Deep Learning

🚀 Top 20 Data Engineering Terms You Should Know

1. Data Engineering
Data Engineering is the practice of designing, building, and maintaining systems that collect, process, transform, and store data for analytics, reporting, and machine learning.

2. Data Pipeline
A data pipeline is an automated workflow that moves data from one or more sources to a destination while applying transformations such as cleaning, validation, and aggregation.

3. ETL (Extract, Transform, Load)
ETL is a process where data is extracted from source systems, transformed into the required format, and then loaded into a data warehouse or database.

4. ELT (Extract, Load, Transform)
ELT is a modern data integration approach where raw data is first loaded into a data warehouse and then transformed using the warehouse's computing power.

5. Data Lake
A data lake is a centralized repository that stores large volumes of raw, structured, semi-structured, and unstructured data in its original format.

6. Data Warehouse
A data warehouse is a centralized database designed to store cleaned, structured, and historical data optimized for reporting, business intelligence, and analytics.

7. Batch Processing
Batch processing is the execution of data processing tasks on a collection of data at scheduled intervals rather than processing each event as it arrives.

8. Stream Processing
Stream processing is the continuous processing of data in real time as it is generated, enabling immediate analysis and decision-making.

9. Big Data
Big Data refers to extremely large and complex datasets that cannot be efficiently processed using traditional database systems due to their volume, velocity, and variety.

10. Apache Spark
Apache Spark is an open-source distributed computing framework used for fast processing of large datasets through in-memory computation.

11. Apache Kafka
Apache Kafka is a distributed event-streaming platform used to publish, store, and process real-time data streams between applications.

12. Partitioning
Partitioning is the process of dividing large datasets into smaller, manageable parts so they can be processed efficiently and in parallel.

13. DataFrame
A DataFrame is a distributed table-like data structure in Spark that organizes data into rows and columns with a defined schema for efficient processing.

14. Schema
A schema defines the structure of a dataset or database, including tables, columns, data types, relationships, and constraints.

15. Change Data Capture (CDC)
Change Data Capture (CDC) is a technique that identifies and captures only the data that has changed since the last processing cycle, making data pipelines faster and more efficient.

16. Data Modeling
Data modeling is the process of designing how data is organized, stored, and related to support efficient querying and analysis.

17. Data Quality
Data quality refers to the accuracy, completeness, consistency, validity, and reliability of data used for business decisions.

18. Data Lineage
Data lineage tracks the journey of data from its source through transformations to its final destination, helping with debugging, auditing, and compliance.

19. Data Governance
Data governance is the framework of policies, standards, and processes that ensure data is secure, consistent, compliant, and properly managed across an organization.

20. Fault Tolerance
Fault tolerance is the ability of a system to continue operating correctly even when one or more components fail, ensuring high availability and reliability.

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