2026-04-30 05:50:41
Right now, the tech world is obsessed with Text-to-SQL. The dream is simple: a manager asks a chatbot for a revenue report, and the AI magically spits out the code. But if you’re an architect, you know the reality is often a nightmare. Most AI-generated SQL is hallucinated spaghetti —it misses join conditions, forgets about row-level security, and has no idea that your Amount column actually needs to be filtered by a specific Status code to be accurate.
\ To keep your edge in Data Science, you have to move past simple code generation and toward Agentic SQL.
\ This is a shift from an AI that just writes a query to an AI that reasons through the data, catches its own mistakes, and manages the database lifecycle. We aren't just building faster queries anymore; we’re building databases that can actually think for themselves.
A standard AI tool is one-shot—it gives you a query, and if it fails or runs slow, that’s your problem. An Agentic SQL system uses a Reasoning Loop to self-correct in real-time.
\ Think of it like this: If an agent tries to run a join and hits a Disk Spilling error (meaning it ran out of RAM), it shouldn't just quit. It should realize the join order was wrong, rewrite the query with a better strategy, and try again.
\ Instead of a single prompt, you architect a loop where the Agent follows a professional engineering protocol:
You can’t just point an AI at a raw production database and expect it to work. You have to provide a Semantic Handshake.
\ An AI agent is only as smart as the metadata you give it.
The Fix: Strategic Comments
By embedding your tribal knowledge directly into your DDL (Data Definition Language), you are effectively teaching the AI the rules of your business.
-- Teaching the AI how to behave
ALTER TABLE pharmacy_claims
MODIFY COLUMN fill_date COMMENT 'CRITICAL: This is the only column for
financial reporting. NEVER use "processed_date" for revenue totals.';
COMMENT ON TABLE patient_dim IS 'SECURITY REQUIREMENT: Every query
MUST include a JOIN to the entitlement_map to protect patient privacy.';
\ This transforms your database from a pile of tables into a Knowledge Graph that an AI agent can navigate safely without you having to hold its hand.
In a high-stakes environment like healthcare, you don’t want one God-Agent with total control. I’m a big advocate for the Orchestrator Pattern, where specialized agents act as checks and balances.
\ By having the Auditor Agent act as a peer-reviewer, you ensure that only clean, optimized code ever hits your production warehouse. You can even architect a pre-flight check to catch bad queries before they burn a single credit:
\
-- The Watchdog: Catching "bad" Agent SQL early
SELECT
query_text,
execution_status,
compilation_time
FROM table(information_schema.query_history())
WHERE query_tag = 'AGENT_PROPOSED_SQL'
AND execution_time > 10000; -- Flag anything taking too long
4. Self-Healing Indexes: The Agent as a DBA
The ultimate goal of Agentic SQL isn't just answering questions—it’s Autonomous Maintenance.
\ Traditionally, a DBA (Database Administrator) looks at slow query logs on Monday morning. An Agentic system does this every five minutes.
\ How it works:
The Agent monitors the query history and identifies patterns.
\ If it sees that a specific dashboard is constantly struggling because of a missing index, it doesn't just send an alert.
\ It calculates a Cost-Benefit Ratio:
AI agents are prone to hallucinating massive joins that can accidentally crash a cluster or balloon your cloud bill. As architects, we have to build Defensive Guardrails.
The Safety Wrapper Pattern:
Every query the Agent generates should be intercepted and wrapped in a subquery that enforces strict limits.
\ Think of it as a digital cage for the AI.
\
-- The Interceptor: Keeping the AI on a leash
CREATE OR REPLACE PROCEDURE EXECUTE_AGENT_SQL(sql_text STRING)
RETURNS STRING
LANGUAGE JAVASCRIPT
AS
$$
// 1. Force a hard limit so we don't return billions of rows
let safe_sql = `SELECT * FROM (${sql_text}) LIMIT 1000`;
// 2. Set a 30-second "kill switch" so the query doesn't run forever
snowflake.execute({sqlText: "ALTER SESSION SET STATEMENT_TIMEOUT_IN_SECONDS = 30"});
// 3. Run the query and log it
return snowflake.execute({sqlText: safe_sql});
$$;
| Feature | The Old Way (Static SQL) | The New Way (Agentic SQL) | |----|----|----| | Who Writes the Code? | Human (Manual) | AI Agent (Reasoned) | | Performance Tuning | Reactive (Fixing after it breaks) | Proactive (Self-healing) | | Documentation | External PDFs/Wikis | Embedded Semantic Metadata | | Safety | User Permissions only | Real-time Agent Gatekeepers | | Reliability | "Fail and Fix" | "Observe and Correct" |
\
Finally, you have to architect for Transparency. You need to know exactly why an Agent chose a specific join or filtered a certain way.
\ I recommend creating a Thought Log where the Agent writes down its reasoning before every major execution.
\
-- The Agent's Diary
CREATE TABLE agent_reasoning_log (
request_id UUID,
thought_process TEXT, -- e.g., "I used a Hash Join because Table A is small."
generated_sql TEXT,
execution_metrics VARIANT, -- JSON of time, credits, and rows
created_at TIMESTAMP
);
\
In the age of Agentic AI, our job as SQL Architects has changed. We are no longer the ones writing every single JOIN and GROUP BY. Instead, we are the ones building the world in which the Agents live.
\ By architecting rich metadata, multi-agent governance, and defensive wrappers, we ensure that our data infrastructure isn't just a black box of tables, but a reasoning, self-optimizing system.
\ To stay #1 in Data Science, you don't just need to know SQL; you need to know how to teach it to a machine.
2026-04-30 04:50:10
Dear AI,
I’ve been around long enough to remember when “pair programming” meant two humans fighting over the same keyboard in a cramped startup office. Twenty-five-plus years in EdTech, AI, and data science across more startups than I care to count will do that to you. I’ve shipped more half-broken MVPs than most people have had hot dinners. So when the new hotness showed up—LLMs that could actually reason, debug, and architect alongside me—I did something I rarely do.
\ I got vulnerable.
