#Data Lineage

Stop Ignoring Data Governance! 🚨 You’re NOT a Data Engineer Without This Data Lineage Observability 🚀 Want to master Data Governance and become a top Data Engineer? In this video, we break down everything you need to know about: ✔️ Data Lineage 🔗 ✔️ Data Observability 👀 ✔️ Data Quality & Monitoring 📊 ✔️ Top Data Governance Tools 🛠️ ✔️ Real-world Data Pipeline Use Cases 💡 Whether you’re a beginner or experienced engineer, this guide will help you build production-ready data systems. 🎯 Who is this for? Data Engineers Backend Developers transitioning to Data Analytics Engineers Anyone working with data pipelines 🔥 Why Data Governance matters? Without governance, your data becomes unreliable, inconsistent, and unusable at scale. Learn how companies ensure trust, compliance, and scalability. follow @dataengineeringwithnishchay for data engineering content & interview experience #dataengineering #video #viral #reels #reelsinstagram Comment “Data Engineering ” and I’ll share the complete roadmap

😈Very bad advice on keeping your Data Lake swampy 🐸 Load Data Multiple Times I can load the data whenever I want, right? Wrong. When it comes to loading small tables and files, it is not difficult, but as the file size increases, loading these can become a problem as it will take more time. One can minimise the time it takes to load large source data sets by loading the entire data set once, and later merging and syncing the changes in the data lake. 🐸 Do Not Catalog The Data On Ingest Loading the data into whatever place and leaving it to catalogue for the future? Ohh yeees. I mean oh no. It’s a big mistake. This is because cataloguing the data from the data lake after some time has passed will prove to be difficult and time-consuming. Organise everything properly from the beggining 🐸 Data Lineage and Data Government are for babies. Different people might clean or start integrating data with other data sets. So there are chances that the data might have already been cleaned, but others will have to redo the work as they don’t know about it. To avoid this problem, document the changes related to the data thoroughly and implement solid governance processes on how it was used and transformed. 🐸 Throw all the data in Organisations dump all company-related data into their data lakes – this should not be done. Start With Project-Specific Data. While the point of having a data lake is to have all company-related information in one place, the answer is to not turn it into a swamp by striking the right balance. Liked it? Press ❤️☺️ #data #datascience #dataengineer #datascientist #bigdata #softwareengineer #programming #datalake #cloudcomputing

𝐀 𝐃𝐞𝐬𝐢𝐠𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧 𝐭𝐡𝐚𝐭 𝐡𝐚𝐬 𝐬𝐢𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐝 𝐭𝐡𝐞 𝐰𝐚𝐲 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐜𝐚𝐧 𝐛𝐞 𝐡𝐚𝐧𝐝𝐥𝐞𝐝! Yes, you guessed it right! 𝐓𝐡𝐞 𝐌𝐞𝐝𝐚𝐥𝐥𝐢𝐨𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 logically organizes and improves the structure and quality of data as the data progresses through the different layers. This architecture, also known as 𝐌𝐮𝐥𝐭𝐢-𝐡𝐨𝐩 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞, has positively impacted the way data is stored and processed. Databricks provides tools that allow users to instantly build data pipelines with just few lines of code with Bronze, Silver and Gold layers - that constitutes the Medallion Architecture 🥉𝐓𝐡𝐞 𝐁𝐫𝐨𝐧𝐳𝐞 𝐥𝐚𝐲𝐞𝐫 is where we land all the data from external source systems. The focus in this layer is to quickly capture the Data changes and to provide an historical archive of source (cold storage), data lineage, auditability, reprocessing if needed without rereading the data from the source system. 🥈In 𝐭𝐡𝐞 𝐒𝐢𝐥𝐯𝐞𝐫 𝐥𝐚𝐲𝐞𝐫 of the lakehouse, the data from the Bronze layer is Filtered, matched, merged, conformed and cleansed. In the data engineering paradigm, typically the ELT methodology is followed vs ETL. Which means only minimal transformations and data cleansing rules are applied while loading the data to the Silver layer. 🥇𝐓𝐡𝐞 𝐆𝐨𝐥𝐝 𝐥𝐚𝐲𝐞𝐫 is for reporting and uses more de-normalized and read-optimized data models with fewer joins. The final layer of data transformations and data quality rules are applied here. So you can see that the data is curated and the quality improves as it moves through the different layers. For more of such interesting content on Big Data Technologies, follow @bigdatabysumit PS ~ New batch of my Ultimate Big Data Masters Program (Cloud Focused) and Elite Data Engineering Program (Cloud Focused) is starting on 27th July 2024. DM to know more! I have trained over 20,000+ professionals in the field of Data Engineering in the last 5 years. 📍Want to get a better understanding on Big Data ❓ 💻Check my official website 🧷Link in the Bio! #dataengineering #databricks #datascience #dataengineers #bigdatatechnologies #bigdata

