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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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Big Data: Top Cloud Computing Interview Questions (1 of 4)

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The below are frequently asked interview questions on Cloud computing:
1) What is the difference between Cloud and Grid?
Grid:
-Information service
-Security Service
-Data management
-Execution Manageement
Cloud:
- Maintains up-to-date information of resources
-Create VMs according to user requirement
-Application deploment
-User management

2) What are the different cloud standards?
-Interoperability standards
-Security standards
-Portability Standards
-Governance and Risk standards

3) What are the two different sub-systems in Cloud computing?
-Management sub system
-Resource sub system

4)What is Cloud compouting?
The promise of cloud computing is ubiquitous access to a broad set of applications and services, which are delivered over the network to multiple customer.

5) Why we need specialized network for Cloud services?
The public Internet is the simplest choice for delivering cloud-based services. In this model, the cloud provider simply purchases Internet connectivity and its customers access the services via their own Internet connections. However, modern high-performance applications are raising communication and bounded-time execution requirements that the public Internet cannot meet neither at the present nor even in the foreseeable future.

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