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Abhisek Sahu | Cloud • Data • AI • Career | India → Netherlands

@dataelevate_engineer

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🧑‍💻 Data | Amsterdam 🇳🇱
🌍 Tech & Career (India & Abroad)
📊 Free Tech content
🤝 180K LinkedIn | 400K+ community
😊 Personal: @indiandutchlife

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Comment “PROJECTS” and I’ll send you all 20 Data Engineering project links straight to your DM.

From batch ETL and SQL warehouses to Kafka streaming, Airflow pipelines, Databricks lakehouses, CDC, data quality, and production-ready architectures, these projects can help you learn Data Engineering by actually building real systems.

[Data Engineering, ETL, Apache Kafka, Airflow, Databricks, Data Pipelines]

#DataEngineering #DataEngineer #ApacheKafka #Airflow #Databricks by @dataelevate_engineer

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14 hours ago

Want to master Data Engineering in 2026-27?

Build projects, not just notes.

The fastest way to understand pipelines, streaming, warehouses, orchestration, lakehouses, and data quality is to build complete systems end to end.

Comment '20' to get all the links straight to your dm

Here are 20 hands-on projects worth exploring:

1. Batch Pipeline
2. API PostgreSQL ETL
3. SQL Data Warehouse
4. NYC Taxi Pipeline:
5. Real-Time Data Pipeline
6. Stock Market Streaming
7. Reddit Data Pipeline
8. YouTube Data Pipeline
9. Spotify ETL Pipeline
10. E-Commerce Streaming Pipeline
11. Real-Time Voting Pipeline
12. Data Quality Pipeline
13. Airline Data Platform
14. Databricks Lakehouse Project
15. Iceberg Lakehouse Pipeline
16. CDC Warehouse Pipeline
17. Train Streaming Pipeline
18. Boardgame Analytics Pipeline
19. Production Incremental Pipeline
20. Real-Time Review Streaming

These projects cover Python, SQL, Kafka, Spark, Airflow, dbt, PostgreSQL, Snowflake, Databricks, Iceberg, Docker, Terraform, CDC, data quality, and cloud infrastructure.

The goal is not to complete all 20.

Pick 3-5, understand every architectural decision, deploy them, document the trade-offs, and make them portfolio-ready.

Which project should I break down next with architecture, tools, and a step-by-step build plan? taken in USA by @dataelevate_engineer

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Comment “LINK” and I’ll send you all the learning resources straight to your DM.

[Data Engineering, Data Analytics, Data Science, AI Engineering, ML Engineering, LLM Engineering]

#DataEngineering #DataAnalytics #AIEngineering #MachineLearning #LLMEngineering by @dataelevate_engineer

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2 days ago

Thinking about building your career abroad in 2026–27?

From Switzerland and the USA to the UAE, Germany, Singapore, Ireland, the Netherlands, Denmark, and Canada, each country offers a different advantage, whether it’s higher salaries, stronger tech opportunities, better work-life balance, or quality of life.

Choose based on your career goals, industry, visa options, and long-term lifestyle priorities.

[Global Careers, Tech Jobs, Work Abroad, Career Growth, International Jobs]

#GlobalCareers #TechJobs #WorkAbroad #CareerGrowth #InternationalJobs by @dataelevate_engineer

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📊 DATA ANALYST BEGINNER → ADVANCED ROADMAP 🚀

🎓 FREE RESOURCES + FREE CERTIFICATES INCLUDED!

💬 Want all the course links + free certificate resources? Comment “DATA” below and I’ll share the complete list.

Want to become a Data Analyst from scratch? Save this roadmap and follow it step by step! 👇

1️⃣ Data Analytics Fundamentals
Learn data types, KPIs, data cleaning, visualization, analytics processes, and business questions.

2️⃣ Excel for Data Analysis
Master formulas, XLOOKUP, PivotTables, charts, conditional formatting, and dashboards.

3️⃣ SQL for Data Analysis
Learn SELECT, WHERE, GROUP BY, JOINs, subqueries, CTEs, and aggregations.

4️⃣ Statistics for Data Analysis
Understand mean, median, probability, distributions, correlation, hypothesis testing, and regression.

5️⃣ Data Cleaning
Learn how to handle missing values, duplicates, inconsistent data, dates, scaling, and normalization.

