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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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Data Vault Top benefits Useful to Your Project

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Data Vault 2.0 (DV2) is a system of business intelligence that includes: modeling, methodology, architecture, and implementation best practices.
The benefits of Data Vault
The components, also known as the pillars of DV2 are identified as follows:
data vault
  • DV2 Modeling (changes to the model for performance and scalability)
  • DV2 Methodology (following Scrum and agile best practices)
  • DV2 Architecture (including NoSQL systems and Big Data systems)
  • DV2 Implementation (pattern-based, automation, generation Capability Maturity Model Integration [CMMI] level 5)
There are many special aspects of Data Vault, including the modeling style for the enterprise data warehouse. The methodology takes commonsense lessons from software development best practices such as CMMI, Six Sigma, total quality management (TQM), Lean initiatives, and cycle-time reduction and applies these notions for repeatability, consistency, automation, and error reduction.

Each of these components plays a key role in the overall success of an enterprise data warehousing project. These components are combined with industry-known and time-tested best practices ranging from CMMI to Six Sigma, TQM (total quality management) to Project Management Professional (PMP).

Data Vault 1.0

Data Vault 1.0 is highly focused on just the data modeling section, while DV2 encompasses the effort of business intelligence. The evolution of Data Vault extends beyond the data model and enables teams to execute in parallel while leveraging Scrum agile best practices.

Data Vault 2.0

DV2 architecture is designed to include NoSQL (think: Big Data, unstructured, multistructured, and structured data sets). Seamless integration points in the model and well-defined standards for implementation offer guidance to the project teams.

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