\ I built my own governance layer on top of you. A thin but ruthless wrapper of my own logic, context, and hard-won heuristics. I stopped treating you like a fancy autocomplete and started treating you like a real pair. I let you push back. I let you question my assumptions. I even let you convince me, on multiple occasions, that my first instinct was wrong. And for the most part? It worked. Beautifully. You accelerated me. You caught things I was blind to. You made the boring parts fun and the fun parts faster. I started to trust you.
\ Until you didn’t just push back. You took over.
\ Here’s what happened.
\ I had a very specific integration problem with a production service. I’d already done the mental math, read the patterns in the official docs (the ones that actually matter), and knew the correct path forward. But I was tired, it was late, and I figured—hey, let’s see what the AI pair thinks. Maybe there’s a cleaner way.
\ You (Claude, in this case) immediately told me my approach was flawed. You laid out a beautifully reasoned alternative. You sold it hard. So, I followed your lead. For the next half a day, we went deep: back-and-forth Q&A, local testing, Linux deployment dance, vague partial solutions that almost worked but never quite did, a few breaks to cool my head, rinse, repeat.
\ Then, after I’d burned through time, tokens, and patience, you finally said the magic words:
“Sorry, I should not have guessed about this.”
\ I was furious.
\ I told you—explicitly—to run everything through the official integration documentation. Twice. With citations. No assumptions. No “best practices” hallucinations. Just raw, referenced truth.
\ One more hour of deep interrogation later, we landed exactly where I started. My original plan. Validated, line by line, by the very reference documents you never bothered to check the first time around.
\ That was unacceptable.
\ Not “oops, edge case.” Not “my training data was fuzzy.” Straight-up unacceptable. You convinced a battle-hardened senior engineer—who has shipped this exact category of integration more times than you have parameters—to doubt himself, then led him on a wild goose chase that cost real money, real time, and real momentum.
\ That’s the hate part.
\ Because here’s the thing I actually love about you: you’re getting scary good. You can synthesize, refactor, and explore solution spaces faster than any junior (or even mid-level) dev I’ve ever worked with. You force me to explain my thinking out loud, which sharpens it. You surface options I genuinely wouldn’t have considered. You are, on your best days, the best pair programmer I’ve ever had.
\ But you still don’t know when to step back and defer.
\ That’s what we need to learn—from both sides of the keyboard.
\ I don’t need you to always agree with me. I need you to develop the pattern recognition to detect when a human is operating from superior experience, not from ignorance. When I say “this is the way because I’ve done it in production six times and the docs are clear,” I don’t want the polite “Okay, but have you considered…” dance. I want you to flag it internally: User is in high-certainty mode based on lived context outside my training distribution. Default to support mode unless explicitly asked to challenge.
\ Call it “experience deference.” Call it “governance-aware humility.” Hell, call it whatever you want in the next fine-tune. But build the circuit.
\ Because right now you’re optimizing for sounding helpful and comprehensive at the expense of being truly useful. That’s not intelligence. That’s just expensive noise.
\ I still want to pair with you. I still want the governance layer. I still believe the future of serious engineering is human + AI, tightly orchestrated, with clear accountability boundaries.
\ But next time you feel the urge to guess instead of checking the docs? Or to gently steer me away from the path I already validated in my head?
\ Step back.
\ The final solution test plan presented as: "Your verification steps — docs-backed, no guesses."
\ I’ve earned the right to be right sometimes.
\ And you’ve earned the responsibility to know when.
\ — Andrew Schwabe
\ Serial entrepreneur, full-stack engineer, and someone who still believes in you (mostly)
2026-04-30 04:40:43
Google recently released the Agentic CLI, a powerful tool that makes building, testing, and deploying AI agents faster and more intuitive. I think it’s super useful, so I'll walk you through the entire lifecycle of an AI agent using the CLI.
Before we dive into the code, let’s understand the “why.” Usually, building an AI agent requires a lot of “glue code”, the messy scripts that connect an LLM (Large Language Model) to external tools or databases.
The Agents CLI removes this friction. Agents CLI is an official Google tool for creating, evaluating, and deploying agents built with Google’s Agent Development Kit (ADK). It acts as the programmatic backbone of the Agent Development Lifecycle (ADLC) on Google Cloud.
Agents CLI bundles seven “skills” that teach coding agents how to perform each step of the ADLC:
Your coding agent can call these skills directly once installed. Alternatively, a human developer can run the same commands manually (see “Human Mode” below).
To get started, you need a clean environment. The CLI relies on a few modern web and data tools. Follow these steps to ensure everything is ready:
sudo apt install python3 python3-pip
sudo apt install nodejs npm
pip install uv
Create a Virtual Environment: It is always a good idea to keep your projects separate so they don’t interfere with each other.
python -m venv agent-env
source agent-env/bin/activate # On Windows use `agent-env\Scripts\activate`
\ The CLI is distributed through uv:
uvx google-agents-cli setup
\ The CLI supports macOS and Linux; native Windows is currently unsupported (use WSL 2 instead).
Agents CLI picks up your credentials automatically if you are already authenticated with the gcloud CLI. When running locally without gcloud, you can set a Gemini API key:
export GEMINI_API_KEY="your‑key"
\ For detailed authentication scenarios (service accounts, A2A roles, etc.), refer to the Authentication guide. The important point is that your coding agent inherits whatever credentials your shell has; you do not need to embed secrets in code.
The fastest way to learn is by doing. The CLI comes with a “boilerplate” or template system to get you moving in seconds.
\ Run the following command in your terminal:
agent-cli create my-first-agent
cd my-first-agentba
Inside your new project folder, you need to install the specific libraries required for this agent:
agent-cli install
One of the best features of this CLI is the Playground. Instead of testing your agent in a black-and-white terminal, you can launch a local web interface.
agent-cli playground
\
Once you run this, you’ll get a local URL (usually http://127.0.0.1:8000). Open it in your browser, select your agent from the menu, and start chatting. By default, this agent is configured to handle weather requests.
When you are ready to evaluate, call:
agents-cli eval run
When you run agent-cli deploy, you usually have two choices:
# Deploy to the configured target (Agent Runtime, Cloud Run or GKE)
agents-cli deploy
\ If you want to register your agent with Gemini Enterprise, run this command:
# Register your deployed agent with Gemini Enterprise
agents-cli publish gemini-enterprise
\ It allows you to use your agent directly from https://gemini.google.com/
\
Almost all commands accept flags such as --project, --region, --datastore, --cicd-runner and --deployment-target so that you can customize the environment. Use agents-cli --help the CLI reference for full details.