6️⃣ Python for Data Analysis 🐍
Build your Python foundation with variables, functions, loops, lists, dictionaries, and data processing.

7️⃣ Pandas
Learn DataFrames, filtering, GroupBy, missing values, merging, and data aggregation.

8️⃣ Data Visualization 📈
Master charts, distributions, trends, patterns, chart selection, and data storytelling.

9️⃣ Power BI
Learn Power Query, data modeling, DAX, reports, interactive dashboards, and business intelligence.

🔟 Build Real Data Analytics Projects 💼
Create portfolio projects in sales analytics, customer churn, marketing analytics, financial dashboards, and e-commerce analytics.

These resources can help you build practical data analyst skills without spending money on courses.

🔖 Save this post and start learning today.

#DataAnalyst #DataAnalytics #DataAnalysis #DataAnalystRoadmap #DataScience SQL Excel Python Pandas PowerBI Statistics DataVisualization BusinessIntelligence Analytics LearnDataAnalytics DataAnalystSkills FreeCourses FreeCertificates CareerInData TechSkills by @dataelevate_engineer

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AI is becoming the data engineer’s second keyboard.

It can write SQL, debug pipelines, generate tests, explain Spark jobs, trace failures, and automate repetitive workflows. But using one chatbot for everything barely scratches the surface.

The real advantage comes from matching each AI capability to the right engineering task.

At the beginner level:

→ ChatGPT helps explain concepts, draft queries, and explore solutions
→ Claude supports deeper reasoning, debugging, and technical analysis

At the intermediate level:

→ GitHub Copilot accelerates SQL and Python development
→ Cursor supports pipeline development across a complete codebase
→ Databricks Assistant helps with Spark, SQL, and notebooks
→ Gemini in BigQuery assists with SQL analytics
→ Snowflake Cortex brings AI closer to enterprise data workflows
→ dbt Copilot supports transformation and analytics engineering work

At the advanced level:

→ Claude Code and Codex handle larger engineering tasks
→ MCP connects AI with databases, catalogs, and development tools
→ Airflow, Dagster, and n8n add intelligence to orchestration and automation
→ LangChain and LlamaIndex provide context for data applications
→ Great Expectations combines testing with AI-assisted data quality
→ OpenLineage strengthens lineage analysis and pipeline debugging
→ RAG improves metadata search and data discovery
→ AI agents can plan, execute, validate, and recover pipeline workflows

The goal is not to use every tool. It is to select the right level of assistance while keeping testing, security, observability, and human review in place.

AI should strengthen engineering judgment, not replace it.

Which AI-powered data engineering workflow would you add?

#ai #genai #agenticai #dataengineering #usa taken in USA by @dataelevate_engineer

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Comment “DATA” and I’ll send you all 20 GitHub repositories straight to your DM.

[Data Engineering, Apache Spark, Data Pipelines, System Design, GitHub, Data Engineer]

#DataEngineering #DataEngineer #ApacheSpark #DataPipelines #GitHub by @dataelevate_engineer

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4 days ago

Azure, GCP and AWS Tech Stack for Data Engineers

Like this post ❤️
Comment “CLOUD” and I’ll send you the free AWS, Azure, and GCP Data Engineering roadmap.

Modern Data Engineering runs on the cloud.

Data Engineers use cloud tools to store data, build pipelines, process large datasets, run analytics, handle real-time events, and monitor systems.

AWS, Azure, and GCP offer similar layers with different tools.

Start with one cloud and learn the complete flow:

Store → Move → Process → Warehouse → Report → Monitor

Save this guide for later.

The page @dataelevate_engineer is here to help professionals build in-demand tech skills and grow their careers 🚀

Practical tutorials, resources & opportunities across:

💻 SQL & Python
📊 Data Analysis
⚙️ Data Engineering
🧠 Data Science
🤖 ML Engineering
☁️ Azure & Cloud
🔷 Databricks
✨ AI & Machine Learning
🌍 Tech Careers & Jobs Abroad

Free resources. Practical learning. Real career opportunities.

