Unless you're the Elle Woods of the data world, testing data applications is hard. Get a walkthrough of some common challenges that data teams face when testing data applications: https://bit.ly/45775n6 #DataOps #DataEngineering
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Common principal mistakes data teams make... As an example, a data consultant provides new requirements that demand structural changes. How it usually goes down while it should not: 1. The data engineer (DE) finds a solution for the back-end while the data analyst (DA) finds a solution for the front-end, each independently 2. The DE starts developing the ETL/ELT pipeline from raw data up to the data mart (or ETLELTLETLELTLETL 🤣🤣) 3. The DA starts developing the dashboard using a dummy dataset 4. The DA switches the connection to the data mart once the DE is done 5. Endless iterations start because of the conflicts between the back-end and front-end => Everyone is stressed and disappointed, and lots of effort is wasted How it should go down: 1. Beat the hell out of the data model on a low level, where the DE MUST go into the front-end calculations which will reflect on the back-end (sometimes even on early transformations), and the DA MUST understand the back-end limitations which will affect front-end calculations 3. Keep iterating until a common ground for a valid solution is found => Everyone is happy and efficiency is maximized
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How do you validate *thousands* of tables during a data migration? Learn how Faire accelerated their migration timeline by over 6 months using Datafold's cross-database data diffing to validate over 5000 ( ❗ ) tables. https://lnkd.in/g-BJtNjC
Faire Case Study - Migration with Validation
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A Data Engineering Story 🚀 What do you do when your clients’ #DataWarehouse turns out to be a #data dumping ground 🗄️ The following story is based on a use case from a real client and the methods we employed in order to process their data. 💻 Check out the full story here: https://lnkd.in/g9Va5hjv 📊 #synvert #DataWarehouse #ETL
A Data Engineering Story - synvert Data Insights
https://datainsights.de
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Dive into the world of API data mapping with our Comprehensive Guide! 🌐 Learn how to effectively navigate and optimize your API data mapping process. #APIDataMapping #Guide #ReadNow 📊 Read now: https://hubs.ly/Q02k0BTH0
Understanding API Data Mapping: A Comprehensive Guide
https://www.adeptia.com
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👉 Read our latest #Article about the Need of Big Data Testing for Business Organizations👈 Now before proceeding into the further discussion to know about the significance of big data testing for business enterprises; let’s know the concept of big data testing briefly which is essential in order to test big data software applications for business enterprises. Link - https://lnkd.in/d8b45yAk #bigdatatesting #bigdata #bigdataengineer #hadoopadmin #databasemanagement #businessadvertising #management #schedulingsoftware
Need of Big Data Testing for Business Enterprises
https://precisetestingsolution.com
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Data Engineer⚡|5.5+ years | Spark | SQL | ADF | Azure Databricks | Python | 2x Azure | Open for Collaboration | Ex- Infosys
🌟Incremental load - Part 1 (What): ⚡ ⏩When moving data in an extraction, transformation, and loading (ETL) process, the most efficient design pattern is to touch only the data you must, copying just the data that was newly added or modified since the last load was run. ✅This pattern is known as "INCREMENTAL LOAD". ⏩It usually presents the least amount of risk, takes less time to run, and preserves the historical accuracy of the data. ⏩An incremental load is the selective movement of data from one system to another. ⏩An incremental load pattern will attempt to identify the data that was created or modified since the last time the load process ran. ⏩ This differs from the conventional full data load, which copies the entire set of data from a given source. ⏩The selectivity of the incremental design usually reduces the system overhead required for the ETL process. ⏩The selection of data to move is often temporal, based on when the data was created or most recently updated. 💡In some cases, the new or changed data cannot be easily identified solely in the source, so it must be compared to the data already in the destination for the incremental data load to work properly. ============================== Stay tuned for the next parts related to incremental load.💯 For more enriching content, Follow: Naman Seth👨💻 Happy Learning! 😄 #data #transformation #upsert #incremental #load #etl #elt #dataengineering #sql #design
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GIS Specialist | Geographic Information Systems | Spatial Data Analysis | Data Visualization | Geospatial Intelligence
What's new in Data Pipelines (beta) for October 2023? Schedules, schema configurations, big integer and date-only field support, updates to the Join tool, improved error reporting, and more! https://ow.ly/kYKl50Q41ES
What's New in Data Pipelines (October 2023)
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🚀 Check out this insightful article on eInfochips to stay updated on the game-changing tools transforming the world of data integration and analytics. https://lnkd.in/djftkYk2 #DataEngineering #ETLTools #DataIntegration #TechTrends
ETL tools for Data Engineering
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