How to be a great ETL developer in the cloud era

🔹 ETL vs ELT – How They Work in Modern Data Engineering 🔹 As data grows in volume and complexity, two common approaches power data pipelines: ETL and ELT. ⚙️ ETL (Extract → Transform → Load) Data is extracted from sources Transformed into a clean, usable format Then loaded into a warehouse for reporting/analytics ✅ Best for structured data and when heavy transformations are needed before loading. ⚡ ELT (Extract → Load → Transform) Data is extracted Loaded directly into the cloud data warehouse (Snowflake, Databricks, BigQuery, Redshift) Transformed inside the warehouse using its compute power ✅ Best for big data, scalability, and near real-time analytics. 💡 Key takeaway: ETL = pre-warehouse processing (control + structure) ELT = cloud-first processing (speed + flexibility) Both have their place, and choosing the right one depends on the business need. I have hands-on experience designing ETL & ELT pipelines using SQL, Python, Spark, Snowflake, Databricks, and AWS/Azure services — ensuring data is accurate, scalable, and ready for insights. 👉 Open to opportunities where I can apply these skills to deliver impactful data solutions. #ETL #ELT #DataEngineer #SQL #Spark #Snowflake #Databricks #AWS #Azure #BigData #DataPipelines

Data engineer project: AWS-based ETL pipeline for YouTube analytics data processing using AWS Glue, Lambda, S3, Athena and Quicksight. 💡 Technical Highlights: - Serverless architecture using AWS Glue, Lambda, and S3 - Hive-style partitioning strategy for optimized regional queries - Predicate pushdown reducing data scan costs by filtering at source - Event-driven Lambda triggers for real-time JSON-to-Parquet transformation - Centralized metadata management via Glue Data Catalog 📊 Pipeline Architecture: 1️⃣ Ingestion → Raw data uploaded to S3 via API (CSV + JSON) 2️⃣ Cataloging → Automated schema discovery with Glue Crawler 3️⃣ Transformation → PySpark jobs convert CSV/JSON → Parquet (Cleansed Layer) 4️⃣ Refinement → Business logic applied, filtered datasets stored (Refined Layer) 5️⃣ Analytics → Athena for SQL queries + QuickSight for interactive dashboards 🔗 GitHub repo with full code in comments #DataEngineering #AWS #ETL #BigData #CloudComputing Thanks, Darshil Parmar, for your lessons!

🚀 𝗪𝗮𝗻𝘁 𝘁𝗼 𝗴𝗿𝗼𝘄 𝗳𝗮𝘀𝘁𝗲𝗿 𝗮𝘀 𝗮 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿? It all starts with mastering the 5 pillars that top companies look for 👇 1️⃣ 𝘿𝙖𝙩𝙖 𝘾𝙤𝙡𝙡𝙚𝙘𝙩𝙞𝙤𝙣 & 𝙎𝙩𝙤𝙧𝙖𝙜𝙚 APIs | Databases | Data Lakes | Warehouses | Lakehouse 🔹 Tools: Snowflake, Redshift, BigQuery, S3, ADLS, Databricks 2️⃣ 𝘿𝙖𝙩𝙖 𝙋𝙧𝙤𝙘𝙚𝙨𝙨𝙞𝙣𝙜 & 𝙏𝙧𝙖𝙣𝙨𝙛𝙤𝙧𝙢𝙖𝙩𝙞𝙤𝙣 ETL/ELT | Cleaning | Normalization | Batch vs. Streaming 🔹 Tools: Spark, SQL, dbt 3️⃣ 𝘽𝙞𝙜 𝘿𝙖𝙩𝙖 & 𝙄𝙣𝙛𝙧𝙖𝙨𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚 Distributed systems | Real-time streaming | Containers 🔹 Tools: Hadoop, Kafka, Flink, Docker, Kubernetes 4️⃣ 𝘿𝙖𝙩𝙖 𝙈𝙤𝙙𝙚𝙡𝙞𝙣𝙜 & 𝘼𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚 Design scalable data systems that tell a story 🔹 Concepts: Star schema, Snowflake schema, lineage, governance 5️⃣ 𝘿𝙖𝙩𝙖 𝙞𝙣 𝙋𝙧𝙤𝙙𝙪𝙘𝙩𝙞𝙤𝙣 (𝙩𝙝𝙚 𝙧𝙚𝙖𝙡 𝙩𝙚𝙨𝙩!) Orchestration | CI/CD | Monitoring | Security 🔹 Tools: Airflow, Prefect, Git, Prometheus 💡 𝗣𝗿𝗼 𝗧𝗶𝗽: Tools change. Fundamentals don’t. Learn how to make these systems work together - that’s what separates good engineers from great ones. 📍 𝗕𝗼𝗻𝘂𝘀: A Databricks or Snowflake certification can add credibility to your profile. 💬 Which of these 5 areas are you focusing on right now? Let’s discuss in the comments! Follow SATISH GOJARATE for more... 🔥 #DataEngineering #BigData #Databricks #Snowflake #ApacheSpark #DataPipelines #Airflow #ETL #MachineLearning #CareerGrowth #DataEngineer