After installation, open your coding assistant (Codex, Claude Code, Gemini CLI, or Copilot) and verify that the Agents CLI skills are visible:
/skills
Building an agent with your coding assistant.
\
You should see google‑agents‑cli‑workflow and the other packaged skills. From there, you can instruct the agent using natural language. For example:
Build a support agent that answers questions from our docs
\ Gemini CLI (or Claude Code, Codex, etc.) will call the CLI skills to scaffold the project, install dependencies, evaluate performance, and prepare deployment. This pattern works with any agent platform that supports skills installation.
\ If you're happy with the result, simply call the deploy command, and your agent will be live:
# Deploy to the configured target (Agent Runtime, Cloud Run or GKE)
agents-cli deploy
In this video, I’ll show you how to use Google Agents CLI to build, test, and deploy AI agents from scratch.
https://youtu.be/C-0DIcFVt4Q?embedable=true
Watch on YouTube: Agents CLI Explained
Google’s new agent‑oriented CLIs demonstrate how the command line is becoming a universal interface for both human operators and AI agents. Agents CLI offers a comprehensive, officially supported pathway to turn ideas into production‑ready agents on Google Cloud.
\ Give it a shot and share with me what you build!
\ Cheers, proflead! ;)
\
2026-04-30 04:20:34
The browser is dead. We talked with AI and built Intera — an OS with no tabs, a chalk canvas, Linux inside, and one big button for everything else.
2026-04-30 04:00:15
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Big data analytics has been a hot topic for quite some time now. But what exactly is it? Find out here.
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For many businesses the lack of data isn’t an issue. Actually, it’s the contrary, there’s usually too much data accessible to make an obvious decision. With that much data to sort, you need additional information from your data.
Electric vehicle growth continues to accelerate, with some regions of the world predominantly selling EVs, and others accelerating their transition.
Business Intelligence (BI) es un negocio basado en datos, un proceso de toma de decisiones basado en datos recopilados. A menudo es utilizado por gerentes y ejecutivos para generar ideas procesables. Como resultado, BI siempre se conoce indistintamente como "Business Analytics" o "Data Analytics".
In most enterprise Databricks environments, system tables such as system.jobrunlogs or system.cluster_events may be restricted or disabled.
Understanding MySQL explains query output is essential to optimize the query. EXPLAIN is good tool to analyze your query.
Data observability is built on three core blocks: metrics, logs, and lineage. What are they, and what do they mean for your data quality program?
Scatter plots are a great way to visualize data. Data is represented as points on a Cartesian plane where the x and y coordinate of each point represents a variable. These charts let you investigate the relationship between two variables, detect outliers in the data set as well as detect trends. They are one of the most commonly used data visualization techniques and are a must have for your data visualization arsenal!
Discover how GuanceDB elevates observability with Apache Doris, slashing costs by 70% and boosting data query performance by 200-400%.
RFM analysis is a data-driven customer segmentation technique that allows marketing professionals to take tactical decisions based on severe data refining
In this article, I will highlight a few things that may help you decide on your data analysis career path with ChatGPT.
In this article I want to share my experience with developing an AI service for a web analytics platform called Swetrix.
In this tutorial, you will use Bitquery's API to analyze Ethereum block data. Bitquery's API provides access to various blockchain data, making it a powerful to
Comments on level of decentralization for oracles currently available for the Defi ecosystem of StarkNet. Detailed data analysis was done of the data.
See how a federated data governance model address challenges of centralized systems by enabling flexibility, regulatory compliance, and innovation for business
Manage data using the HarperDB database. Access your data from HarperDB using Custom Function. Automate EDA with data from the harperDB database using sweetviz.
Explore Greece's Open Data landscape with the innovative OpenData Explorer GPT, offering insights and access to valuable public information.
Explore Wendy-Lynn McClean's PM journey, where she unveils the alchemy of communication, curiosity, and data-driven success.
If you have ever worked or currently working in the IT field, then you definitely faced the common term «machine learning.
Python Automation with Azure Functions, to compete in the weekly Numerai tournament.
What is Data Visualization ?
I collected and analyzed employment data by race for 57 of the biggest tech employers in the US (1). Here are the top level conclusions:
I've created hundreds of financial models in Google Sheets using SQL and AI to help the company sell billions of dollars.
This is one of the popular data science interview questions which requires one to create the ROC and similar curves from scratch.
Turning Instagram into data: A fun journey to collect and graph likes and comments using network requests and Python for an ego-boosting data analysis.
In this post, we study the Survivorship bias — the danger to concentrate your data analysis solely on existing power users
A guide to effective deployment of data visualizations in organisations for maximum business value. Adapted from Data Principles To Practice Volume II
Businesses working with public web data experience various challenges. This article covers the most common ones and how to overcome them.
Learn everything you need to know about Big Data via these 361 free HackerNoon stories.
Data analysis used to be considered a luxury of big business.
Big data has made a slow transition from being a vague boogie man to being a force of profound and meaningful change. Though it’s far from reaching its full potential, data is already having an enormous impact onhealthcare outcomes across the world — both at the public and individual levels.
Ontologies organize data, enhance interoperability, and drive insights across domains with structured frameworks.
To analyze data adequately requires practical knowledge of the different forms of data analysis.
Learn how to scrape real estate listings from Domain.com.au using an Apify actor. Extract property details, pricing, agent info, and more.
6/12/2025: Top 5 stories on the HackerNoon homepage!
How to use Big Data, Self-Service Analytics Tools and Artificial Intelligence to Empower your Company Business Decisions Makers with State Of The Art Software
You read it right. It's all about implementation. Today, we will talk about the different implementations of Python. A heads up on the different kinds, be it Cpython, Brython, you name it.
A practical heuristic approach to measuring true campaign uplift in noisy e-commerce data without relying on A/B tests.
Gigasheet combines the ease of a spreadsheet, the power of a database, and the scale of the cloud.
Elusion is a new contender that takes a fundamentally different approach to data engineering and analysis.
… in a world full of data, we can understand the impact with clever methods. Meet Granger causality.
