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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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PL/SQL Sample code and error handling mechanism

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SAMPLE PL/SQL



CREATE TABLE dummy


( dummy_value       VARCHAR2(1));
DECLARE
  -- Define local variable.
  my_string VARCHAR2(1) := ' ';
  my_number NUMBER;
BEGIN
  -- Select a white space into a local variable.
  SELECT ' ' INTO my_string FROM dummy;
  -- Attempt to assign a single white space to a number.
  my_number := TO_NUMBER(my_string);
EXCEPTION
 WHEN no_data_found THEN
   dbms_output.put_line('SELECT-INTO'||CHR(10)||SQLERRM);
END;
/







Output and Error:

The program returns the following output, which illustrates formatting user- defined exceptions. 





The CHR(10) inserts a line return and provides a clean break between the program's SQLCODE and SQLERRM messages:







RAISE my_error SQLCODE [1] 



SQLERRM [User-Defined Exception]







































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