Sumit Gupta

Sumit GuptaSep 15, 2026

Snowflake skills can open seven different career paths. Learning SQL is only the beginning. Modern Snowflake roles require expertise across data engineering, architecture, administration, security, Snowpark, governance, and production machine learning. Here are 7 certifications that can strengthen your data engineering profile: 1. SnowPro Core, COF-C03 Build your foundation in architecture, warehouses, data loading, transformation, connectivity, performance, and protection. https://lnkd.in/geaexF_6 2. Advanced Data Engineer, DEA-C02 Validate ingestion, replication, streaming, transformation, scalable compute, and optimization skills. https://lnkd.in/gNu_SbRU 3. Advanced Architect, ARA-C01 Learn to design secure data flows, integrations, sharing patterns, and end-to-end architectures. https://lnkd.in/d8GiQ5Wf 4. Specialty Snowpark, SPS-C01 Cover DataFrames, sessions, stored procedures, transformations, and Python-based processing. https://lnkd.in/dQxAbFEP 5. Advanced Administrator, ADA-C02 Validate administration, governance, monitoring, optimization, replication, and disaster recovery. https://lnkd.in/d-JCnk6X 6. Advanced Security Engineer, SEA-C01 Develop expertise in IAM, compliance, governance, monitoring, risk, and incident response. https://lnkd.in/dcBvsHPz 7. Advanced MLOps Engineer, MLA-C01 Cover feature engineering, deployment, monitoring, CI/CD, orchestration, and ML governance. https://lnkd.in/d8CVbzAs Start with SnowPro Core, then choose the path aligned with the work you want to own. Which certification are you preparing for? Follow Sumit Gupta 📊 for more such insights!!

Sumit GuptaSep 16, 2026

𝗗𝗮𝘆 𝟭𝟵/𝟯𝟬: One Data Engineering Concept Every Day 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 Running one job is easy. Running 100 jobs in the right order every day is a different problem. That is workflow orchestration. It coordinates: → What should run → When it should run → What it depends on → What happens if it fails Imagine this: Extract Orders ↓ Clean Orders ↓ Build Sales Table ↓ Run Tests ↓ Refresh Dashboard If the first step fails, the dashboard should not refresh with incomplete data. That is exactly why orchestration matters. 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗲𝘅𝗮𝗺𝗽𝗹𝗲: Your daily revenue dashboard needs: Customer Data ✓ Orders Data ✓ Payments Data ✓ If payments are missing, downstream jobs should wait. An orchestrator manages that dependency automatically. Common tools include Airflow, Dagster, and Prefect. 𝗙𝗿𝗲𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: → Airflow https://lnkd.in/gMpyr_jz → Dagster https://docs.dagster.io/ 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝘄𝗵𝗮𝘁 𝘁𝘂𝗿𝗻𝘀 𝗮 𝗯𝘂𝗻𝗰𝗵 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗷𝗼𝗯𝘀 𝗶𝗻𝘁𝗼 𝗼𝗻𝗲 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄. Tomorrow: 𝗗𝗮𝘆 𝟮𝟬/𝟯𝟬 → 𝗖𝗵𝗮𝗻𝗴𝗲 𝗗𝗮𝘁𝗮 𝗖𝗮𝗽𝘁𝘂𝗿𝗲 Keep learning, keep building, and keep growing. Tag #DataBySumit30D to follow along and learn Data Engineering one concept at a time :)

Sumit GuptaSep 17, 2026

RAG is not “upload a PDF and ask questions.” A reliable RAG system is an end-to-end pipeline that turns scattered enterprise data into grounded, context-aware answers. Here is how it works: → 1. Data Ingestion Collect content from files, PDFs, websites, APIs, databases, and connectors. Capture the raw document, source, permissions, and metadata before moving it into landing storage. → 2. Data Preprocessing Parse the content, remove noise, deduplicate records, normalize formats, and split the cleaned text into retrieval-ready chunks with useful metadata. → 3. Embedding Generation Send text chunks through an embedding model to convert semantic meaning into dense vectors. Validate the output before indexing it. → 4. Indexing Store vectors and metadata in a vector database such as FAISS, Pinecone, Weaviate, or Qdrant. Build and refresh the index so content stays searchable. → 5. Retrieval Transform the user query into searchable form, generate its embedding, run similarity search, apply metadata filters, rerank candidates, and select the strongest top-k chunks. → 6. Generation Combine the user query with the retrieved context. The LLM creates a draft, while citation and safety checks help verify the final grounded response. Every stage affects answer quality. Poor parsing creates weak chunks. Weak chunks create poor embeddings. Poor retrieval gives the model irrelevant context. And even a strong model cannot reliably recover from missing evidence. RAG quality is rarely only a model problem. It is a pipeline quality problem. Which stage causes the biggest challenge in your RAG system? Follow Sumit Gupta 📊 for more such insights!!