Data Engineering isn’t just about tools — it’s about mastering the stack from the ground up. To build scalable, production-grade data solutions, a Data Engineer needs to grow through different skill layers: Problem Solving & Business Understanding – Asking the right questions is the foundation. Tech Stacks & Distributed Processing – Spark, Kafka, Databricks, and cloud-native streaming. Cloud, DevOps & Visualization – AWS, Azure, GCP, Git, CI/CD, Tableau, Power BI. ETL/ELT & Data Architecture – Data Lakes, Warehouses, Pipelines, Orchestration. Programming & SQL – Python, PySpark, advanced SQL — the core engine. A solid data engineer isn’t defined by a single tool, but by layered expertise that turns raw data into actionable intelligence. #DataEngineering #ETL #ELT #AWS #Azure #GCP #Python #SQL #Spark #Kafka #Databricks #DevOps #Tableau #PowerBI #DataArchitecture #CloudComputing #DataAnalytics #BigData #DataEngineer #C2C #SeniorDataEngineer

🚀 Designing Next-Gen ETL Pipelines with Apache Spark Most teams build ETL pipelines… But only a few optimize them for speed, scalability, and reliability. ⚙️ After working across multiple data platforms — AWS, Azure, and GCP — I’ve learned that building a high-performance ETL architecture means focusing on four pillars: ✅ Extract smartly — Read efficiently using Parquet, pushdown filters, and parallelism. ⚡ Transform efficiently — Use Spark SQL functions, handle nulls early, and cache reusable data. 💾 Load optimally — Write analytics-ready data (Parquet/Delta/Hudi) with schema validation and partitioning. 📊 Monitor continuously — Automate with Airflow, track metrics via CloudWatch, and validate data quality using dbt or Great Expectations. Whether you’re working with batch or streaming, these optimization patterns can drastically reduce latency, costs, and pipeline complexity. 🔗 I’ve summarized it all in a visual 7-slide carousel — from extraction to automation — with best practices that scale across multi-cloud environments. 👇 Check it out & share: 💬 What’s one Spark optimization trick that’s saved you the most runtime? #DataEngineering #ApacheSpark #ETL #PySpark #AWS #PerformanceTuning #BigData #Glue #Airflow #Redshift #Optimization #Databricks #SparkSQL

Drop external ETL components for the simplicity and speed of in-database transformation with OpenText Vertica. Here’s why: - SQL-first workflow: Every transformation is written in SQL—easy to review, test, and reproduce. No hidden logic buried in Python scripts or third-party tools. It’s transparent, auditable, and version-controllable. - Direct on data: Vertica queries data directly from internal tables or external sources like Parquet, ORC, and JSON—no need to move data around. ELT happens where the data lives, reducing latency and duplication. - Built for scale: Vertica handles massive joins, filters, and aggregations with ease. Its MPP architecture and columnar storage mean you can process billions of rows without worrying about memory crashes or bottlenecks. - Simplified architecture: One engine replaces Spark, Airflow, pandas, and temp databases. With Vertica, you get ingestion, transformation, and analytics in one platform—no orchestration overhead or fragile pipelines. Talk to us today about how our existing customers get simplicity, speed, and savings by standardizing their data platform with OpenText Vertica.

