stream. Most companies think they need real-time data. They usually don’t. level 2 Handle complexity with modular design: - DAGs should be atomic, idempotent, and parameterized - Use task dependencies and sensors wisely - Break transformations into layers (staging → clean → marts) - Design for failure recovery. If a step fails, how do you re-run it? From scratch or just that part? Learn how to backfill without breaking the world. level 3 Data quality and observability: - Add tests for nulls, duplicates, and business logic - Use tools like Great Expectations, Monte Carlo, or built-in dbt tests - Track lineage so you know what downstream will break if upstream changes Know the difference between: - a late-arriving dimension - a broken SCD2 - and a pipeline silently dropping rows At this level, you understand that reliability > cleverness. level 4 Build for scale and maintainability: - Version control your pipeline configs - Use feature flags to toggle behavior in prod - Push vs pull architecture - Decouple compute and storage (e.g. Iceberg and Delta Lake) - Data mesh, data contracts, streaming joins, and CDC are words you throw around because you know how and when to use them. What else belongs in the journey to mastering data pipelines?

Dear data engineers If your data loads once a day → A cron-based scheduler is enough If your data runs 24/7 across teams → build DAGs, own SLAs, and log every damn thing /— If your team is writing ad-hoc queries → Snowflake or BigQuery works just fine If you're powering production systems → invest in column pruning, caching, and warehouse tuning /— If a schema change breaks 3 dashboards → send a Slack If it breaks 30 downstream systems → build contracts, not apologies /— If your pipeline fails once a week → monitoring is still not optional If your pipeline is in the critical path → observability is non-negotiable /— If your jobs run in minutes → you can get away with Python scripts If your jobs move terabytes daily → learn how Spark shuffles, partitioning, and memory tuning actually work /— If your source systems are stable → snapshotting is a nice-to-have If your upstream APIs are flaky → idempotency, retries, and deduping better be built-in /— If data is just for reporting → optimize for cost If data drives ML models and customer flows → optimize for accuracy and latency /— If you're running a small team → move fast and log issues If you're scaling infra org-wide → document like you’re onboarding your future self – People think Data Engineering is about moving data from A to B. It’s about: – Choosing between fast and correct – Knowing when to drop a job vs debug it for hours – Deciding if it’s worth reprocessing a billion rows because one column was off Data engineers keep the system boring, so other teams can build exciting things on top of it. Found value? Repost it. P.S. Follow me for more such data engineering insights.

𝗢𝗻𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗧𝗵𝗮𝘁 𝗧𝗮𝘂𝗴𝗵𝘁 𝗠𝗼𝗿𝗲 𝗧𝗵𝗮𝗻 𝗔𝗻𝘆 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝗘𝘃𝗲𝗿 𝗗𝗶𝗱 Remember the roadmap for Data Engineering looked like a never-ending list of tools? Learning Data Engineering meant learning 10 tools back-to-back causing chaos. Everywhere we looked, it was: “Master Airflow, Spark, Kafka, DBT, Snowflake, Docker… or you’re not job-ready.” It sounded great on paper, but honestly? We couldn’t explain any of it end-to-end. That's when we decided to stop chasing those checkmarks and pick one project to learn and showcase our experience, from start to finish. We set out to build something simple, but complete: 🎯  YouTube Trending Video Tracker  • 𝗙𝗲𝘁𝗰𝗵 𝗱𝗮𝘁𝗮 𝗳𝗿𝗼𝗺 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗔𝗣𝗜 𝘂𝘀𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻 ✅ Runs as a Python script inside an Airflow DAG (on an EC2 machine, Cloud Composer, or local Airflow setup)  • 𝗖𝗹𝗲𝗮𝗻 𝗮𝗻𝗱 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 𝗱𝗮𝘁𝗮 𝘂𝘀𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻 ✅ Runs in the same Python script, inside Airflow task or a separate Python module  • 𝗟𝗼𝗮𝗱 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 ✅ Done by Python using Snowflake Connector — also inside the Airflow DAG  • 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝘁𝗵𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗶𝗿𝗳𝗹𝗼𝘄 ✅ Airflow runs on a VM (e.g., AWS EC2, GCP Composer, or local server)  • 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝘂𝘀𝗶𝗻𝗴 𝗦𝘁𝗿𝗲𝗮𝗺𝗹𝗶𝘁 ✅ Streamlit app runs separately — typically on a local machine, Streamlit Cloud, or a web server (e.g., EC2) That’s it. Just one project — but done properly, from start to finish. And guess what? → It gave real confidence → Finally understood the flow of a pipeline - how data moves, transforms, and becomes useful. → Had something solid to talk about in interviews ⚠️ 𝗪𝗵𝗮𝘁 𝗥𝗲𝗲𝗹𝘀 𝗦𝗮𝘆: “Learn 10 tools in 30 days” ✅ 𝗪𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗪𝗼𝗿𝗸𝘀: 𝗚𝗼 𝗱𝗲𝗲𝗽 𝗶𝗻𝘁𝗼 𝗼𝗻𝗲 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 — and build everything around it. Freshers, if you’re feeling stuck or overwhelmed, here’s my advice: Don’t learn tools in isolation. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲. 𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗲 𝗮 𝗯𝗶𝘁. 𝗗𝗲𝗽𝗹𝗼𝘆 𝗶𝘁. 𝗦𝗵𝗼𝘄 𝗶𝘁. 𝗧𝗮𝗹𝗸 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁. That’s how you stand out. 📌 Here’s a simple architecture diagram below if you’re willing to get started 👇 #data #engineering #reeltorealdata #YouTube #ETL

