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Dear Connections , If anyone looking for ETL Testing let me know this is the right time to upskill your knowledge. We have a Real Time Expert who is having 10+ Years going to take the sessions. If anyone Interested please join today demo session. #etljobs #etldeveloper #ethicalhacking #etlinformatica #etltools #testingjobs #testautomation #tester #testing #testengineer #testingautomation #testingtools #testes #testingcommunity #testingengineer #informationsecurity #informatica #informaticadevelopers #informaticadeveloper #database #sqldeveloper #sql #informaticajobs #dataanalysis
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ETL Process flow
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Types of ETL
Automation Test Engineer at TCS | Ex-JK Tech | 3+ Years in Software Testing | Expertise in Selenium, Java, BDD (Cucumber), TestNG, API Testing (Rest Assured), GCP | Manual & Automation Testing | 70K+ LinkedIn Network
🔍 Top 6 Types of ETL Testing Every Data Engineer Should Know! Ensuring data quality during ETL processes is crucial, and here are the top ETL testing types to help you achieve it: 1️⃣ Production Validation Testing: Confirm that data in the production environment is accurate and meets expectations. 2️⃣ Source to Target Count Testing: Ensure that the count of records is consistent between source and target. 3️⃣ Data Transformation Testing: Verify that data transformations are applied correctly, ensuring data integrity. 4️⃣ Referential Integrity Testing: Check relationships between tables to ensure data consistency. 5️⃣ Integration Testing: Validate data integration from various sources into the target system. 6️⃣ Performance Testing: Measure ETL job performance and optimize processing time. 💡 Which ETL testing type do you find most essential? Let’s discuss below! #ETL #DataTesting #DataQuality #DataEngineer #DataManagement #Analytics
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Our next article explores the pivotal role of ETL developers in data management and analytics. Learn more about how they contribute to efficient and accurate data integration. https://rfr.bz/la0ydyu #ETLdeveloper #datamanagement #analytics
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#hiring #hrcglobalservices #dataengineer #postgresql #guidewire #python #informatica Data Engineer 5+ years experience Markham Hybrid Role ETL tools, Guidewire policy billing and cliams, database managemnt, postgresql, oraclefusion Please share your updated resume here: manasa@hrc-globalservices.com #hiring #guidewiredeveloper #dataengineer #markham #hiringdataengineer #servingnoticeperiod #openworkpermit #hybridrole #dataanalysis #datamodeling #etl #python #qlik #informatica
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Mastering Data Profiling and Analysis in ETLChallenge: Migrating data from a legacy system to a new data warehouse revealed significant anomalies. Resolution :- Data Profiling and AnalysisConducted detailed manual data profiling to uncover issues like duplicates, null values, and incorrect data types. For example, I identified that 20% of customer records had inconsistent formatting. Created an insightful data quality dashboard using custom scripts and tools to visualize and track the resolution of anomalies. Benefit: By identifying and addressing these issues early, I ensured a smooth transition with accurate and reliable data, saving the company from potential data-driven decision errors and boosting operational efficiency. 🔍 Meticulous data profiling is the cornerstone of a successful ETL process. Ensuring data integrity from the start is what sets top-tier ETL QA engineers apart. #DataQuality #ETLTesting #DataProfiling
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What to Do with a Broken ETL Pipeline? The simplest approach is often to just re-run the pipeline, and sometimes that works! However, the easiest way isn’t always the best way. Relying on reruns can lead to recurring failures down the road. Here’s my approach: 𝗮𝗹𝘄𝗮𝘆𝘀 𝗮𝗶𝗺 𝘁𝗼 𝗺𝗮𝗸𝗲 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝘁𝗼 𝘁𝗵𝗲 𝗘𝗧𝗟 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗮𝗳𝘁𝗲𝗿 𝗮 𝗳𝗮𝗶𝗹𝘂𝗿𝗲. The logic is straightforward—if your ETL pipeline breaks, it will likely break again in the future unless something is altered. Steps to handle a broken ETL pipeline: 1️⃣ Check the error: Start by examining the error that was raised. If you’ve set up retries, look at the first attempt (original run) for clues. 2️⃣ Debug and resolve: Understand the root cause of the failure and update your code to ensure similar issues don’t arise again. This proactive change can prevent future disruptions. 3️⃣ Change the pipeline timing: If you can’t pinpoint the exact reason, consider adjusting the pipeline’s schedule. Sometimes, data sources undergo maintenance or other downtime that overlaps with your pipeline triggers. Personally, I get bad feeling when I saw the failure notification on my phone early in the morning, but it is a part of our job. Remember, even the best ETLs can fail. Not because data engineer missed something, but because the data provider changes something on its side, and they just didn’t mention it to you. 🔗Visit My Website: https://farnamdata.com/ #dataengineering #debug
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