#Data Lineage

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#Data Lineage Reel by @dataengineeringwithnishchay - Stop Ignoring Data Governance! ๐Ÿšจ You're NOT a Data Engineer Without This Data Lineage Observability ๐Ÿš€ Want to master Data Governance and become a
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@dataengineeringwithnishchay
Stop Ignoring Data Governance! ๐Ÿšจ Youโ€™re NOT a Data Engineer Without This Data Lineage Observability ๐Ÿš€ Want to master Data Governance and become a top Data Engineer? In this video, we break down everything you need to know about: โœ”๏ธ Data Lineage ๐Ÿ”— โœ”๏ธ Data Observability ๐Ÿ‘€ โœ”๏ธ Data Quality & Monitoring ๐Ÿ“Š โœ”๏ธ Top Data Governance Tools ๐Ÿ› ๏ธ โœ”๏ธ Real-world Data Pipeline Use Cases ๐Ÿ’ก Whether youโ€™re a beginner or experienced engineer, this guide will help you build production-ready data systems. ๐ŸŽฏ Who is this for? Data Engineers Backend Developers transitioning to Data Analytics Engineers Anyone working with data pipelines ๐Ÿ”ฅ 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 โ€œData Engineering โ€ and Iโ€™ll share the complete roadmap
#Data Lineage Reel by @nataindata (verified account) - ๐Ÿ˜ˆVery bad advice on keeping your Data Lake swampy ๐Ÿธย Load Data Multiple Times I can load the data whenever I want, right? Wrong. When it comes to l
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@nataindata
๐Ÿ˜ˆVery bad advice on keeping your Data Lake swampy ๐Ÿธย 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. ๐Ÿธย 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. Itโ€™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 ๐Ÿธย 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 donโ€™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. ๐Ÿธย Throw all the data in Organisations dump all company-related data into their data lakes โ€“ 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 โค๏ธโ˜บ๏ธ #data #datascience #dataengineer #datascientist #bigdata #softwareengineer #programming #datalake #cloudcomputing
#Data Lineage Reel by @bigdatabysumit - ๐€ ๐ƒ๐ž๐ฌ๐ข๐ ๐ง ๐๐š๐ญ๐ญ๐ž๐ซ๐ง ๐ญ๐ก๐š๐ญ ๐ก๐š๐ฌ ๐ฌ๐ข๐ฆ๐ฉ๐ฅ๐ข๐Ÿ๐ข๐ž๐ ๐ญ๐ก๐ž ๐ฐ๐š๐ฒ ๐๐ข๐  ๐ƒ๐š๐ญ๐š ๐œ๐š๐ง ๐›๐ž ๐ก๐š๐ง๐๐ฅ๐ž๐! Yes, you guessed it rig
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@bigdatabysumit
๐€ ๐ƒ๐ž๐ฌ๐ข๐ ๐ง ๐๐š๐ญ๐ญ๐ž๐ซ๐ง ๐ญ๐ก๐š๐ญ ๐ก๐š๐ฌ ๐ฌ๐ข๐ฆ๐ฉ๐ฅ๐ข๐Ÿ๐ข๐ž๐ ๐ญ๐ก๐ž ๐ฐ๐š๐ฒ ๐๐ข๐  ๐ƒ๐š๐ญ๐š ๐œ๐š๐ง ๐›๐ž ๐ก๐š๐ง๐๐ฅ๐ž๐! Yes, you guessed it right! ๐“๐ก๐ž ๐Œ๐ž๐๐š๐ฅ๐ฅ๐ข๐จ๐ง ๐€๐ซ๐œ๐ก๐ข๐ญ๐ž๐œ๐ญ๐ฎ๐ซ๐ž logically organizes and improves the structure and quality of data as the data progresses through the different layers. This architecture, also known as ๐Œ๐ฎ๐ฅ๐ญ๐ข-๐ก๐จ๐ฉ ๐š๐ซ๐œ๐ก๐ข๐ญ๐ž๐œ๐ญ๐ฎ๐ซ๐ž, 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 ๐Ÿฅ‰๐“๐ก๐ž ๐๐ซ๐จ๐ง๐ณ๐ž ๐ฅ๐š๐ฒ๐ž๐ซ 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. ๐ŸฅˆIn ๐ญ๐ก๐ž ๐’๐ข๐ฅ๐ฏ๐ž๐ซ ๐ฅ๐š๐ฒ๐ž๐ซ 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. ๐Ÿฅ‡๐“๐ก๐ž ๐†๐จ๐ฅ๐ ๐ฅ๐š๐ฒ๐ž๐ซ 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. ๐Ÿ“Want to get a better understanding on Big Data โ“ ๐Ÿ’ปCheck my official website ๐ŸงทLink in the Bio! #dataengineering #databricks #datascience #dataengineers #bigdatatechnologies #bigdata
#Data Lineage Reel by @jessramosdata (verified account) - comment "AI" for my full synthetic data tutorial Youtube video! save for later & follow for more! Save for later & follow for more! You can customiz
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@jessramosdata
comment โ€œAIโ€ 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. Hereโ€™s what you should specify: โœจ size of dataset(s) (rows / columns) โœจ column names and data types โœจ primary keys and foreign keys โœจ distribution and allowed values โœจ variation of datapoints โœจ downloadable as CSVs โœจ 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?