BREAKING – Agentic Data Engineering is LIVE!!!! Over the past few weeks, I’ve been listening closely to data engineers talk about what slows them down the most: -- Constantly checking if pipelines broke (and why) -- Manually documenting lineage and logic for onboarding -- Chasing down schema changes after they cause issues -- Writing status updates that don’t reflect the real impact of their work -- Feeling like half their time is spent managing tools—not building That’s why Ascend.io’s announcement on Agentic Data Engineering is getting a lot of attention right now—because it speaks directly to those problems. Here’s what they’ve launched:  https://hubs.li/Q03n44B60 An intelligence core that tracks everything via unified metadata This includes: -- Schema versions -- Pipeline lineage -- Execution state -- Diffs across time And it does this automatically, with no extra config. A programmable automation engine Engineers can write their own triggers, actions, and logic tied to metadata events. It goes beyond traditional orchestration—because the system knows what’s happening inside each pipeline component. Native AI agents built into the platform These aren’t just chat interfaces. They operate on real metadata and help engineers: - Flag breaking changes while you were OOO - Convert components (like Ibis to Snowpark) - Create onboarding guides for new teammates - Trace full lineage of any column - Suggest QA and data quality checks - Summarize your weekly work for 1:1s - Even help prepare resumes by pulling your real impact from work you’ve done The biggest takeaway I’ve heard from engineers so far? This actually feels like it was built with us in mind. Not to replace the role—but to remove the repetition, surfacing the knowledge we usually have to explain again and again. It’s early days, but this looks like a shift in how modern data platforms could be designed: metadata-aware, programmable, and agent-powered from the start. If you want to take a look at the full experience and the agent capabilities, check it out here: https://hubs.li/Q03n44B60 I’m curious—what part of this would help your team the most? Or what’s missing from your current stack that a system like this could take off your plate? #ai #agenticengineering #ascend #theravitshow

🔥 Preparing for a Data Engineering Job is HARD. Not because we’re not smart. But because most of us are stuck in a loop that looks like this: 🔁 Watch 10 hours of YouTube tutorials 🔁 Switch to a Udemy course halfway through 🔁 Jump to a Medium blog that says "Build this Data Pipeline in 15 mins" 🔁 Then try solving random LeetCode SQL problems 🗣️And finally, panic when an interview asks: 👉 “How would you design an SCD Type 2 pipeline on Snowflake with Airflow?” 👉 “Can you optimize a PySpark job for skewed data?” 👉 “Can you model a real-world payments system with changing requirements?” ❌ There’s no structured path ❌ No platform where you can learn, apply, and practice in one place. And no space that feels built for Data Engineers. That’s exactly what led me to build DataVidhya — A complete ecosystem for aspiring and practicing Data Engineers. 🧠 First, I created in-depth courses on: ✅ Python, SQL (job-focused, not academic) ✅ Data Warehouse with Snowflake ✅ Apache Spark with Databricks ✅ Kafka, Airflow, and 16+ real-world projects 📟 Then — I launched Code+, a platform where you can: ✅ Practice PySpark, dbt, SQL, Scala, and Python ✅ Solve real-world DE coding questions (not just "fizzbuzz") ✅ Build data models in an interactive playground ✅ Prep with mock interviews & resume reviews ✅ And upskill with tools that companies actually use No fluff. No skipping the hard parts. Just focused, job-ready learning and practice. 📌 If you’re tired of collecting resources and finally want a clear path into Data Engineering, this is for you. 🔗 Check out the link in the comments to know more about it 💬 And if you're preparing right now, let me know your biggest blocker — I might already have a solution for it. You will find everything below ⬇️ #dataengineering #dataengineer

