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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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Data Science in Retail Marketing and Opportunities

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Data Science and Analytics started by all big companies including Shoppers top and Wal-mart. Data Science in Retail Shoppers Stop started big data analytics to study customers behavior. Started with one of the easiest programs. Where they studied customer loyalty program. Some Insights Studied patterns Studied customer buying styles Some people buy shirt-only and some people buy both Shirt and Trouser Studied buying patterns of Women buyers Read more at Live Mint
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Retail Analytics Solution from Leader in Data Science SAS

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Retail market is changed now. So many companies now open shops across the world. Every business owner tries to increase sales. This is possible with analytics. Retail Analytics The study also includes an in-depth look at how best-in-class retailers use analytics, as well as the business analytics software vendor landscape. Walmart started retail business in India. So the real use of data analytics started now. References Download here
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