#Data Lineage Reel by @machgorithm - Types of Data Structure . Video by @codingwithjd . . . #coding #cppproject #cplusplusprogramming #codinglife #codingbootcamp #codingisfun #codingninj
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@machgorithm
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
#Data Lineage Reel by @vidyangi - From Engineer to Unicorn CEO- @cyberhaveninc My conversation with @Nishantdoshi ๐ŸŽ™๏ธ๐Ÿš€ I recently had the opportunity to sit down with Nishant Doshi, a
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@vidyangi
From Engineer to Unicorn CEO- @cyberhaveninc My conversation with @Nishantdoshi ๐ŸŽ™๏ธ๐Ÿš€ I recently had the opportunity to sit down with Nishant Doshi, and Iโ€™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 โ€œwhyโ€ behind his transition from engineer to founder, the future of the โ€œAI cat and mouse gameโ€ 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, itโ€™s easy to see why they are growing so fast. Full podcast will be out shortly #Cybersecurity #Podcast #Leadership #Cyberhaven #TechFounders DataSecurity
#Data Lineage Reel by @aiwithpj - 2026 Data Engineer Roadmap ๐Ÿš€ (0 โ†’ Job Ready) Want to become a Data Engineer? Start with Python & advanced SQL โ†’ learn databases & data modeling โ†’ m
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@aiwithpj
2026 Data Engineer Roadmap ๐Ÿš€ (0 โ†’ Job Ready) Want to become a Data Engineer? Start with Python & advanced SQL โ†’ learn databases & data modeling โ†’ master ETL pipelines โ†’ work with big data tools like Spark & Kafka โ†’ deploy on cloud platforms. This roadmap covers: Python โ€ข SQL โ€ข ETL โ€ข Airflow โ€ข dbt โ€ข Spark โ€ข Kafka โ€ข AWS/GCP โ€ข Data Warehousing โ€ข Real-time pipelines. Perfect for students, developers, and anyone entering data engineering. Save this reel & start building data pipelines today ๐Ÿ“Š๐Ÿ”ฅ #DataEngineer #DataEngineering #BigData #ETL #AIwithPJ
#Data Lineage Reel by @data_pumpkin - Ever wondered why data scientists are obsessed with log transformations? It's not just math-it's magic for messy data! From taming skewed distribution
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@data_pumpkin
Ever wondered why data scientists are obsessed with log transformations? Itโ€™s not just mathโ€”itโ€™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 trafficโ€”all of these get easier with logs. But hereโ€™s the twist: theyโ€™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 belowโ€”letโ€™s talk data! ๐Ÿš€๐Ÿ“Š #DataScience #StatisticsMadeSimple #DataVisualization #MachineLearning #LogTransformations #DataAnalysisTips #AnalyticsExplained #StatQuestInspired #LearnDataScience #DataScient
#Data Lineage Reel by @phdwithgrace_ - 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 downl
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@phdwithgrace_
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
#Data Lineage Reel by @hashem.alghaili (verified account) - Your DNA could be hacked: experts warn next generation sequencing may be a prime cyberattack target.
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@hashem.alghaili
Your DNA could be hacked: experts warn next generation sequencing may be a prime cyberattack target.
#Data Lineage Reel by @tapilinaelina - 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
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@tapilinaelina
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 ๐Ÿคช #dna #dnatest #ethnicity #race #myheritage

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