@jade_grobler: Getting serenaded in croatia, wait for it

Jade grobler

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Saturday 30 July 2022 18:45:29 GMT

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Tiwi Romelo :

hi babe😝

2022-07-30 19:01:39

0

troutflyhighguy :

Whyd u run and hide?

2022-07-30 19:02:38

0

User1977010101 :

Nice…. 🥰😏

2022-07-30 20:51:50

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user4645976775168 :

🥰🥰🥰🥰

2022-09-07 23:54:18

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VMV Homes :

🥰😁

2023-01-08 09:27:11

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cierto 🥲

Can you trust the data before engineering it❓ No ❌ Data Gathering, cleaning, querying, analyzing is one of the most important aspect when dealing with data. Working with data requires more than simply applying select on the tables. Data is the key and #SQL is the fundamental skill for any data engineer. So, where there's data there's loads of querying. Data Engineering often traverses through various stages while building a data pipeline going through lots of data level decisions. ✅Each stage from Ingestion of data to delivering the data to end consumer, the phase travels through a lot of technical nuances and process it by cleaning, loading and transforming it. ♐️Let's explore the various stages of a data pipeline and understand the importance of each phase - - 𝐃𝐚𝐭𝐚 𝐜𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧: Collection of raw data from various sources, such as databases, sensors, or surveys. - 𝐃𝐚𝐭𝐚 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠: Ingesting data through various sources and preparing it for analysis by handling duplicates, errors, and missing values. - 𝐃𝐚𝐭𝐚 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: Transforming data into a reusable format using various compute approaches. - 𝐃𝐚𝐭𝐚 𝐬𝐭𝐨𝐫𝐚𝐠𝐞: Storing processed data in a synchronous format for future use. - 𝐃𝐚𝐭𝐚 𝐂𝐨𝐧𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧: Analyzing, modelling data to identify patterns, trends, and relationships to generate business insights. ➡️Few of the best practices recommended for data pipelines include: ✔️Auditing and data lineage tools to monitor data movement and guarantee data governance. ✔️Constructing scalable and fault-tolerant structures to manage massive data volumes. ✔️Using appropriate logging, metrics, and alarms, monitoring pipeline performance, data quality, and faults. ✔️Establishing appropriate documentation and communication channels to aid stakeholder participation. ✔️Testing and validating the pipeline on a regular basis, making sure to cover backup and disaster recovery procedures. Here's an infographics depicting a holistic view to cover SQL under the hood by Wallarm: API Security Leader and Ivan Novikov #dataengineering #data #analytics #systemdesign #bigdata #cloudcomputing #datamining #engineering #dataanalytics

Can you trust the data before engineering it❓ No ❌ Data Gathering, cleaning, querying, analyzing is one of the most important aspect when dealing with data. Working with data requires more than simply applying select on the tables. Data is the key and #SQL is the fundamental skill for any data engineer. So, where there's data there's loads of querying. Data Engineering often traverses through various stages while building a data pipeline going through lots of data level decisions. ✅Each stage from Ingestion of data to delivering the data to end consumer, the phase travels through a lot of technical nuances and process it by cleaning, loading and transforming it. ♐️Let's explore the various stages of a data pipeline and understand the importance of each phase - - 𝐃𝐚𝐭𝐚 𝐜𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧: Collection of raw data from various sources, such as databases, sensors, or surveys. - 𝐃𝐚𝐭𝐚 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠: Ingesting data through various sources and preparing it for analysis by handling duplicates, errors, and missing values. - 𝐃𝐚𝐭𝐚 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: Transforming data into a reusable format using various compute approaches. - 𝐃𝐚𝐭𝐚 𝐬𝐭𝐨𝐫𝐚𝐠𝐞: Storing processed data in a synchronous format for future use. - 𝐃𝐚𝐭𝐚 𝐂𝐨𝐧𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧: Analyzing, modelling data to identify patterns, trends, and relationships to generate business insights. ➡️Few of the best practices recommended for data pipelines include: ✔️Auditing and data lineage tools to monitor data movement and guarantee data governance. ✔️Constructing scalable and fault-tolerant structures to manage massive data volumes. ✔️Using appropriate logging, metrics, and alarms, monitoring pipeline performance, data quality, and faults. ✔️Establishing appropriate documentation and communication channels to aid stakeholder participation. ✔️Testing and validating the pipeline on a regular basis, making sure to cover backup and disaster recovery procedures. Here's an infographics depicting a holistic view to cover SQL under the hood by Wallarm: API Security Leader and Ivan Novikov #dataengineering #data #analytics #systemdesign #bigdata #cloudcomputing #datamining #engineering #dataanalytics

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🌷🌷#explore #fyp #الشعب_الصيني_ماله_حل😂😂

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#seamstress #seamstressesoftiktok #seamstresslife #sewing #sewingtiktok #sewingtipsandtricks #sewinghacks #needlethreader #quilter #sewingforyoupage #sewingtools #sewingtips

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Have you streamed “The Deep End” yet?! 💙😆 #artist #newmusic #music #singer #songwriter #fyp

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