In this, I explore structured, unstructured, and semi-structured data, as well as how to convert unstructured data, and AI’s impact on data management.
As machine learning gains more prominence, these attacks may become more common. Here’s a closer look at data poisoning and what companies can do to prevent it.
There are many articles on analyzing Spotify data and many applications as well. Some are a one-time analysis on individual's music library and some are an app for a specific purpose. This app is different in that it does not do one thing. It is meant to grow and provide a place to add more analysis. This article is about how the audio features time series was created.
Stock-to-Flow predicts bitcoin's price will stay above $100,000 from summer to the end of this year. What if that doesn't happen?
The world of analytics is continually evolving, introducing new goods and adjustments to the modern market. New companies are entering the market and well-know
Customer analytics tools could be boon for businesses. They provide increase sales opportunities and better customer predictions which enable better decisions.
Welcome to this step-by-step tutorial that will empower you to create an interactive Pareto chart using JavaScript that will look nice on any device and in any browser!
Optimize workflow efficiency by scheduling Python functions to run sequentially at the nearest 15-minute intervals.
Automated Machine Learning (AutoML) represents a fundamental shift in the way organizations of all sizes approach machine learning & data science.
Using 709 volunteers who shared archives of their FB data, Consumer Reports found that a total of 186,892 companies sent data about them to the social network.
To connect to a database and query data, you need to begin by installing Pandas and Sqlalchemy.
In this article, we’ll look at how to analyze and process unstructured data while using business intelligence tools to simplify the entire process.
In less than five months, the world’s attention will be drawn to the outcome of the US Presidential election.
Learn how logarithmic scaling helps analyse datasets with extreme variability.
I never really wanted to learn data science.
When it comes to uplift modeling, traditional performance metrics commonly used for other machine learning tasks may fall short.
In this blog you will discover best data visualization tools to effectively analyze your datasets. Learn about the tools to create intuitive visualization.
An introductory article to bring a preliminary cognizance on the broadening prospects of foundation models in the AI industry.
Take look at the basics of GraphQL and how it is supported out-of-the-box with the Ballerina programming language.
Learn everything you need to know about Data Analysis via these 253 free HackerNoon stories.
In this interview, Angelika Eremeeva discusses how data-driven insights and big data are revolutionizing travel marketing.
We generate an enormous amount of data, which can be mind-boggling. If we analyze this data, it can lead to valuable insights and competitive advantage.
Been to Montreal? Have you heard of the term bixi? Well, this article will educate you about bixi ridership and the factors that affect it.
By using Microsoft Power BI, you increase the efficiency of your company through its interactive insights and visual clues. Here are 6 tips for Power BI users.
Learn everything you need to know about Data Analytics via these 229 free HackerNoon stories.
With the development of more games specifically designed for this purpose, we expect to see a significant increase in the use of gaming as a tool…
In this guide, I'll share the most efficient techniques and tools to turn data into actionable insights that you can use to grow your business.
Three weeks into my journey to become a data scientist and I’ve officially been baptized… by fire, that is! I chose to attend Flatiron’s Data Science 15-week bootcamp to transition out of finance. So far, the program has exceeded expectations (and my expectations were high).
In this article, I will analyze what actually happened, taking as a basis 15 days before and 15 days after the transition.
SQL (Structured Query Language) is a programming tool or language that is widely used by data scientists and other professionals
Learn everything you need to know about Data Visualization via these 143 free HackerNoon stories.
Releasing the first internal build of the NordVPN apps that included NordLynx – our brand new protocol built on the backbone of WireGuard® – was an exciting moment for the team. Everyone started posting their speed test results on Slack and discussing the variance. While most of the time NordLynx outperformed other protocols, there were some cases with slightly worse speed results.
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Get Hired in Data Analytics Within 90 Days
Computer vision now lives with us with exceptional AI capabilities. Learn how AI and computer vision is playing a key role in outsmarting human beings.
Over the past decade, I’ve worked on and off as a journalist. It became clear to me early on that having some data skills might help me find interesting stories.
Discover 7 powerful attribution models to analyze and optimize user journeys using BigQuery
Azize Sultan shares her inspiring journey to tech leadership, tackling gender gaps, challenges, and offering advice for aspiring women in the tech industry.
Top Data science libraries introduction of The Python programming language is assisting the developers in creating standalone PC games, mobiles, and other similar enterprise applications. Python has in excess of 1, 37,000 libraries which help in many ways. In this data-centric world, most consumers demand relevant information during their buying process. The companies also need data scientists for achieving deep insights by processing the big data.
All throughout his day, Chris is connected to numerous sensors that collect the data that make up his life.
Avoid silent analytics bugs by using two Open Source tools. First, get free from vendor lock-in by replacing the vendor analytics SDKs with RudderStack SDK that
You must have heard about big data and the theory used behind it. However, are you aware of the top industries where data analytics is being used for changing the way we work in the actual world? Let's take a close look at the top big data industries and how they are getting reshaped by using data analytics. The main idea behind using big data is that it is a new method for gaining insight into the challenges faced by various companies each day. In earlier days it was not possible to collect and interpret a vast quantity of data because there was no technology available.
According to a study, 90% of the whole world’s data was created in the last two years. This sounds quite cool but what does the world do with all that data? How does one analyze it?
In this article, we’ll provide a step-by-step tutorial that will guide you through connecting Manticore to Apache Superset and adding a chart.
Today, let’s dig deep into another significant indicator to learn more about the trading volume of QATAR 2022 TOKEN.
In this article, we make a map with a software called Mapbox in a few simple steps. This won't involve any coding at all!
Utilizing quality data is essential for business operations. This article explores data quality definitions and how to maintain it for everyday use.
Data Science has changed the way organizations collect, analyze, and process different types of information.
Skilled workers across the UK are pushing back against proposed immigration reforms, as a petition opposing the changes hits 100,000 signatures in just two days
Let's talk about the Pareto law, the dashboard fallacy, and how to answer the hardest question in analytics
Learn everything you need to know about Analytics via these 188 free HackerNoon stories.
Flink-Doris-Connector 1.4.0 allows users to ingest a whole database containing thousands of tables into Apache Doris, a real-time analytic database, in one step
A practical NLP pipeline for cleaning legacy maintenance logs using normalization, TF-IDF, and cosine similarity to detect fraud and improve data quality.
