#Extract Transform Load Data Warehousing

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#Extract Transform Load Data Warehousing Reel by @b2a_tech - ETL vs ELT vs EtLT Ever wondered what's the real difference between ETL, ELT, and the newer EtLT? This image explains it perfectly - with oranges 🍊
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@b2a_tech
ETL vs ELT vs EtLT Ever wondered what’s the real difference between ETL, ELT, and the newer EtLT? This image explains it perfectly — with oranges 🍊 as our data! ▶️ ETL (Extract → Transform → Load) You extract the data, clean/transform it first (like juicing the oranges), and then load it into the data warehouse. ➡️ Best for: traditional systems like on-prem databases. ▶️ ELT (Extract → Load → Transform) You extract and load raw data into a modern warehouse (like BigQuery, Snowflake, etc.), and then transform it inside the warehouse. ➡️ Best for: cloud-based, large-scale analytics. ▶️ EtLT (Extract → transform → Load → Transform) A hybrid approach — apply a light transformation first for basic cleaning (like filtering bad oranges 🍊), load into the warehouse, and then perform deeper transformations later. ➡️ Best for: modern data pipelines that need both speed and flexibility. 💡 The order may change — but the goal remains the same — turn raw data into something valuable and insightful! #datascientist #powerbi #dataanalytics #datascience #etl #dataanalysis #data #businessinteligence
#Extract Transform Load Data Warehousing Reel by @theengineerguy_ (verified account) - Let's break down the steps for creating your data engineering project-a simple ETL (Extract, Transform, Load) pipeline-from scratch. 🚀 1. Choose a D
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@theengineerguy_
Let’s break down the steps for creating your data engineering project—a simple ETL (Extract, Transform, Load) pipeline—from scratch. 🚀 1. Choose a Data Source (API): - Visit the [RapidAPI website](https://rapidapi.com/) and explore the available REST APIs. - Select an API that interests you. Make sure it provides data in JSON format. 2. Extract (E): - Use Python to fetch data from the chosen API. - You can use libraries like requests or http.client to make API requests. - Extract the data in JSON format. 3. Transform (T): - Clean and preprocess the data: - Handle missing values. - Convert data types if needed (e.g., dates to datetime objects). - Remove duplicates. - Perform any necessary transformations: - Aggregations (e.g., sum, average). - Joins (if you have multiple data sources). - Apply business logic specific to your project. 4. Load (L): - Set up a MySQL database (or any other relational database of your choice). - Use Python libraries like mysql-connector or SQLAlchemy to connect to the database. - Create a table to store your data. - Insert the transformed data into the table. 5. Documentation and GitHub: - Document each step thoroughly: - Explain your approach. - Include code snippets. - Describe any challenges faced and how you overcame them. - Create a GitHub repository for your project: - Upload your Python script(s). - Write a README with instructions on how to run your pipeline. - Share the repository link on your resume. 6. Celebrate! 🎉 - You’ve built your first ETL pipeline! - Showcase your project to potential employers or colleagues. Save it for future ✅ Comment “Data Engineering Project” I’ll share all details in your DM ♥️🤝 Remember, practice makes perfect! Keep exploring new APIs, databases, and tools to enhance your skills. Happy coding! 👩‍💻 #kaish #theengineerguy #DataEngineering #ETL #Python #project #pipeline #bigdata #bigdataanalytics
#Extract Transform Load Data Warehousing Reel by @muskan.khannaa - I found an ETL (Extract, Transform,Load) testing project at Accenture itself to get something on my profile on data. Then I found an ETL development p
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@muskan.khannaa
I found an ETL (Extract, Transform,Load) testing project at Accenture itself to get something on my profile on data. Then I found an ETL development project. It helped me get the actual first flavour of data. . . . . . . Data engineer, Accenture, career transition, testing to data engineering, ETL, how to switch to data engineering, how to switch to data #data #dataengineering #techcareers #womeninstem
#Extract Transform Load Data Warehousing Reel by @azure_data_engineer - I have used the below for processing data: - Informatica ETL Tool - Informatica Cloud - Shell Scripting - Alteryx - PySpark - Presto - Python - PL/SQ
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@azure_data_engineer
