#Data Mining

Comment “DATA” for all projects & links! #coding #datascience #machinelearning #university #student

Day24/365 of making ₹0 to ₹10Cr (Data Miner) #startuplife #business #enterprenuership #sales

Performing joins especially with large datasets will be a huge challenge in data processing. Here is the fix. 👇 1️⃣ Make a broadcast join Instead of shuffling 50TB of data across the network to find matches, you should send a copy of the small table to every single worker node. 2️⃣ Map-Side Operation This converts the operation into a local lookup. Each executor holds the full 100MB table in RAM and joins it against its local slice of the 50TB data. 3️⃣ The Memory Trap Be careful -> if that “small” table grows too big (e.g., 2GB), broadcasting it will cause Out-Of-Memory (OOM) errors on the executors and crash the application. 4️⃣ Configuration Threshold Check the spark.sql.autoBroadcastJoinThreshold. If the table is slightly larger than the default (usually 10MB), the system might default to a slow Sort-Merge join unless I increase this limit. #dataengineering #bigdata #coding 🏷️ Data Engineering, Apache Spark, Coding Interview, Tech Interview, Big Data Processing, Spark, Python

A data warehouse is a single source of truth that helps business functions perform their data analysis operations easier. Here's what a simple data warehouse looks like: 1. Data sources 2. Bronze layer 3. Silver layer 4. Gold layer 5. Analytics There's so much more that goes into a data warehouse (e.g. ingestion frequency, data governance policies, data validation checks etc), but this is a high level design you can start with. Different companies may configure the stages in different ways according to their users' unique requirements, but the generic workflow applies to all! #dataanalytics #dataengineering #datascience #techtok #dejavu

comment “AI” for my full synthetic data tutorial Youtube video! save for later & follow for more! Save for later & follow for more! You can customize any dataset for any industry, business problem, or project and get way more interesting data than Kaggle. Plus, you can ask for imperfect data with inconsistent values, duplicates, or nulls to make it feel more realistic to the real world. You just have to know how to specify your requirements and constraints when prompt engineering. Here’s what you should specify: ✨ size of dataset(s) (rows / columns) ✨ column names and data types ✨ primary keys and foreign keys ✨ distribution and allowed values ✨ variation of datapoints ✨ downloadable as CSVs ✨ anything else that may impact your project! Full example below: You are a data engineer generating a realistic synthetic dataset for [INDUSTRY] and [PROJECT TYPE OR PURPOSE].Can you generate [NUMBER] realistic datasets with the following requirements.Create an [TABLE NAME] table with [ROW COUNT] rows and columns: [LIST REQUIRED COLUMNS], plus any additional realistic columns you think would be useful. [PRIMARY KEY] is the primary key. [FOREIGN KEY 1] and [FOREIGN KEY 2] are foreign keys that connect to the [RELATED TABLE NAME] table. Ensure that [NUMBER] foreign key values exist in the related table but do not appear in this table (to simulate missing relationships).Create a [DIMENSION TABLE NAME] table with [ROW COUNT] rows and columns: [LIST REQUIRED COLUMNS], plus any additional realistic columns. [PRIMARY KEY] is the primary key and connects to the first table. Ensure that [NUMBER] records in this table have no matching rows in the first table.For both tables, include high variation across values, non-even category distributions, and realistic data patterns. All ID fields should be random numeric values only (no letters).[Add in any other requirements, constraints, or behavior rules]Return each table as a separate, downloadable CSV file. Have you tried this hack and said goodbye to Kaggle yet?

The 3 mining options. Choose your fighter. #bitcoin #web3 #evervalue #mining

Comment "projects" to get the links in your DMs!🚀 . . [Data analytics, data analyst, projects, career, data analytics career] #dataanalytics #dataanalyst #careertips #project #datascience #guide

Data Engineers work tirelessly behind the scenes to build the infrastructure for data projects. However, their efforts often remain invisible to business users, who focus on the end product and reward Data Scientists and Analysts with more recognition! #dataengineering #azure #pyspark #dataengineer #azuredataengineer #data #aws #gcp #azuredatabricks #dataanalyst #datascientist #datascience

Hidden ore losses and wasted hours impact mining. Embrace AI and IIoT for real-time insights! ⛏️📊 Peytec’s MuckPuck tags ensure efficient tracking from face to mill, maximizing yield! 🚀✨ #mining #IIoT #AI #OreTracking #Reconciliation #Technology #Wireless

It’s so useful! 🤗 Comment data and I’ll send you the link!

Perbandingan Data Scientist vs Data Analyst vs Data Engineer 🧑‍💻 Cari kerja itu tidak gampang, per November 2025, jumlah orang menganggur ada sekitar 7.35 juta orang. Akan tetapi, @dibimbing.id bisa bantu kamu persiapan semua yang dibutuhkan dari awal sampai salurin ke hiring partner. Komen "MAU" untuk dapetin trial class secara GRATIS! Btw, kamu tipe karir data yang mana nihh, coba vote 👇 #PulangBawaCerita #dataanalytics #dataengineering #datascience #artificialintelligence

Data is new Oil in New World || Your Data will be used for training next Gen AI #data #privacy #oil