Learn. Build. Grow. 🤝🚀

Follow @dataelevate_engineer, keep building your tech career, and do share this page with your friends and colleagues who are looking to grow their skills too! 😇🚀 taken in New York by @dataelevate_engineer

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4 days ago

Comment “LINK” and I’ll send you all 20 GitHub repositories straight to your DM.

From Python, SQL, Excel, Pandas, and Power BI to portfolio projects, interview prep, roadmaps, storytelling, and real analytics practice, these repositories can help you build stronger Data Analytics skills step by step.

[Data Analytics, SQL, Python, Power BI, Excel, GitHub]

#DataAnalytics #SQL #Python #PowerBI #DataAnalyst by @dataelevate_engineer

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5 days ago

Comment 'free' , i will send all links straight to your dm !

The Ultimate Free Learning Resources for Data Analyst & Data Engineers 🧑‍💻

The page @dataelevate_engineer is here to help professionals build in demand tech skills and grow their careers 🚀

Practical tutorials, resources & opportunities across:

💻 SQL & Python
📊 Data Analysis
⚙️ Data Engineering
🧠 Data Science
🤖 ML Engineering
☁️ Azure & Cloud
🔷 Databricks
✨ AI & Machine Learning
🌍 Tech Careers & Jobs Abroad especially in Europe & the UK

Free resources. Practical learning. Real career opportunities.

Learn. Build. Grow. 🤝🚀

Follow @dataelevate_engineer and keep building your tech career! 😇 taken in USA by @dataelevate_engineer

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10 ETL Patterns Used in Production

ETL is not limited to Extract → Transform → Load.

In production data engineering, different patterns are used depending on data volume, frequency, latency, scalability, and business requirements.

1. Full Load

The entire dataset is loaded every time. This approach is simple and works well for smaller tables or reference data.

2. Incremental Load

Only new or modified records are loaded instead of processing the complete dataset. This reduces processing time and cost.

3. Change Data Capture (CDC)

CDC captures changes such as inserts, updates, and deletes from the source system and transfers only those changes to the target.

4. Batch Processing

Data is processed in scheduled batches, such as hourly, daily, or weekly. This is commonly used for reporting and periodic data processing.

5. Streaming ETL

Data is processed continuously as it arrives. It is useful when applications require real-time or near-real-time data processing.

6. Lambda Architecture

This combines batch and streaming processing to support both accurate historical processing and low-latency results.

7. ELT (Extract → Load → Transform)

Instead of transforming data before loading it, raw data is loaded first and transformed within the data warehouse or lakehouse.

8. Data Vault

A modeling and loading approach designed for scalability, auditability, and maintaining historical data over time.

9. Slowly Changing Dimensions (SCD)

Used to manage changes in dimensional data while maintaining history. For example, SCD Type 2 keeps previous versions of a record.

10. Fan-Out / Fan-In

Fan-out distributes data from one source to multiple downstream pipelines, while fan-in combines data from multiple sources into a single processing flow.

The key idea is that production ETL is not simply about moving data from one system to another.

It is about designing pipelines that are scalable, reliable, efficient, and capable of handling both historical and real time data.

#etl #datawarehouse #datalake #databricks #cloudcomputing by @dataelevate_engineer

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Comment “LINKS” for the ULTIMATE FREE Data Analytics + Data Engineering Cheat Sheet resources 🚀

SQL • Excel • Python • Pandas • NumPy • Statistics • Data Cleaning • Data Visualization • Power BI • Tableau • Looker Studio • Git • GitHub • Linux • Bash • PostgreSQL • MySQL • Data Modeling • ETL • ELT • Data Warehousing • Data Lakes • dbt • Airflow • Dagster • Apache Spark • PySpark • Kafka • Flink • Hadoop • Hive • Trino • Presto • Parquet • Avro • Iceberg • Delta Lake • Snowflake • BigQuery • Redshift • Databricks • AWS • Azure • GCP • Docker • Kubernetes • Terraform • CI/CD • APIs • REST • Data Quality • Data Governance • Medallion Architecture • Machine Learning Basics • Generative AI • LLMs

All resources. One cheat sheet. 100% FREE.

#DataAnalytics #DataEngineering #SQL #Python #PowerBI Tableau ApacheSpark Kafka Airflow dbt Snowflake Databricks AWS Azure GCP BigQuery DataScience by @dataelevate_engineer

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