标签：#dataengineering

If you've been calling yourself a "prompt engineer" for the past two years, it's time to update your vocabulary — and your mental model. In 2026, the real leverage when building LLM-powered systems isn't in crafting the perfect sentence. It's in context engineering : designing everything an LLM sees before it ever generates a response. Andrej Karpathy coined the term in mid-2025, and it's since taken over serious AI engineering discussions. This article breaks down what context engineering actually is, why it matters more than prompt writing, and gives you concrete techniques you can apply today. What Is Context Engineering? Context engineering is the discipline of systematically designing the information environment that surrounds a prompt. Where prompt engineering asks "what should I tell the model to do?", context engineering asks "what does the model need to know to do it well?" Think of it this way: a doctor doesn't just answer the question you ask on the spot. They look at your chart, your history, your vitals, and then respond. Context engineering is building that chart for your LLM. The context window is the LLM's working memory — everything it can "see" at once. In 2026, these windows are massive: Claude Opus 4.x : 200K tokens GPT-4o : 128K tokens Gemini 2.5 Flash : Up to 1M tokens But bigger isn't automatically better. More tokens = more cost, more latency, and a real risk of what researchers call the "lost-in-the-middle" problem — where models process information at the beginning and end of the context more reliably than content buried in the middle. Why This Matters for Data Engineers Data engineers are increasingly building pipelines that feed LLMs: RAG systems, AI copilots for data quality, agents that write and review SQL, tools that summarize data lineage. In every one of these systems, the quality of what lands in the context window directly determines output quality. A poorly designed context is like feeding a senior analyst a jumbled mess of raw l

T-SQL on Microsoft Fabric - Episode 1: Mastering Data Retrieval with SELECT, WHERE, and ORDER BY Learning Goals In this lesson, you will learn how to: Read data from tables using SELECT Filter rows with WHERE Sort query results with ORDER BY Get familiar with standard T-SQL syntax Practice directly in Microsoft Fabric Warehouse 1. Understanding Database and Schema In Fabric Warehouse, objects are commonly organized like this: Warehouse | |-- sales | |-- Customers | |-- Orders | |-- hr | |-- Employees | |-- finance |-- Transactions Schemas help you: Group related tables Manage permissions Organize large systems more effectively 2. Create a Schema Create a schema for the sales dataset: CREATE SCHEMA sales ; Check existing schemas: SELECT * FROM sys . schemas ; 3. Create Tables Create the Customers table: CREATE TABLE sales . Customers ( CustomerID INT , CustomerName VARCHAR ( 100 ), City VARCHAR ( 50 ), Country VARCHAR ( 50 ) ); Create the Orders table: CREATE TABLE sales . Orders ( OrderID INT , CustomerID INT , OrderDate DATE , Amount DECIMAL ( 10 , 2 ) ); 4. Insert Sample Data Customers INSERT INTO sales . Customers VALUES ( 1 , 'John Smith' , 'New York' , 'USA' ), ( 2 , 'Emma Brown' , 'Chicago' , 'USA' ), ( 3 , 'David Wilson' , 'London' , 'UK' ), ( 4 , 'Sophia Taylor' , 'Manchester' , 'UK' ), ( 5 , 'Michael Lee' , 'Singapore' , 'Singapore' ); Orders INSERT INTO sales . Orders VALUES ( 101 , 1 , '2026-01-10' , 1200 . 00 ), ( 102 , 1 , '2026-01-15' , 800 . 00 ), ( 103 , 2 , '2026-01-20' , 2500 . 00 ), ( 104 , 3 , '2026-02-01' , 500 . 00 ), ( 105 , 5 , '2026-02-05' , 3200 . 00 ); 5. SELECT Get all columns: SELECT * FROM sales . Customers ; Get specific columns: SELECT CustomerName , Country FROM sales . Customers ; 6. Alias Rename columns in the output: SELECT CustomerName AS Customer , Country AS Nation FROM sales . Customers ; 7. WHERE Filter rows using conditions. Customers in the USA: SELECT * FROM sales . Customers WHERE Country = 'USA' ; Orders greater than 100

Switching focus from Frontend development to Data Engineering means shifting from building user interfaces to architecting robust data pipelines. It’s a completely different mindset, and the learning curve is exciting! As I dive deeper into the world of Data, these are the 4 essential tools and concepts that have become the absolute backbone of my daily learning roadmap: 1️⃣ Python (The Swiss Army Knife): Coming from JavaScript/TypeScript, picking up Python has been a breath of fresh air. From writing custom ETL scripts to data manipulation with Pandas, it's the ultimate language for data manipulation. 2️⃣ Advanced SQL (The Core): It's not just about simple SELECT statements anymore. Mastering Window Functions, CTEs (Common Table Expressions), and query optimization is where the real magic happens when interacting with Data Warehouses. 3️⃣ ETL/ELT Pipelines: Understanding how to efficiently Extract, Transform, and Load data without breaking downstream analytics. Moving from UI state management to Data state management is a game-changer. 4️⃣ Cloud Ecosystems & Modern Stack: Exploring how data flows through modern cloud environments and learning how big data tools manage scale. The transition requires patience, but applying my previous engineering background to these new tools makes the journey incredibly rewarding. 💡 To the Data Engineers in my network: What is the one tool or concept you believe is a "must-have" for someone transitioning into the field today? Drop your advice below!