Azure Data Factory Data Flows: When to Use Them (And When NOT To) Are you optimizing your ETL/ELT pipelines in Azure Data Factory (ADF)? The Mapping Data Flow activity is a game-changer for data transformation, but like any powerful tool, it comes with trade-offs. Knowing when to use it is key to managing performance and cost. Here's a breakdown of the major pros and cons: ✅ The PROS: The Power of Low-Code Spark Code-Free Transformation: You can build incredibly complex ETL logic using a simple, visual drag-and-drop interface. No need to write or manage complicated Spark, Scala, or Python code. Massive Scale: Data Flows run on automatically managed Apache Spark clusters. This makes them highly effective and scalable for processing big data workloads (terabytes of data). Built-in Schema Drift: Your pipeline can automatically detect and adapt to changes in your source file or table schema (like new, missing, or changed columns) without failing. Interactive Debugging: The Debug feature allows you to preview data at every step, making development and troubleshooting faster and much less painful. ❌ The CONS: Latency and Cost Considerations Initial Cluster Spin-up Time: This is the most common complaint. The underlying Spark cluster takes a few minutes to start up for each run, introducing significant latency. This makes Data Flows unsuitable for low-latency or very small, frequent micro-batches. Cost: Data Flows are generally more expensive than simply using the Copy Activity or performing transformations natively within a database (ELT). You are paying for the dedicated Spark compute. Complexity for Simple Tasks: If your transformation can be done with a single SQL statement (e.g., a simple MERGE or UPDATE), using a Data Flow often adds unnecessary overhead and cost due to the spin-up time. ✨ Verdict: Use ADF Data Flows when your job is a large, complex batch transformation where developer productivity and schema agility outweigh the initial startup time. For simple data movement or high-frequency/low-latency loads, stick to the Copy Activity or Stored Procedures. What's your go-to transformation tool in Azure? Drop a comment! #AzureDataFactory #ADF #DataEngineering #ETL #ELT #MicrosoftAzure #BigData

ETL Made Simple — Powering the Modern Data Stack In every data-driven organization, ETL (Extract, Transform, Load) is the foundation that ensures data flows seamlessly from raw sources to analytics-ready systems. Let’s break down how it really works 👇 🔹 Extract (E) → Retrieve data from diverse and distributed sources such as SQL/NoSQL databases, APIs, CSVs, flat files, log streams, or cloud storages (AWS S3, GCS, Azure Blob). A robust extraction layer focuses on incremental pulls, change data capture (CDC), and data validation to ensure minimal latency and zero data loss. 🔹 Transform (T) → Once extracted, data undergoes cleansing, enrichment, standardization, and schema mapping. Typical transformations include: - Data type conversions - Deduplication and null handling - Business rule applications - Aggregations and joins across sources - Calculated fields and surrogate keys Tools: Python (Pandas, PySpark), dbt, Airflow, Spark SQL, Kafka Streams 🔹 Load (L) → Transformed data is loaded into target systems — Data Warehouse (Snowflake, BigQuery, Redshift), Data Lake (Delta Lake, Lakehouse architecture), or downstream analytics layers. Modern architectures often adopt ELT, where transformation happens post-load inside the warehouse for scalability and compute optimization. ⚙️ A well-orchestrated ETL pipeline ensures: ✔️ Data consistency & integrity ✔️ Automated scheduling & monitoring (via Airflow, Prefect, or Dagster) ✔️ Version-controlled transformations ✔️ Scalable and maintainable architecture ETL is not just a process — it’s the engine that powers reliable data insights, ML pipelines, and BI dashboards. Anoop Rawat, Kuldeep Singh, Amit Kumar, Sandeep Kumar, Utsav Gautam, Punya Mittal, Tushar Panchal, Mohit Bailwal, Priyanshu Sharma, vidhi gautam, Simran Sharma and Preeti Sharma #ETL #ELT #DataEngineering #DataPipeline #BigData #DataAnalytics #DataIntegration #DataTransformation #DataWarehouse #Snowflake #BigQuery #Redshift #ApacheAirflow #ApacheSpark #dbt #Kafka #DataLake #DeltaLake #ETLPipeline #DataOps #AnalyticsEngineering #CloudData #ModernDataStack #Python #SQL #Automation #TechLearning #DataScience #ETLProcess