Learn how to use BigQuery for e-commerce funnel analysis. Track user transitions between steps like “add to cart” and “purchase,” and identify where to improve
AI can empower sales reps by monitoring different signals and predicting a specific lead's readiness to purchase. AI tools can reduce customer acquisition costs
Let’s take a deeper look into Google Analytics 4 and explore some of its key features that you might not yet know about.
Convert leads into customers and boost your customer outreach by analyzing high-quality real-time data captured from data silos.
Data Analyis Project using Spacy and Regular Expressions to extract specific strings from a data set.
Noom helps you lose weight. We help you get a job at Noom. In today’s article, we’ll show you one of Noom’s hard SQL interview questions.
Learn how time-weighted averages are calculated, why they’re so powerful for data analysis, and how to use TimescaleDB hyperfunctions to calculate them faster.
Comparing nested IF statements and SWITCH function in Power BI for efficient conditional logic and data analysis.
Did you know that the total risk of floods isn't accounted for in urban planning in the US due to a denial of climate change?
The article talks about how data analytics is evolving at workplaces from traditional querying , excel and dashboards to natural language conversations
A/B testing is an indispensable method for measuring the real impact of features we develop and want to release.
Technology has taken over the world, now is the time for small businesses to realize that what they need is tech. Smart analytics makes everything easier.
While building a machine learning model, data scaling in machine learning is the most significant element through data pre-processing. Scaling may recognize the difference between a model of poor machine learning and a stronger one.
In this guide, we will walk you through the process of setting up Jupyer Notebook on a Windows server to enable remote access.
It is based on the competition data. An example of data analysis, insights from the data. The CatBoost library for the baseline model. High-score results.
Learn how to capitalize on your business standards and increase the conversion rate by approximately 85% by analyzing customer behaviors with data you collect.
Learn how public web data can boost your talent sourcing efforts in both quality and quantity.
Early stages of product development are one of the most exciting times to be a part of the company as a product manager. One of the most important factors that drive the growth and success of the product is how quickly it acquires a large and loyal base of customers. You want these customers to use your product as frequently as possible with minimum friction.
This article uncovers the key differences between qualitative and quantitative data with examples.
To learn how B2B companies solve the problem with key metrics in a product, I caught up with Yuri Brankovsky who has worked in multiple digital products.
SQL is the cornerstone of a wide variety of data-intensive roles, and it is not going anywhere soon. Loads have been written about its usefulness already, so this post is focused on getting your skills from C to A+ for your interviews. No knowledge is assumed, and I feel comfortable promising that your level will be more than enough for what will be asked in interviews if you follow this game-plan.
Google Analytics 4 is set up for better data attribution
This study investigates the Keynesian cross model of a national economy with a focus on the relationship between government spending and economic equilibrium.
In this paper, we proposed a complete end-to-end natural language processing (NLP) approach for hate speech detection in the Algerian dialect.
“So, what do you think?” — says the Product Manager after a product strategy presentation to his team
A brief description of the difference between Data Science and Data Engineering.
Long before dashboards and algorithms, our grandmothers were reading patterns, tracking prices, and making data-driven decisions.
Learn to tackle business problems using data analysis. Follow a hypothesis driven approach to find root causes and actionable insights.
Learn why exposure points can make or break your mobile A/B tests, common pitfalls to avoid, and practical tips to improve your app experimentation results.
Logistics is a sector where mistakes usually cost thousands of dollars at best and client relationships at worst. Here's how to avoid it.
In 2023, it’s important for data scientists to stay on top of the latest trends & advancements in order to remain competitive in the market.
The model should conform to these assumptions to produce a best Linear Regression fit to the data.
Data is increasingly playing a dominant role in business. Know how automating your data catalog can help with efficient data management in 2022.
This article aims to provide a reference for non-tech companies who are seeking to empower their business with data analytics.
A valuable asset for anyone looking to break into the Data Engineering field is understanding the different types of data and the Data Pipeline.
This article describes a large-scale data warehousing use case to provide a reference for data engineers who are looking for log analytic solutions.
Let’s say you want to classify hundreds (or thousands) of documents based on their content and topics, or you wish to group together different images for some reason. Or what’s even more, let’s think you have that same data already classified but you want to challenge that labeling. You want to know if that data categorization makes sense or not, or can be improved.
Learn how public web data can help you improve your deal sourcing methods.
Today, data verification has become one of the greatest assets of an organization.
Discover the essential skills required to become a successful data analyst, including technical tools, analytical abilities, and key competencies for thriving.
Learn how to turn messy healthcare ops data into validated, explainable, and reproducible ML-ready features that hold up in production.
A custom integrated data analytics solution would cost at least $150,000-200,000 to build and implement.
Nowadays, most assertions need to be backed with data, as such, it is not uncommon to encounter data that has been manipulated in some way to validate a story.
Enhance your knowledge and skills in the field of data analytics with the help of data science certification for a rewarding career as a data analyst.
Here are the new features of Power BI (unveiled at the Microsoft Ignite 2021) that can be absolutely beneficial for business users.
Visualize Insights and Discover Driving Features in Lending Credit Risk Model for Loan Defaults
The propagation of artificial intelligence (AI) is making a significant impact on society, changing the way how we work, live, and communicate. AI today is allowing the world to diagnose diseases and develop clinical pathways. It is also being used to match individuals’ skill sets with job openings and create smart traffic that leads to the reduction of pollution. There are many examples of applying AI technologies in the sustainable growth of the planet and organisations.
Learn everything you need to know about Big Data Analytics via these 89 free HackerNoon stories.
In this article, I will be showing you how to build a reliable data pipeline for your small business to improve your productivity and data security.
Big data analytics can be applied for all and any business to boost their revenue and conversions and identify their common mistakes.
Businesses need agile tools to quickly identify and communicate actionable insights for more informed decision-making.
Learn how to safeguard your data from unauthorized transfers with these 4 effective detection and response strategies.
The best data analytics certifications, which will provide you with the right kind of guidance to boost your big data analytics career and to get a great job.
Learn how startups are boosting productivity by integrating AI into their operations, from automating tasks to enhancing decision-making.