𝐌𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐒𝐐𝐋: 𝐒𝐭𝐞𝐩-𝐛𝐲-𝐒𝐭𝐞𝐩 𝐆𝐮𝐢𝐝𝐞 Query optimization is a key skill for improving the performance of SQL queries, ensuring that your database runs efficiently. Here’s a step-by-step guide on how to optimize SQL queries, along with examples to illustrate each step: ↳ 𝐔𝐬𝐞 𝐈𝐧𝐝𝐞𝐱𝐞𝐬 𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐥𝐲: Indexing speeds up data retrieval. Identify columns frequently used in WHERE, JOIN, and ORDER BY clauses and create indexes accordingly. CREATE INDEX idx_column_name ON table_name (column_name); ↳ 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐉𝐨𝐢𝐧𝐬: Use appropriate join types (INNER JOIN, LEFT JOIN, etc.), and ensure indexes exist on join keys for better performance. SELECT a.column1, b.column2 FROM table_a a INNER JOIN table_b b ON a.id = b.a_id; ↳ 𝐀𝐯𝐨𝐢𝐝 𝐒𝐄𝐋𝐄𝐂𝐓: Select only required columns instead of SELECT * to reduce data retrieval time. SELECT column1, column2 FROM table_name; ↳ 𝐔𝐬𝐞 𝐖𝐇𝐄𝐑𝐄 𝐈𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐇𝐀𝐕𝐈𝐍𝐆: WHERE filters records before aggregation, while HAVING filters after, making WHERE more efficient in many cases. SELECT column1, COUNT(*) FROM table_name WHERE column2 = 'value' GROUP BY column1; ↳ 𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐂𝐚𝐜𝐡𝐢𝐧𝐠 𝐚𝐧𝐝 𝐌𝐚𝐭𝐞𝐫𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐕𝐢𝐞𝐰𝐬: Store precomputed results to improve performance for complex queries. CREATE MATERIALIZED VIEW view_name AS SELECT column1, column2 FROM table_name; ↳ 𝐏𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧 𝐋𝐚𝐫𝐠𝐞 𝐓𝐚𝐛𝐥𝐞𝐬: Partitioning helps break down large tables into smaller chunks, improving query performance. CREATE TABLE table_name ( id INT, column1 TEXT, created_at DATE ) PARTITION BY RANGE (created_at); ↳ 𝐔𝐬𝐞 𝐄𝐗𝐏𝐋𝐀𝐈𝐍 𝐏𝐋𝐀𝐍 𝐭𝐨 𝐀𝐧𝐚𝐥𝐲𝐳𝐞 𝐐𝐮𝐞𝐫𝐢𝐞𝐬: Identify bottlenecks and optimize queries accordingly. EXPLAIN ANALYZE SELECT column1 FROM table_name WHERE column2 = 'value'; ↳ 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬 𝐰𝐢𝐭𝐡 𝐂𝐓𝐄𝐬: Use Common Table Expressions (CTEs) instead of nested subqueries for better readability and performance. WITH CTE AS ( SELECT column1, column2 FROM table_name WHERE column3 = 'value' ) SELECT * FROM CTE; Do you have any additional tips for query optimization? Drop them in the comments! 👇 𝐆𝐞𝐭 𝐭𝐡𝐞 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐜𝐚𝐥𝐥: https://lnkd.in/ges-e-7J 𝐉𝐨𝐢𝐧 𝐦𝐞: https://lnkd.in/giE3e9yH p.s: If you found this helpful, follow for more #DataEngineering insights!

