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The group kicked off the session by exchanging ideas about what it means to have a modern data architecture. Atif Salam noted that as recently as a year ago, the primary focus in many organizations was on ingesting data and building datalakes.
To power AI and analytics workloads across your transactional and purpose-built databases, you must ensure they can seamlessly integrate with an open data lakehouse architecture without duplication or additional extract, transform, load (ETL) processes.
Tools such as Python’s Pandas library, Apache Spark, or specialised data cleaning software streamline these processes, ensuring data integrity before further transformation. Step 3: Data Transformation Data transformation focuses on converting cleaned data into a format suitable for analysis and storage.
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