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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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Differences: Data Center Vs. Telecom Networking

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Data Center Networking


Data Center (DC)-based services are emerging as a relevant source of network capacity demand for service providers and telecom operators. Cloud computing services, Content Distribution Networks (CDNs), and, generally, networked applications have a huge impact on the telecom operator infrastructure.

New trends

The Cloud computing paradigm provides a new model for service delivery where computing resources can be provided on-demand across the network. This elasticity permits the sharing of resources among users, thus reducing costs and maximizing utilization while posing a challenge towards an efficient cloud-aware network.

The computing resources can be provided on-demand depending on the user requests. Such resources can be allocated on distinct servers into a data center, or through data centers distributed in the network. Under this new model, the users access their assigned resources, as well as the applications and services using them, through telecom operator networks. 



Differences: Data Center Vs. Telecom Networking
Networking


Tradition telecom networks


Traditional telecom networks have been built on the concept of totally managed services, with an end-to-end approach, where the telco operator is in charge, not only, of providing the necessary connectivity to the end-user and the final service itself, but also of providing total control of the service provision, including tasks such as subscription management, billing, network operation and troubleshooting, quality of service guarantee, customer care, etc.

Such an approach mandates a tight control of the service path and a comprehensive understanding of the service and its implications. The telco operator offers those services to its customers, which merely consume them (even, in some cases, composing some of them) in a controlled manner, within the limits provided by the telco operator. 

New Trends


These services can be seen as building blocks, which at the same time are supported by network building blocks, both at transport and control level, monolithically. The telco services are typically provided by centralized nodes located deep in the network.

These service nodes are under the sole control of the network operator. Such a controlled environment tends to remain stable where the innovation in technology and services is gradual and modulated by the network operator.

However, during the last decades, the technology fundamentals of computer networking have been influencing the telecom networks, mainly due to the hegemony of the Internet Protocol (IP), which has been emerged as the technology substrate for every kind of service, also for the traditional services offered by telco operators.


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