Data Types

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?

Types of Data Structure . Video by @codingwithjd . . . #coding #cppproject #cplusplusprogramming #codinglife #codingbootcamp #codingisfun #codingninjas #coder #coderlife #coderslife #codersofinstagram #programming #programmingproblems #programmers #codingdays #codingchallenge #assembly #instagramgrowth #asciiart #cmd #cmdprompt #batchprocessing #aiartcommunity #artificialintelligence #deepseek #openai #meta #metaverse

From Engineer to Unicorn CEO- @cyberhaveninc My conversation with @Nishantdoshi 🎙️🚀 I recently had the opportunity to sit down with Nishant Doshi, and I’m still buzzing from the conversation. Nishant is currently CEO at Cyberhaven, leading the company after its recent $100M Series D raise and $1B valuation. But his story goes so much deeper than the current headlines. We unpacked his incredible journey from a Symantec engineer who discovered a massive data leak affecting 100,000 apps at a large company to a two-time founder who exited companies to Palo Alto Networks and Harness. We dove into the “why” behind his transition from engineer to founder, the future of the “AI cat and mouse game” in security, and why Data Lineage is the breakthrough the industry has been waiting for. On a personal note, I had an absolute blast working with the Cyberhaven team to make this happen. When you see the culture and the technology they are building in the Data Detection and Response (DDR) space, it’s easy to see why they are growing so fast. Full podcast will be out shortly #Cybersecurity #Podcast #Leadership #Cyberhaven #TechFounders DataSecurity

2026 Data Engineer Roadmap 🚀 (0 → Job Ready) Want to become a Data Engineer? Start with Python & advanced SQL → learn databases & data modeling → master ETL pipelines → work with big data tools like Spark & Kafka → deploy on cloud platforms. This roadmap covers: Python • SQL • ETL • Airflow • dbt • Spark • Kafka • AWS/GCP • Data Warehousing • Real-time pipelines. Perfect for students, developers, and anyone entering data engineering. Save this reel & start building data pipelines today 📊🔥 #DataEngineer #DataEngineering #BigData #ETL #AIwithPJ

Ever wondered why data scientists are obsessed with log transformations? It’s not just math—it’s magic for messy data! From taming skewed distributions to stabilizing variance, logs are the unsung heroes of data analysis. Think about it: predicting house prices, analyzing income, or visualizing website traffic—all of these get easier with logs. But here’s the twist: they’re not a one-size-fits-all solution. Curious to know when to use them and when to skip them? Watch this reel and level up your data game! Have you used log transformations before? Drop your experiences below—let’s talk data! 🚀📊 #DataScience #StatisticsMadeSimple #DataVisualization #MachineLearning #LogTransformations #DataAnalysisTips #AnalyticsExplained #StatQuestInspired #LearnDataScience #DataScient

New to RNA-seq data? Follow this step by step guide with programs to use to quantify your data. Once samples have been sequenced, you receive or download FASTQ files. They are large, raw sequencing files that need to be processed through a multi-step RNA-seq pipeline to ultimately generate gene expression counts. Manuals or the GitHub pages exist for each program to follow along #rnaseq #bioinformatics #phdjourney #biotech

Your DNA could be hacked: experts warn next generation sequencing may be a prime cyberattack target.

I took another one DNA test with @myheritage_official , and they have this new feature that looks at how your genes match with ancient people from the middle ages, the Roman empire, iron and bronze age funny way to spend your adult money 🤪 #dna #dnatest #ethnicity #race #myheritage