SQL Server to PostgreSQL Migration with Python ETL Pipeline

🚀 **From Big Data Basics to PySpark Pipelines: Beginner Python Roadmap** Big data architecture Not with Python + PySpark! After covering core concepts, here's your **30-min starter ETL pipeline** using Lambda architecture. **Step 1: Lambda Layers Quick View** - **Ingestion**: Batch (HDFS/S3) + Speed (Kafka) - **Storage**: Data Lake (Parquet) - **Processing**: PySpark for scalable transforms **Hands-on Code (use Databricks Community Edition - Free!):** 💡 **Pro Tip**: Start with sample CSV . run on Databricks Community (no setup needed) #BigData #PySpark #DataEngineering #Python #ETL #BeginnerFriendly

🚀 Just built a File-to-Database Loader using Python! I developed a data pipeline tool that reads structured CSV flat files and loads them into PostgreSQL — schema-driven, memory-efficient, and production-ready. 🔧 What it does: ✅ Schema-driven column mapping via schemas.json ✅ Chunked loading (10K rows at a time) for large datasets ✅ Supports 6 retail datasets: customers, orders, products, categories, departments & order_items ✅ Flexible CLI — load all tables or target specific ones ✅ Powered by Pandas + SQLAlchemy + psycopg2 📦 Tech Stack: Python | Pandas | SQLAlchemy | PostgreSQL | dotenv This kind of ETL tooling is at the heart of modern data engineering — taking raw files and making them queryable at scale. link first comment #Python #DataEngineering #ETL #PostgreSQL #Pandas #DataPipeline

Thrilled to complete "Introduction to Importing Data in Python" on DataCamp! 📥🐍 As a Data Engineer, the first step of any successful data pipeline is getting data into Python efficiently. This course was a comprehensive masterclass on data ingestion from ALL sources: Key Skills Mastered: 🔹 Flat Files: Reading and customizing imports from .txt, .csv using pandas and NumPy 🔹 Enterprise Formats: Excel spreadsheets, Stata, SAS, and MATLAB files 🔹 Relational Databases: SQL queries with SQLite & PostgreSQL (filtering, ordering, JOINs) 🔹 Production ETL Foundations: Building robust data extraction workflows From simple CSV imports to complex database joins, I now have a complete toolkit for the most critical first step in data engineering. Ready to build more efficient, scalable data ingestion pipelines! 🚀⚙️ #DataEngineering #Python #DataPipelines #ETL #SQL #Pandas #DataCamp #DataIngestion #ContinuousLearning

Designed and implemented a modular ETL pipeline in Python to extract data from a REST API, transform and normalize JSON structures, and load processed data into PostgreSQL using SQLAlchemy. Focused on clean separation of pipeline stages and scalable architecture. Tech: Python, Pandas, SQLAlchemy, PostgreSQL. Link => https://lnkd.in/dWNjvx9n

Spark Connect — I kept seeing this term but never really understood what problem it was solving. So I dug deeper. Before Spark Connect, the client and Spark driver were tightly coupled. Your PySpark script ran directly inside the driver process. This meant: → Heavy dependency overhead (matching Java, Scala, Python versions) → Client crashes could take down the driver → Building non-JVM clients was a difficult process → PySpark relied on Py4J to bridge into the driver's JVM Spark Connect changes all of this by clearly separating the client and the server. Here's the simplified flow: 1. The client converts your DataFrame or SQL query into an Unresolved Logical Plan 2. That plan is serialized using Protocol Buffers 3. Sent to the Spark server via gRPC 4. The server deserializes, optimizes, and executes it 5. Results come back as Apache Arrow record batches — streamed, not dumped all at once The result? The client no longer needs a full Spark installation. The server can be updated independently. And since the entire communication stack (gRPC + Protobuf + Arrow) is language-agnostic, building Spark clients in Python, Go, Rust — much simpler. Check out my detailed write-up on Spark Connect on Medium 👇https://lnkd.in/gmZegTXn #ApacheSpark #PySpark #SparkConnect #DataEngineering 

Ever stuck with unstructured data in Excel sheets or spreadsheets and needed to push that messy data into a structured database? 🤯 Recently, I faced a similar challenge, a large spreadsheet filled with inconsistent, unstructured data that needed to be transformed into multiple clean tables. Doing it manually would’ve been time consuming and error prone. Here comes Python 🐍 Instead of struggling with manual cleanup, I built a small data pipeline using Python to automate the entire process from parsing and structuring the data to inserting it directly into a PostgreSQL Supabase database. What could’ve taken hours was reduced to minutes with better accuracy and scalability. As software engineers, knowing the right tool can turn a messy problem into an elegant solution. #Python #DataEngineering #Automation #PostgreSQL #Supabase #SoftwareEngineering

Sharing a quick walkthrough of my work on Building ETL Skills with Spark: A Practical Approach. This video highlights key PySpark ETL operations including data reading, transformation, filtering, aggregation, and writing outputs in different formats. A practical overview of how data processing workflows are built using Spark. Allan Abraham SS Infotech #PySpark #DataEngineering #ETL #ApacheSpark #Python

Something that helped me understand data engineering better: Think about data flow. Instead of thinking about code first, I try to visualize: Where the data starts How it moves Where it gets transformed Where it ends up For example: Source → Cleaning → Transformation → Storage → Analytics That’s essentially the idea behind ETL pipelines. Python and SQL help implement it. But the real skill is designing the flow of data through the system. That shift in thinking has helped me understand data engineering much better. #DataEngineering #ETL #DataPipelines #Python #SQL #CloudData

Sail 0.6 is out. Three new surfaces, all Arrow-native: - Arrow UDFs from Spark 4. Python functions decorated with @arrow_udf run against Arrow data directly. Because Sail executes Python Arrow UDFs inline within the same Rust process, it enables Python code to run at native speed with zero-copy data transfer, avoiding the separate-process overhead inherent in Spark's architecture. - Variant type in SQL. Parse JSON into a variant with parse_json, then query it with variant_get and path expressions. Lookups run against binary data instead of re-parsing strings. - Arrow Flight SQL server on the wire. The first alternative protocol Sail supports beyond Spark Connect. Start a Flight SQL server powered by Sail and connect from any Flight SQL client to query it directly. Read the full post: https://lnkd.in/gdAJ28gw

Over the past few days, I’ve been diving into PySpark and distributed data processing concepts. Coming from a background in Python, SQL, and data-driven backend systems, it’s been interesting to see how similar data transformations scale when working with large datasets. I’ve been exploring how Spark handles data processing across clusters and how it fits into real-world data pipelines. Currently focusing on: • Working with Spark DataFrames • Performing transformations (filter, groupBy, joins) • Understanding ETL workflows at scale Still early in the learning process, but it’s a valuable step toward building more scalable data solutions. #PySpark #DataEngineering #BigData #Python #LearningJourney