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The extracted data is often raw and unstructured and may come in various formats such as text, images, audio, or video. The extraction process requires careful planning to ensure dataintegrity. It’s crucial to understand the source systems and their structure, as well as the type and quality of data they produce.
But in reality, a data warehouse migration to cloud solutions like Snowflake and Redshift requires a tremendous amount of preparation to be successful—from schema changes and datavalidation to a carefully executed QA process. Facilitating self-service data? Integrating new tooling? Better governance?
.” – Take A Bow, Rihanna (I may have heard it wrong) Validatingdata quality at rest is critica l to the overall success of any Data Journey. Using automated datavalidation tests, you can ensure that the data stored within your systems is accurate, complete, consistent, and relevant to the problem at hand.
Step 4: Data Transformation and Enrichment Data transformation involves changing the format or value inputs to achieve a specific result or to make the data more understandable to a larger audience. Enriching data entails connecting it to other related data to produce deeper insights.
Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. SchemaSchema on Read Schema on Write Best Fit for Applications Data discovery and Massive Storage/Processing of Unstructured data. are all examples of unstructured data.
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