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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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5 Top features of Sqoop in the age of Big data

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The ‘Sqoop’ is a command-line user interface program for conveying information amid relational databases and Hadoop.

The SQOOP

It aids increasing stacks of a sole table either a gratis shape SQL request as well like preserved appointments that may be run numerous periods to ingress upgrades produced to a database ever since the final ingress.

 Imports may as well be applied to inhabit boards in Apache Hive|Hive either HBase. Exports may be applied to put information as of Hadoop into a relational database.

Apache Foundation

Sqoop grew to be a top-level Apache Software Foundation, Apache program in March 2012. Microsoft utilizes a Sqoop-based connector to aid transference information as of Microsoft SQL Server databases to Hadoop.

 Couchbase, Inc. As well delivers a Couchbase Server-Hadoop connector by intents of Sqoop.

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