Most Commonly Used Terminology in Big Data Engineering
I am a Tech Enthusiast having 13+ years of experience in ๐๐ as a ๐๐จ๐ง๐ฌ๐ฎ๐ฅ๐ญ๐๐ง๐ญ, ๐๐จ๐ซ๐ฉ๐จ๐ซ๐๐ญ๐ ๐๐ซ๐๐ข๐ง๐๐ซ, ๐๐๐ง๐ญ๐จ๐ซ, with 12+ years in training and mentoring in ๐๐จ๐๐ญ๐ฐ๐๐ซ๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ๐ข๐ง๐ , ๐๐๐ญ๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ๐ข๐ง๐ , ๐๐๐ฌ๐ญ ๐๐ฎ๐ญ๐จ๐ฆ๐๐ญ๐ข๐จ๐ง ๐๐ง๐ ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐๐. I have ๐๐๐๐๐๐๐ ๐๐๐๐ ๐๐๐๐ 10,000+ ๐ฐ๐ป ๐ท๐๐๐๐๐๐๐๐๐๐๐๐ and ๐๐๐๐ ๐๐๐๐๐ ๐๐๐๐ ๐๐๐๐ 500+ ๐๐๐๐๐๐๐๐ ๐๐๐๐๐๐๐๐ in the areas of ๐๐จ๐๐ญ๐ฐ๐๐ซ๐ ๐๐๐ฏ๐๐ฅ๐จ๐ฉ๐ฆ๐๐ง๐ญ, ๐๐๐ญ๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ๐ข๐ง๐ , ๐๐ฅ๐จ๐ฎ๐, ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ข๐ฌ, ๐๐๐ญ๐ ๐๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง๐ฌ, ๐๐ซ๐ญ๐ข๐๐ข๐๐ข๐๐ฅ ๐๐ง๐ญ๐๐ฅ๐ฅ๐ข๐ ๐๐ง๐๐ ๐๐ง๐ ๐๐๐๐ก๐ข๐ง๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ . I am interested in ๐ฐ๐ซ๐ข๐ญ๐ข๐ง๐ ๐๐ฅ๐จ๐ ๐ฌ, ๐ฌ๐ก๐๐ซ๐ข๐ง๐ ๐ญ๐๐๐ก๐ง๐ข๐๐๐ฅ ๐ค๐ง๐จ๐ฐ๐ฅ๐๐๐ ๐, ๐ฌ๐จ๐ฅ๐ฏ๐ข๐ง๐ ๐ญ๐๐๐ก๐ง๐ข๐๐๐ฅ ๐ข๐ฌ๐ฌ๐ฎ๐๐ฌ, ๐ซ๐๐๐๐ข๐ง๐ ๐๐ง๐ ๐ฅ๐๐๐ซ๐ง๐ข๐ง๐ new subjects.
Data Source: The origin of the data, which can be databases, files, APIs, or streaming platforms.
Extraction: The process of gathering data from different sources, and transforming it into a suitable format for processing.
Transformation: Manipulating and converting data into a desired format or structure, including cleaning, filtering, aggregating, and joining operations.
Load: The process of storing transformed data into a destination system, such as databases or data warehouses.
ETL: Stands for Extract, Transform, Load. It refers to the overall process of extracting data from various sources, transforming it, and loading it into a target system.
Batch Processing: Handling and processing data in large volumes at scheduled intervals or in batches.
Real-time Processing: Processing and analyzing data as it arrives, providing immediate insights and actions.
Streaming: Handling and processing continuous data streams in real-time.
Data Pipeline: A series of interconnected steps that enable the movement and processing of data from source to destination.
Data Warehouse: A central repository for storing structured and organized data, optimized for querying and analysis.
Data Lake: A storage repository that stores vast amounts of raw or unprocessed data in its native format.
Data Governance: A set of policies and practices to ensure data quality, integrity, security, and compliance throughout the data pipeline.
Data Quality: The measure of data's accuracy, completeness, consistency, reliability, and relevance.
Metadata: Information about the data, such as its source, structure, format, and meaning.
Workflow Orchestration: Coordinating and managing the execution of different tasks and dependencies in a data pipeline.
Data Partitioning: Splitting and organizing data into smaller, manageable subsets based on specific criteria (e.g., time, location, or category).
Data Replication: Copying and synchronizing data across different systems or locations for redundancy, scalability, or fault tolerance.
Data Integration: Combining data from multiple sources or systems into a unified view.
Data Modeling: Designing and structuring data to represent real-world entities, relationships, and business logic.
Data Pipeline Monitoring: Monitoring the health, performance, and data flow within a pipeline, often with the help of metrics, alerts, and logging.
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