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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 best solution Ceph Data Storage for big data

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#The best solution Ceph Data Storage for big data:
#The best solution Ceph Data Storage for big data:
The power of Ceph can transform your organization’s IT infrastructure and your ability to manage vast amounts of data. If your organization runs applications with different storage interface needs, Ceph is for you! Ceph’s foundation is the Reliable Autonomic Distributed Object Store (RADOS), which provides your applications with object, block, and file system storage in a single unified storage cluster—making Ceph flexible, highly reliable and easy for you to manage.

Ceph’s RADOS provides you with extraordinary data storage scalability—thousands of client hosts or KVMs accessing petabytes to exabytes of data. Each one of your applications can use the object, block or file system interfaces to the same RADOS cluster simultaneously, which means your Ceph storage system serves as a flexible foundation for all of your data storage needs. You can use Ceph for free, and deploy it on economical commodity hardware. Ceph is a better way to store data.

OBJECT-BASED STORAGE
Organizations prefer object-based storage when deploying large scale storage systems, because it stores data more efficiently. Object-based storage systems separate the object namespace from the underlying storage hardware—this simplifies data migration.

WHY IT MATTERS
By decoupling the namespace from the underlying hardware, object-based storage systems enable you to build much larger storage clusters. You can scale out object-based storage systems using economical commodity hardware, and you can replace hardware easily when it malfunctions or fails.

THE CEPH DIFFERENCE
Ceph’s CRUSH algorithm liberates storage clusters from the scalability and performance limitations imposed by centralized data table mapping. It replicates and re-balance data within the cluster dynamically—elminating this tedious task for administrators, while delivering high-performance and infinite scalability.

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