Data scientists, engineers, and analysts must be on discovery calls and in planning meetings. Excluding them until after promises have been made will end in tears. The reason is always the same, and it’s a weak excuse. “They don’t get the business. They say the wrong things at the wrong time. They get too technical, and the conversation ends up in the weeds.” Training technical people for these types of meetings isn’t rocket science. Here’s what works for me. For 2-3 calls, have the data scientist or engineer stay in the shadows for the entire call. They join but don’t participate. Have them note down each time they wanted to jump in and what they wanted to say. Have a meeting after each call to discuss their notes. I give them frameworks to decide when they should jump in and what needs to be communicated. Once they pick up the process, they join the next call and only jump in to ask questions. They DM everything else to me, and I can do some situational coaching. After a few more meetings, they get used to asking questions vs. making speeches. Their timing for clarifying gets better, and they learn to dial back explanations. The final stage is teaching them to translate technical language into business language. I talk with them after the meeting and rephrase some of the things they said in terms the people on the call relate to better. After a couple of months, they’re proficient and add a lot of value to these calls. It takes a bit of extra effort but avoids so many pitfalls that happen when technical people are excluded until the end. #datascience #analytics #consulting

Database Performance Cheat Sheet – What You Need to Know to Clear Your Next Interview If you've ever worked with databases, you've probably faced slow queries, high latency, or performance bottlenecks. Optimizing database performance is not just about using the right database. it's about implementing the right strategies. Here’s a structured breakdown What Impacts Database Performance? 1. Key Metrics – The essential factors to measure performance: - Query Execution Time – How long does a query take to run? - Throughput – How many queries per second can the system handle? - Latency – The delay between a request and response. - Resource Utilization – CPU, memory, and disk usage. 2. Workload Type – Different workloads create different challenges: Write-Heavy – Increased latency due to lock contention and index maintenance. - Read-Heavy – High latency for complex queries and cache misses. - Delete-Heavy – Fragmentation leads to performance degradation. - Competing Workloads – Real-time vs. batch processing can lead to resource contention. 3. Key Factors – Things that affect performance: - Item size, item type, dataset size - Concurrency & consistency expectations - Geographic distribution & workload variability 4. Database Indexing - Speeds up search queries by allowing faster lookups. - Helps in reducing the time complexity of data retrieval. - Be mindful – too many indexes can slow down writes. 5. Sharding & Partitioning - Distribute large databases across multiple servers (shards) to prevent overload. - Helps in scaling databases horizontally. - Works well for high-volume applications with large datasets. 6. Denormalization - Reduces the number of joins in complex queries. - Improves read performance at the cost of redundant data. - Used in analytics and reporting where performance matters more than strict normalization. 7. Database Replication - Keeps multiple copies of data across different nodes. - Leader-follower architecture improves read scalability. - Ensures availability in case of failures. 8. Database Locking Techniques Prevents race conditions in concurrent transactions. Ensures data consistency when multiple users modify records simultaneously. Implementing proper locking strategies reduces contention. 𝐅𝐨𝐫 𝐌𝐨𝐫𝐞 𝐃𝐞𝐯 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐉𝐨𝐢𝐧 𝐌𝐲 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 : Telegram - https://lnkd.in/d_PjD86B Whatsapp - https://lnkd.in/dvk8prj5 Happy learning !

I Reviewed 20+ Data Engineering Courses. These 3 Stood Out. While working on my own course, I explored a wide range of data engineering courses to understand what truly helps learners progress. Out of the many I reviewed, these three stood out the most: 🔹Associate Data Engineer in SQL: https://lnkd.in/dpGHQG3b This is one of the best introductory tracks I have come across for anyone looking to transition from data analyst to data engineer. It is project-based, beginner-friendly, and provides a solid foundation in core skills. 🔹Introduction to Apache Airflow in Python: https://lnkd.in/dSnB458S I took this course while working on a real-world assignment and found it incredibly useful. It helped me structure DAGs, manage dependencies, and automate workflows with clarity and confidence. 🔹Foundations of PySpark: https://lnkd.in/dCjqn2_i Distributed data processing is a key part of modern data engineering, and Spark is everywhere. This course offered a clear and accessible path into PySpark, breaking down the complexity in a way that was easy to apply. What I appreciated most about these courses is that they go beyond theory. They are hands-on, practical, and focused on doing the work, something I find essential in technical learning. If you are upskilling in data engineering, I highly recommend starting with these. I would also love to hear what courses you have taken and found valuable. #dataengineer #technology #sql #python #programming