Introduction In contemporary research, the volume of biological data deposited in public repositories is growing exponentially. The Gene Expression Omnibus (GEO), NCBI Gene, PubMed, and UniProt accumulate thousands of new records daily, including sequences, expression profiles, scientific articles, and functional annotations. On the one hand, this scenario represents a unique opportunity for biomedical research. On the other hand, the diversity of data formats, access protocols, and metadata models creates a significant barrier: each source requires a specific collector, distinct rate-limiting strategies, and its own validation logic. Above all, the lack of standardization in data storage compromises the reproducibility of scientific studies. The need for integrated tools capable of unifying data extraction, curation, and persistence has been widely discussed. In practice, ad hoc solutions such as isolated scripts for individual repositories generate redundant work and make maintenance difficult. First and foremost, it is necessary to establish an architecture that treats data collection as a service rather than a collection of scattered artifacts. This work presents Project 1 of the Integrated Bioinformatics Platform: a containerized Biomedical Data Collector coupled with a Data Lake. Its objective is to provide a REST API capable of triggering asynchronous data collections from the four aforementioned sources, storing immutable raw data in MinIO, and persisting metadata in PostgreSQL, all while ensuring traceability and resilience. Development The system architecture is divided into three main layers. The first is the API and orchestration layer , implemented using FastAPI. Its five endpoints — POST /collections , GET /collections , GET /collections/{id} , GET /collections/{id}/download/{dataset_id} , and GET /health — expose a clean interface for initiating and monitoring collection processes. The second layer is the collector engine , composed of abstract classe

When I first connected to a precious metals WebSocket API, I expected to get a clean stream of prices. What I actually got was a firehose of mixed ticks—gold, silver, platinum—all arriving through the same callback. If you’ve ever tried to build a trading bot or a custom chart, you know this is a recipe for disaster. In this post, I’ll share how I solved the problem with a few lines of Python and a clear mapping strategy. The scenario: You have one WebSocket URL that pushes quotes for multiple metals. You need to separate them so you can update different UI components, run independent strategies, or store them in distinct database tables. The data pain point: every message uses the same JSON structure, and the only differentiator is a field like symbol . If you don’t act on it immediately, everything gets mixed up. Identify Assets via the Symbol Field Start by checking the API docs for the field that carries the instrument code. Usually it’s symbol , but instrumentId or type are also used. Here’s a typical reference table: Field Description Example symbol Asset code XAUUSD, XAGUSD instrumentId Internal platform ID 1001, 1002 type Asset class gold, silver I turn this into a dictionary mapping each symbol to a human-readable category: asset_map = { " XAUUSD " : " gold " , " XAGUSD " : " silver " , " XPTUSD " : " platinum " } Buffer Messages by Type Because these streams are high-frequency, I avoid processing every tick individually. Instead, the WebSocket callback just updates an in-memory store that is already grouped by asset type: # Keep the hot path extremely light def on_message ( msg ): symbol = msg [ ' symbol ' ] price = msg [ ' price ' ] asset_type = asset_map . get ( symbol , " unknown " ) cache [ asset_type ][ symbol ] = price Then, a background timer fetches the latest prices from cache["gold"] and cache["silver"] separately and does the actual work—like computing indicators or rendering charts. The key benefit is complete isolation: your gold logic never t

Over the past decade, the core evolution of data engineering has been the deconstruction and reconstruction of traditional data warehouse architectures through the Modern Data Stack. We separated data ingestion from databases, forming the Data Ingestion layer, using tools like FiveTran, Airbyte, and Apache SeaTunnel to solve ELT / CDC / Reverse ETL problems; We separated compute from storage, forming cloud data warehouse and lakehouse systems such as Snowflake, Databricks, Iceberg, and Hive; We separated orchestration from scripts, leading to orchestration systems like Apache Airflow and Apache DolphinScheduler; SQL development, data modeling, lineage, data quality, BI, and AI analytics were further split into independent tools. This architecture was undoubtedly progress. It moved data engineering away from the primitive era of “a bunch of scripts + Crontab” toward cloud-native infrastructure, elastic computing, engineering governance, and open ecosystems. The greatest contribution of the Modern Data Stack was “decoupling,” and its biggest side effect was also “decoupling.” Tools became more powerful, but data engineers were forced to switch between more systems than ever before: datasources in one place, synchronization configs in another, DAGs somewhere else, logs elsewhere, SQL stored in Git, and Snowflake / Iceberg / cloud warehouse execution results living in yet another environment. As a result, many data engineers spend less time on data modeling, business understanding, metric definitions, architecture design, and cost optimization — and far more time configuring datasources, setting field mappings, dragging DAG nodes, modifying SQL, checking logs, and rerunning tasks. This is the hidden pain created by the Modern Data Stack: data engineers became trapped inside tools. The emergence of engineering-focused AI systems like Codex and Claude Code is now changing the entire software engineering workflow. But how can data engineers truly achieve Vibe Coding? That