I have used the below for processing data: - Informatica ETL Tool - Informatica Cloud - Shell Scripting - Alteryx - PySpark - Presto - Python - PL/SQL - SQL - R One thing that really stood out for me is Processing data using Spark! All other processes can be easily grasped and learnt As you are dealing with just one machine When it comes to Spark, You deal with the entire cluster For processing your data So, as such there are many things that come into play You gotta think about how you can best utilize the entire cluster So that the data is processed in parallel and thus in a timely manner! Your thought process of designing pipelines Thus change with spark. Let's see how ETL is done using PySpark. 👉 PySpark provides APIs in Python for working with big data Let's see a 𝐛𝐚𝐬𝐢𝐜 𝐄𝐓𝐋 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐮𝐬𝐢𝐧𝐠 𝐏𝐲𝐒𝐩𝐚𝐫𝐤: 🖊 𝐄𝐱𝐭𝐫𝐚𝐜𝐭: 👉 The first step is to extract data from various sources. 👉 PySpark supports extracting data from a variety of sources such as files (CSV, JSON, Parquet, etc.), databases (MySQL, PostgreSQL, etc.), and distributed storage systems (HDFS, Amazon S3, etc.). 🖊 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦: 👉 After extracting the data, you perform transformations on it to clean, filter, aggregate, or manipulate it according to your requirements. 👉 PySpark provides a rich set of functions for these transformations. 👉 Transformations are performed majorly on DataFrames. 🖊 𝐋𝐨𝐚𝐝: 👉 Finally, the transformed data is loaded into a target destination, which could be a database, a data warehouse, or another storage system. Here sharing a pyspark cheat sheet by Waleed that contains commonly used pyspark functions and its syntax. Save this post for sure. If you've read so far, do LIKE the post 👍 #databricks #pandas #dataengineer
#Extract Transform Load Data Warehousing Reel by @dispatchtrucks - Rate con data entry is killing your productivity. 3 minutes of typing vs 8 seconds of uploading. The data's already in the PDF. Why are you retyping
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@dispatchtrucks
Rate con data entry is killing your productivity. 3 minutes of typing vs 8 seconds of uploading. The data’s already in the PDF. Why are you retyping it? I built RateConConverter because I was tired of watching dispatchers waste hours on data entry that a computer should handle. Upload → Extract → Done. Want to test it? Comment “RATE CON” below and I’ll send you free access. Let’s see if this actually saves you time. #freightdispatching #truckdispatcher #dispatcherlife #trucking
#Extract Transform Load Data Warehousing Reel by @emrcodes (verified account) - Comment "ETL" to get the links! 🔥 If you're working with data but don't truly understand ETL, you're only seeing half the system. Dashboards, analyt
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@emrcodes
Comment “ETL” to get the links! 🔥 If you’re working with data but don’t truly understand ETL, you’re only seeing half the system. Dashboards, analytics, ML pipelines, and data platforms all depend on it — whether you realize it or not. ⚙️ ETL (Extract, Transform, Load) — Explained Clearly A clean breakdown of what ETL is, why it exists, and how raw data actually becomes usable. 📚 ETL with a Real Example Step-by-step explanation showing how data moves from source → transformation → destination in real systems. 🎓 ETL for Beginners (Non-Technical Explanation) Perfect if you’ve heard the term everywhere but never built a solid mental model of it. 💡 With these ETL resources you will: 📊 Understand how data pipelines really work 🧠 Learn the difference between raw data and analytics-ready data 🏗 Connect databases, data warehouses, and reporting systems logically ☁ Level up for Data Engineering, Backend, Analytics, and Cloud roles If you want to move from “I query data” to “I design data systems”, ETL isn’t optional — it’s foundational. 📌 Save this post so you always have an ETL roadmap 💬 Comment “ETL” and I’ll send you all the links 👉 Follow for Backend Engineering, Data Systems, and Career Growth
#Extract Transform Load Data Warehousing Reel by @pre_placement_preparations - 12 Essential Data Engineering Terms You Should Know Whether you're new to data or already deep into backend systems, mastering these core concepts is
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@pre_placement_preparations
12 Essential Data Engineering Terms You Should Know Whether you're new to data or already deep into backend systems, mastering these core concepts is crucial. They form the foundation of scalable, reliable, and insight-driven data infrastructure. ETL (Extract, Transform, Load) Classic method to move data from sources transform it - load into data warehouses. → Data Lake Central storage for massive volumes of raw data in native formats highly flexible for downstream processing. Data Warehouse Optimized, structured storage designed for analytics, dashboards, and Bl workloads. Streaming Real-time data processing as events occur supports instant decision-making and alerts. → Data Pipeline Automated series of steps that extract, transform, and deliver data between systems. Batch Processing Processes large volumes of data in scheduled intervals - suitable for