Azure Data Factory Data Flows: When to Use Them (And When NOT To) Are you optimizing your ETL/ELT pipelines in Azure Data Factory (ADF)? The Mapping Data Flow activity is a game-changer for data transformation, but like any powerful tool, it comes with trade-offs. Knowing when to use it is key to managing performance and cost. Here's a breakdown of the major pros and cons: ✅ The PROS: The Power of Low-Code Spark Code-Free Transformation: You can build incredibly complex ETL logic using a simple, visual drag-and-drop interface. No need to write or manage complicated Spark, Scala, or Python code. Massive Scale: Data Flows run on automatically managed Apache Spark clusters. This makes them highly effective and scalable for processing big data workloads (terabytes of data). Built-in Schema Drift: Your pipeline can automatically detect and adapt to changes in your source file or table schema (like new, missing, or changed columns) without failing. Interactive Debugging: The Debug feature allows you to preview data at every step, making development and troubleshooting faster and much less painful. ❌ The CONS: Latency and Cost Considerations Initial Cluster Spin-up Time: This is the most common complaint. The underlying Spark cluster takes a few minutes to start up for each run, introducing significant latency. This makes Data Flows unsuitable for low-latency or very small, frequent micro-batches. Cost: Data Flows are generally more expensive than simply using the Copy Activity or performing transformations natively within a database (ELT). You are paying for the dedicated Spark compute. Complexity for Simple Tasks: If your transformation can be done with a single SQL statement (e.g., a simple MERGE or UPDATE), using a Data Flow often adds unnecessary overhead and cost due to the spin-up time. ✨ Verdict: Use ADF Data Flows when your job is a large, complex batch transformation where developer productivity and schema agility outweigh the initial startup time. For simple data movement or high-frequency/low-latency loads, stick to the Copy Activity or Stored Procedures. #AzureDataFactory #ADF #DataEngineering #ETL #ELT #MicrosoftAzure #BigData

🚀 Why I Stopped Writing ETL Code and Started Designing ELT Systems: Data engineering has evolved beyond nightly ETL scripts. Today’s architectures, especially across Snowflake, BigQuery, and Databricks demand a shift in how we think about data transformation. I’ve learned that the real power lies in ELT, not ETL. Here’s why: 🔸 1. Compute Has Moved Downstream:  Modern warehouses are no longer just storage — they’re processing engines. Why extract and transform elsewhere when Snowflake or BigQuery can handle transformations at scale with optimized compute? 🔸 2. Simplified Pipelines:  By pushing transformations downstream, data pipelines become thinner and easier to monitor. The orchestration layer (Airflow, ADF, or Composer) handles movement — while dbt handles business logic declaratively. 🔸 3. Version Control & Testing:  SQL models under Git + dbt = real CI/CD for analytics. Every transformation is tested, versioned, and documented automatically. 🔸 4. Cost and Transparency:  No hidden Spark clusters, no opaque jobs. Every transformation is visible and cost-attributed at the warehouse level. We’re not just moving data anymore — we’re designing data products.  If you’re still writing “ETL jobs” the old way, you’re missing the scalability and observability modern ELT gives. hashtag#DataEngineering hashtag#ELT hashtag#Snowflake hashtag#DBT hashtag#Airflow hashtag#Databricks hashtag#SQL hashtag#CloudData hashtag#AnalyticsEngineering