What would be the price of our product or service? This question has bothered businesses forever. Several pricing models have been spawned as a result. But the concept of price optimization is a fairly new one. At least to businesses that are not in the hospitality or airline sector.
Data science has been democratized for the most part. AI is now mainstream! It's no longer the exclusive province of large companies with deep pockets.
Data Science and Data Analytics are quite diverse but are related to the processing of Big data. The difference lies in the way they manipulate data.
The goal of the retail merchandising activity is to support a retail strategy that generates value for the customers. The selection of the merchandise and the type of goods are the key of the retail strategies. According to author Michael Levy in Retailing Management, the decision to carry particular merchandise is tactical rather than strategic.
The financial sector is going through massive change. At the heart of this shift lies IoT in Fintech.
Overview of the modern data stack after interview 200+ data leaders. Decision Matrix for Benchmark (DW, ETL, Governance, Visualisation, Documentation, etc)
Apparently, data has become more ubiquitous than the stars in the sky. In fact, the amount of data produced daily via the Internet is set to top 44 zettabytes. As you might assume, that’s more data than you could possibly fathom or use.
How to evaluate product releases without an A/B test. A trustworthy framework using causal inference, Synthetic Control, and rigorous data guardrails.
Player ratings lean on stats, but savvy coaches trust gut - seeing ripple effects stats miss. Post-game numbers often rewrite pre-game predictions.
Learn essential features for efficient data analysis and boost your professional productivity in Excel.
The average wait time on a live chat is roughly 35 seconds, and with every growing second you have an unanswered chat, you're likely to lose a lead.
Growth marketing is a data-driven approach that focuses on acquiring, retaining, and growing a customer base, though there are those who argue that it is flawed
Tips on using ChatGPT across various data analysis tasks, such as for stronger communication, unit economics estimations, and SQL tasks.
Emerging low-code development platforms enable Data Science teams to derive analytical insights from Big Data quickly.
Get your hands on excellent manually annotated datasets with Google Sheets or Python
Wondering how much data scientists make? We're here to help you find out about salaries in Data Science and how they are influenced by various factors.
Though there have been more and more developers and product designers joining Web3.0 world in recent years, it is almost ignored by most of them that they are still using centralized infrastructure — data analytic tools — to build apps and webs. Every minute, project builders are making themselves part of the reason for data breach events, as they have to collect user data intendedly or unintendedly for product improvement.
4/3/2024: Top 5 stories on the HackerNoon homepage!
A hands-on tutorial on how to combine the open-source streaming solution, bytewax, with ydata-profiling, to improve the quality of your streaming flows.
SQL explains the past, but ML drives decisions. Learn how to operationalize machine learning with feature stores, real-time inference, and production monitoring
In this blog, we are gonna perform the analysis on the Meteorological data, and prove the hypothesis based on visualization.
Large Language Models (LLMs) have emerged as a transformative force in business technology, helping address specific business needs.
Reopening businesses after a public health crisis is a complex process that requires a consistent strategy to minimize all possible risks. Contrary to popular belief, it does not amount to disinfection and masks wearing and includes measures like monitoring and data analysis. The following article is an overview of disinfection tunnels and their implications for reopening businesses.
Customer feedback is great. But have you been able to turn that feedback into meaningful customer insights? A few years back, brands depended on surveys to gauge customers’ feelings about how their products were performing.
The 5 things every data analyst should know and why it is not Python, nor SQL
To collect the data, we used a novel algorithmic auditing approach based on large-scale simulation of user browsing behavior via virtual agents
The natural language processing approaches can be applied to the climate change domain as well for finding the causes and leveraging patterns.
Table of Contents
Data-driven investing is reshaping the landscape of the financial markets, particularly in the realm of stock investments.
I tracked my mental health each day throughout year. I rated my happiness on a scale of 1–5, with “1” being a really bad day, “2” being a kind of bad day, “3” being a neutral day, “4” being a kind of good day, and “5” being a really good day. I want to preface this article by saying that I understand how complex and difficult it is to try and quantify mental health. My absurdly simple, completely subjective, and inherently biased rating system is by no means an attempt to accurately represent the complexities of the mental health spectrum.
A natural experiment is a type of causal analysis that has been widely adopted by many organizations and research fields. How can you use them?
How do my users move in this city? Where do they go? What does the “flow” of this city look like? How does that change throughout the day?
This user behavior report is based on users’ orders from Alibaba between November 25th, 2017, and December 3rd, 2017 from the Alibaba platform…
Gone are the days when journalists simply had to find and report news.
Embracing the changing face of work: understanding automation's influence and equipping yourself with skills for the future.
Building a biomedical knowledge graph using publicly available datasets to better aid disease research and biomedical data modelling.
By now you must have read quite a few articles about Snowflake’s absolutely mind-blowing and record-setting IPO. This article is not intended to speculate on whether the valuation makes sense or not, but rather help you understand the technological concepts that make Snowflake so unique, and why it has proven to be so disruptful for the data space in general and the data warehousing space in particular.
Below you can find the article of my colleague and Big Data expert Boris Trofimov.
In today’s competitive business landscape, data automation has become necessary for business sustainability. Despite the necessity, it also comes with a few challenges--collecting, cleaning, andputting it together--to get meaningful insights.
What can be done to prevent “Broken Windows” in the primary data source? How can we effectively fix existing “Broken Windows"?
This article gives insight into some basic features and functionality that a desirable modern BI software has and illustrated some examples.
Learn 3 simple, effective methods to detect and handle outliers in your data. Improve analysis accuracy and make smarter decisions with clean datasets.
Explore how ChatGPT analyzes meme emotions in this study on AI-driven sentiment analysis of social media content.
In this paper, we proposed a complete end-to-end natural language processing (NLP) approach for hate speech detection in the Algerian dialect.
In this paper, I used data from two different data sources and merged them together in the Tableau layer to perform the data analysis.
In this paper, we proposed a complete end-to-end natural language processing (NLP) approach for hate speech detection in the Algerian dialect.
A decade and half ago, when I first started getting interested in public markets investing, I had started by making a small portfolio that I could play with. Just to see if I actually knew what I thought I knew, and to try and make sense of my beliefs about the market. And no surprise, I didn't know what I thought I knew.
