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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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The awesome points to learn from DB2 NoSQL GraphStore

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 #db2 graphstore: One best example, prior to understanding the RDF format for Graph data model -  If the graph data model is the model the semantic web uses to store data, RDF is the format in which it is written.  Related: Highly Demanding Web Designer Jobs Summary of DB2 Graph Store: DB2-RDF support is officially called "NoSQL Graph Support".   The API extends the Jena API (Graph layer).  Developers familiar with Jena TDB will have the Model layer capabilities they are accustomed to. Although the DB2-RDF functionality is being released with DB2 LUW 10.1, it is also compatible with DB2 9.7. Full supports for SPARQL 1.0 and a subset of SPARQL 1.1.  Full SPARQL 1.1 support (which is till a W3C working draft) will be forthcoming. While RDBMS implementations of RDF graphs have typically been non-performant, that is not the case here*.  Some very impressive and innovative work has been put into optimization capabilities.  Out-of-t...
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