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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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Distributed Computing Why it is Popular

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Distributed information systems are becoming more popular as a result of improvements in computer hardware and software, and there is a commensurate rise in the use of the associated technologies.

Why distributed computing so popular

  • With the increasing desire for business-to-business (B2B) communication and integration, technologies such as Service-Oriented Computing (SOC), Semantic Web, Grid, Agents/Multi-agents, peer-to-peer, etc., are receiving a high level of interest nowadays.
  • Due to the revolution in the internet, implementation of SOC B2B integration (e.g. e-commerce, e-government, and e-healthcare) is popular

Where lacking

  • Building Web Services comprehensively needs further improvement, for instance, Quality of Service (QoS).
  • Detection of service availability to achieve self-healing in the invocation process
  • Service reuse, how best to define atomic services
  • Service composition

Future computing

  1. Meanwhile, it should be noted that Web Services play only a partial role in evolving distributed information systems. With the development of future computer hardware, software, and business requirements, many other technologies will probably emerge that will serve particular business goals better. 
  2. Therefore, much recent research has been focusing not only on individual technologies in distributed systems but also on the possibility of combining currently available technologies to improve business outcomes.
  3. We concentrate mainly on Web Services and technical issues associated with current Web Services standards, but we also give a brief overview of three other distributed technologies, namely Grid, agents, and Semantic Web, which can work with Web Services. Thus, it concentrates initially on the background of services in distributed information systems, then it introduces Grid, agent, and Semantic Web technologies.

Web services Vs distributed computing

After that, it discusses several technical aspects of Web Services in current distributed information systems, in particular, general Web Service availability and performance issues and the possibility of combining agent technology and Web Services to provide an improved understanding of service availability. 

We then introduce JSON (JavaScript Object Notation), which may provide an alternative to current approaches that will deliver better Web Service Performance and discuss service composition, illustrating it with implementation from the EU Living Human Digital Library (LHDL) project.

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