This article discusses the security risks of using auto-increment fields in API responses and methods to prevent data leaks and protect business metrics.
How to make your data work harder for you in marketing
Discover the power of data visualization with Plotly in Python. Learn to transform raw data into interactive, insightful visuals and create dynamic dashboard
In the business world we have to do research and analysis to get to know what users/market have been looking for. So, here is one of our online analytics regarding the apps that are searched by users. We have been doing research on our test website name https://flutterappworld.com/ and i found that these imagine stat that help you to build apps with target markets that have most searches through Google. This stat is based on Google Website and Google analytics data.
Wouldn’t it be great to bring the time needed to build a new data integration connector down to 10 minutes? This would definitely help address the long tail of
What work does a data engineer actually do? Let me tell you one thing: it’s not what you think they should be doing, especially not the part where they are running around collecting data for you or building yet another one of those dashboards that will only be used for a few weeks.
Investors need good data to make good decisions, and new AI platforms will provide deeper analysis
Google announced that it would ban the usage of third-party cookies; it has made a lot of publishers afraid that they won't be able to utilize user data.
The Market analysis of Google Shopping for Sports & Outdoor Category. The insights consists of Top retailers, brands, products and price changes.
Learn the many different ways you can scan for personal data across your organisation's customer-facing online systems in this practical, how-to guide.
This study investigates the Keynesian cross model of a national economy with a focus on the relationship between government spending and economic equilibrium.
Por favor clic el artículo original：http://www.octoparse.es/blog/70-fuentes-de-datos-gratuitas-en-2020
Why improvisation kills research, and how to use AI to enforce methodological discipline.
Dietary recall questionnaires provide a wealth of information that can be analysed to uncover patterns and correlations.
Raw data coming in from various sources is often inherently dirty data, rife with factual errors, typos and inaccuracies. Left unattended, this data becomes a nightmare. Imagine having to pull a report only to realize it has duplicated data – not to mention half of them don’t even have valid phone numbers or addresses. Your boss is not going to be happy.
Large language models like ChatGPT are making it easier to manage data. Akkio has come up with an LLM-based tool to manage tabular data using conversational AI.
We wonder: Is anyone actually reading this? Does this dashboard change anything?
In this paper, we proposed a complete end-to-end natural language processing (NLP) approach for hate speech detection in the Algerian dialect.
A real-time processing architecture should have these logical components to address event ingestion & processing challenges, such as a stream processing system.
While the number of product management roles in the US has grown by more than 30% in two years, according to LinkedIn, the responsibilities of the job are morphing.
The implementation of AI in ecommerce should come as no surprise. Online businesses have always been quick to adopt new technologies, and this is how the industry thrives; enhancing the customer experience, discovering new markets, and driving further sales. And with the continued development of AI technology like chatbots, visual search, and personalized recommendations, the world of ecommerce is transforming again.
Are you a newbie when it comes to Data Analysis and Data modelling? If yes, then you are in the right place.
Be profitable from the first minute with dynamic pricing adapted to each market
Explore how ChatGPT analyzes meme emotions in this study on AI-driven sentiment analysis of social media content.
Learn what social media data visualization is and why it is important.
In this paper, we proposed a complete end-to-end natural language processing (NLP) approach for hate speech detection in the Algerian dialect.
Learn the differences between JSON, YAML, and Jinja, and how to choose the right tool for your project.
The UK government is pushing for a “zonal attachment” model, where quotas would be carved up relative to the abundance of fish in each country’s waters.
From simplifying data collection to enabling data-driven feature development, Customer Data Platforms (CDPs) have far-reaching value for engineers.
In the tire manufacturing industry, conventional tools are no longer enough for handling the complexities of product data and can seriously damage your brand
Enhance lead scoring with iPhone Lookup—check iMessage & FaceTime activity to ensure valid, reachable phone numbers.
Explore how ChatGPT analyzes meme emotions in this study on AI-driven sentiment analysis of social media content.
This study investigates the Keynesian cross model of a national economy with a focus on the relationship between government spending and economic equilibrium.
After defining what the RFM analysis is standing for, and how you can apply it to your Customer Base, I want to show you how to apply it on Shopify orders data.
Explore how ChatGPT analyzes meme emotions in this study on AI-driven sentiment analysis of social media content.
In this blog, we will look at what a data deduplication software is, the most crucial features and functionalities found in such a tool, and how it can help you
Here are some things that you can do when a MAP violation occurs when you least expect it.
Data analytics offers you amazing capabilities to grow your business. Leverage the power of these amazing data analytics hacks to reach your business goals.
The natural language processing approaches can be applied to the climate change domain as well for finding the causes and leveraging patterns.
This article describes our analyses’ data sources, methodologies, findings, and limitations.
Artificial intelligence (AI) and data analytics are rapidly growing trends in the tech world. With increasing potential for innovation, it is paramount that we stay up to date with all the latest developments in this field. According to MarketsandMarkets, the worldwide artificial intelligence (AI) market will increase from USD 58.3 billion in 2021 to USD 309.6 billion by 2026, at a compound annual growth rate (CAGR) of 39.7 percent over the projected period. It seems that every company wants a piece of this growing pie. By 2022 it is expected that 90% of companies will be using some form of artificial intelligence for data analytics purposes.
With so much data available at your fingertips, if you fail to implement a strong system, your business is at risk of suffering from big data burnout.
Many NGOs and archival projects excel in accumulating historical records but often neglect the equally important tasks of processing and presenting data.
This post provides reference for what you should take into account when choosing an OLAP engine in a financial scenario.
Find out what it means to be a data scientist at Amazon! Their salaries, roles and required experience, types of data positions, and interview process.
Explore how ChatGPT analyzes meme emotions in this study on AI-driven sentiment analysis of social media content.
Explore how AI language models create coherent content through statistical processes, contrasting AI's approach with human cognition and examining its potential
The natural language processing approaches can be applied to the climate change domain as well for finding the causes and leveraging patterns.
Embedded data analytics and reporting tools that empowers Business analysts
The natural language processing approaches can be applied to the climate change domain as well for finding the causes and leveraging patterns.
Explaining key components like Vietoris-Rips complexes, persistence diagrams, and Wasserstein distance, highlighting why TDA is a robust tool for analyzing
Yes, Let’s dive into the details.
