Your team is divided on data quality vs. processing speed in ETL processes. How do you find a middle ground?
In data engineering, the Extract, Transform, Load (ETL) process is a critical pipeline for moving and preparing data for analysis. A common debate within teams is prioritizing data quality versus processing speed. High-quality data ensures accurate analysis, but fast processing is essential to meet business needs. Finding a middle ground requires a strategic approach that balances these priorities without compromising on either.
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Sai Krishna ChivukulaPrincipal Data Engineer @ Altimetrik | 🌟 Top Data Engineering Voice 🌟| 22K+ Followers | Ex Carelon, ADP, CTS | 2x…
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Anvita PatilInformation Systems student at Northeastern | Data Engineer intern @ Point32Health | Cloud Computing | Analytics
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Saman Afshan🌟LinkedIn Top Voice || Data Engineer || Snowflake |Snowpark| Azure Durable Functions | Azure Databricks | Pyspark |…