high-throughput jobs. Data Mart A focused slice of a data warehouse - tailored for specific business functions or teams. MPP (Massively Parallel Processing) Executes data tasks across multiple nodes in parallel - essential for performance at scale. Data Mesh Decentralized architecture where each domain owns and governs its own data as a product. Data Quality Ensures data is accurate, complete, consistent, and trustworthy throughout its lifecycle. CDC (Change Data Capture) Efficiently captures inserts, updates, and deletes from source systems for real-time syncing. Data Lineage Tracks where data comes from, how it's transformed, and where it goes - enables auditability and trust. These terms are the building blocks of the modern data stack. Master them to boost your confidence, clarity, and capability as a data engineer. . . Follow for more 👉 @pre_placement_preparations . . #dataentry #dataanalyst #dataanalytics #dataanalysis #data #tech #technology #databases #datastructure #dataengineer #computerscience #computerengineering #sqldeveloper #sql #sqldatabase #machinelearning #artificialintelligence #ml #ai #datascience #python #python3 #pythonprogramming #pythondeveloper #datascientist
#Extract Transform Load Data Warehousing Reel by @askdatadawn (verified account) - Build a data cleaning & reporting workflow in ✌🏽 minutes (without code!) #KNIMECollab In this example, I'm using KNIME to build a very simple data c
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@askdatadawn
Build a data cleaning & reporting workflow in ✌🏽 minutes (without code!) #KNIMECollab In this example, I’m using KNIME to build a very simple data cleaning & reporting workflow. This is the most common framework I’ve used for reports: 1. Extract data 2. Clean / transform the data 3. Load data I just started using KNIME recently, and it is so powerful and versatile. It’s designed for Data Analysts & Data Scientists in mind, and they’re already integrated with many different data tools, like Excel and SQL databases. Plus, when you get KNIME Pro, you can schedule the workflows you build. No need to keep running the same processes manually! If you are in one of those roles, I highly recommend that you check it out! #datascience #dataanalytics #automations
#Extract Transform Load Data Warehousing Reel by @datawithsai (verified account) - Every insight. Every dashboard. Every ML model. All depend on one thing - ETL 💡 Here's how it works: 1️⃣ Extract: Gather raw data from databases, s
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@datawithsai
Every insight. Every dashboard. Every ML model. All depend on one thing - ETL 💡 Here’s how it works: 1️⃣ Extract: Gather raw data from databases, spreadsheets, APIs, or apps — just like collecting ingredients before cooking. 2️⃣ Transform: Clean, filter, and format it. Remove duplicates, fix missing values, and apply calculations — prepping your data to perfection. 3️⃣ Load: Store it in a data warehouse or BI tool — ready for analytics, dashboards, and ML models to shine. Without ETL, your data is just noise — messy and unreliable. Follow us @datawithsai to kick start your Data Analytics and Science journey. . Stay tuned for more. . #datascienceeducation #datascientist #dataanalysis #dataanalyst #pythonprogramming #pythondeveloper
#Extract Transform Load Data Warehousing Reel by @topclickmediasa (verified account) - Use the Instant Data Scraper to quickly pull data from websites! #scrape #data
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@topclickmediasa
Use the Instant Data Scraper to quickly pull data from websites! #scrape #data
#Extract Transform Load Data Warehousing Reel by @nailiartifact - The 2025 Loading Machine That Changes Everything! #FutureOfLogistics #SmartLoading #HydraulicPower #WarehouseRevolution #NextLevelEfficiency
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@nailiartifact
The 2025 Loading Machine That Changes Everything! #FutureOfLogistics #SmartLoading #HydraulicPower #WarehouseRevolution #NextLevelEfficiency
#Extract Transform Load Data Warehousing Reel by @themathcompany - Data engineering used to be simple: extract, transform, load, and move on. But as Varun Saraogi, Principal Data Architect, MathCo, shares, today's re
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@themathcompany
Data engineering used to be simple: extract, transform, load, and move on. But as Varun Saraogi, Principal Data Architect, MathCo, shares, today’s reality looks nothing like that. Watch as he breaks down how the shift from ETL to context-driven engineering is reshaping the entire landscape. Messy data, complex platforms, and a growing need for context-aware decisions are redefining what it means to be a data engineer and why value-focused engineering matters more now than ever. #MathCo #DES2025 #DataEngineering #EnterpriseAI #AIPlatform #EvolutionofDataEngineering #OwnYourIntelligence

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