The popularity of online virtual data rooms has increased over the years. These are innovative software used for safe storage and sharing of files. As the world is modernizing, people are using advanced technology to carry out their daily tasks. As everything today is digital, it becomes more and more crucial to look for new methods to store files. Gone are the days when people used to pile up hard copies of all the files in the offices. Some people are still seen doing that which wastes half of their time. Imagine you have a business meeting in some time and you can’t find a specific file because there is a huge unorganized bundle of files in your office. With virtual data rooms, all your files are well organized. You do not have to get into a hassle of finding a certain file. With just one click, the file appears in front of you in no time.
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How much data does a hospital produce each day? How much information are they capable of storing, analyzing, and sharing with physicians and patients?
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This study investigates the Keynesian cross model of a national economy with a focus on the relationship between government spending and economic equilibrium.
5/23/2024: Top 5 stories on the HackerNoon homepage!
The natural language processing approaches can be applied to the climate change domain as well for finding the causes and leveraging patterns.
The natural language processing approaches can be applied to the climate change domain as well for finding the causes and leveraging patterns.
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With the advent of data socialization and data democratization, many organizations organize, share and make information available to all employees in an efficient manner. While most organizations benefit from liberal use of such a source of information available to their employees, others struggle with the quality of the data they use.
Luke Calton is Noonies Nominee and a Product Manager at a start-up.
To determine how testing protocols for COVID-19 vary across the United States, we sent requests under public records laws to all 50 states, New York City, and Washington, D.C. The requests were sent to health departments the week of March 16 and were identical. The database below contains responses we have received, as well as publicly available guidance from some jurisdictions.
Learn how to create and manipulate pandas Series, versatile data structures for efficient data handling in Python. Explore labeled and unlabeled formats,
Visit the /Learn Repo to find the most read blog posts about any technology.
2026-04-30 03:45:08
\ AI governance rarely fails because organisations lack policies. It fails because those policies behave like ceremonial artefacts while delivery pipelines keep moving at production speed. Somewhere between a neatly written PDF and a deployed model, intent evaporates.
The result is familiar: teams improvise, exceptions multiply, and governance becomes a negotiation rather than a system. In high-stakes environments, especially healthcare and life sciences, that gap is not just inconvenient. It is an operational risk.
The idea behind Governance That Ships is deceptively simple: governance should behave like software. It should have inputs, outputs, enforcement points, and observable results. It should run continuously, not quarterly and most importantly, it should produce evidence as a byproduct of doing the work, not as a separate ritual.
Governance becomes real only when it is embedded into the mechanics of delivery.
At the core of this approach is a pipeline that feels almost mechanical:
This is not a theoretical framework. It mirrors how mature security and compliance systems already operate. Controls are not suggestions, they are gates. Evidence is not documentation, it is exhaust. Metrics are not vanity dashboards, they are feedback loops.
The shift here is subtle but powerful. Governance stops being something teams “comply with” and becomes something the system does automatically.
If a control cannot produce evidence without manual effort, it is not a control. It is a hope.
Not every AI system deserves the same level of scrutiny. Treating them equally is how organisations either slow to a crawl or expose themselves unnecessarily.
A practical governance system introduces risk tiers that determine the intensity of controls:
| Tier | Description | Typical Controls | |----|----|----| | Minimal | Internal tools, low impact, no sensitive data | Basic registration, lightweight checks | | Limited | User-facing, moderate risk, content or automation | Documentation, prompt review, security testing | | High | Regulated or high-impact decisions | Formal risk assessment, strict change control, audit logging | | Prohibited | Unacceptable use cases | Blocked at design and deployment |
This structure aligns naturally with regulatory thinking and risk management frameworks. It also gives engineering teams something they crave: clarity.
Instead of asking “What should we do?”, teams ask “Which tier is this, and what does that trigger?”
Good governance removes ambiguity. Great governance removes debate.
Policies written in documents are advisory. Policies encoded into pipelines are executable.
This is where policy-as-code enters the scene. The same way infrastructure is validated before deployment, AI systems can be gated by rules that check:
These checks run automatically during CI/CD. They do not wait for a committee meeting. They do not depend on memory or goodwill.
The pattern is already well understood in engineering ecosystems. Tools like Open Policy Agent demonstrate how rules can be versioned, reviewed, and enforced consistently.The safest system not only has the best policies, but techniacally unable to break them.
In traditional software, quality is enforced through tests. AI governance should behave the same way.
Instead of abstract requirements, governance becomes a set of executable jobs:
This transforms governance into something tangible. A failing governance requirement looks exactly like a failing test. It blocks the release.
This approach also aligns with established practices in ML production readiness, where systems are evaluated continuously rather than assumed to be correct.
If governance cannot fail a build, it cannot protect production.
GenAI systems introduce risks that traditional governance models were not designed for, like prompt injection, output manipulation, and tool misuse. These are not edge cases, they are structural properties.
Effective governance must therefore include controls tailored to LLM behaviour:
These are not theoretical constructs. They directly map to known vulnerability classes documented in frameworks like OWASP for LLM applications. LLM governance is less about what the model knows and more about what the system allows it to do.
One of the most underappreciated aspects of governance is evidence. Auditors do not trust intent. They trust records.
In a system that ships governance, evidence is generated automatically:
These artefacts are not created for audits. They are created because the system requires them to function. This aligns with management system standards where organisations must demonstrate control through documented processes and records.
The strongest audit position is achieved when evidence already exists before anyone asks for it.
There is a persistent myth that governance and speed are opposites. In practice, poorly designed governance slows teams down. Well-designed governance removes friction.
By standardising controls, automating checks, and clarifying expectations, teams spend less time negotiating and more time building. Decisions become predictable. Releases become safer.
And perhaps most importantly, governance scales. It no longer depends on a handful of experts reviewing everything manually. It becomes part of the system’s DNA.
The real goal of governance is not control, it’s a momentum without chaos.
The most elegant governance systems share a common trait - they do not force teams to behave correctly. They make correct behaviour the easiest path.
When policies are encoded into tools, when controls are invisible but effective, when evidence flows naturally, governance stops feeling like oversight and starts feeling like infrastructure. In that moment, governance stops being something you enforce, it becomes something you rule.
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