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Data Concepts and Environment Fundamentals Flashcards
CompTIA DataX DY0-001 (V1) Flashcards
| Front | Back |
| Batch processing | Executing a series of commands on a large set of data at once |
| Big data | Large and complex datasets that require advanced tools for storage, processing, and analysis |
| Blockchain data storage | Decentralized, secure method to store data across a network |
| Cloud computing | Using remote servers hosted on the internet to store, manage, and process data |
| Columnar database | A database that stores data by columns, optimized for analytical workloads |
| Concurrent processing | Performing multiple tasks or operations simultaneously in a system |
| Data anonymization | Masking or removing identifying information to protect privacy |
| Data audit trails | Records showing the history and transformations of data |
| Data compression | Reducing the size of data to save storage and improve performance |
| Data encodings | Methods for formatting data into a standardized representation such as UTF-8 |
| Data federation | Integrating data from different sources into a virtual unified view |
| Data governance | Policies and practices to ensure data quality, security, and compliance |
| Data integrity | Ensuring data is accurate, consistent, and reliable |
| Data lake | A storage solution that holds raw data in its native format before processing |
| Data lineage | Tracking where data comes from, how it moves, and where it ends up |
| Data modeling | The process of creating a visual representation of a data system |
| Data partitioning | Dividing data into smaller chunks to optimize performance and scalability |
| Data pipeline | A series of processes that move and transform data from source to destination |
| Data processing | The act of converting raw data into meaningful information |
| Data redundancy | Storing the same data in multiple locations leading to inefficiency |
| Data scalability | The ability to handle increasing amounts of data without performance issues |
| Data storage | The method or technology used to save data such as HDD, SSD, or cloud storage |
| Data type | Represents the kind of data such as integer, float, string, or boolean |
| Data visualization | Representing data graphically to better understand trends and patterns |
| Data warehouse | A centralized repository for storing large amounts of structured data for analysis |
| Distributed computing | Using multiple machines to process large volumes of data |
| ETL process | Extract, Transform, Load - steps to move and prepare data for analysis |
| Foreign key | A field in one table that links to the primary key in another table |
| Immutable data | Data that cannot be changed after it is stored |
| Index in database | A structure that improves the speed of data retrieval operations |
| Metadata | Data that provides information about other data such as its format or origin |
| Non-relational database | A database that stores unstructured or semi-structured data like key-value pairs or documents |
| Normalization | Process of organizing data to reduce redundancy and improve consistency |
| NoSQL | Non-relational database systems designed for scalability and flexibility |
| OLAP | Online Analytical Processing designed for complex data analysis and decision-making |
| OLTP | Online Transaction Processing focused on handling routine transaction data |
| Primary key | A unique identifier for a record in a relational database |
| Real-time processing | Processing data as it is generated to provide immediate results |
| Relational database | A type of database using tables with rows and columns to store structured data |
| Replication in databases | Creating duplicates of data for backup and high availability |
| Sharding | Dividing a database into smaller pieces to distribute the load across servers |
| Snapshot in databases | A point-in-time copy of the database for backup or analysis |
| SQL | Structured Query Language used to interact with relational databases |
| Structured data | Data organized in rows and columns often seen in relational databases |
| Transactional data | Data generated from business transactions like orders or payments |
| Unstructured data | Data with no predefined format like images, videos, and text documents |
Front
Real-time processing
Click the card to flip
Back
Processing data as it is generated to provide immediate results
Front
Data compression
Back
Reducing the size of data to save storage and improve performance
Front
Data partitioning
Back
Dividing data into smaller chunks to optimize performance and scalability
Front
Primary key
Back
A unique identifier for a record in a relational database
Front
Unstructured data
Back
Data with no predefined format like images, videos, and text documents
Front
Data lineage
Back
Tracking where data comes from, how it moves, and where it ends up
Front
Data type
Back
Represents the kind of data such as integer, float, string, or boolean
Front
Data integrity
Back
Ensuring data is accurate, consistent, and reliable
Front
Immutable data
Back
Data that cannot be changed after it is stored
Front
Cloud computing
Back
Using remote servers hosted on the internet to store, manage, and process data
Front
Data visualization
Back
Representing data graphically to better understand trends and patterns
Front
SQL
Back
Structured Query Language used to interact with relational databases
Front
Metadata
Back
Data that provides information about other data such as its format or origin
Front
ETL process
Back
Extract, Transform, Load - steps to move and prepare data for analysis
Front
Index in database
Back
A structure that improves the speed of data retrieval operations
Front
NoSQL
Back
Non-relational database systems designed for scalability and flexibility
Front
Data anonymization
Back
Masking or removing identifying information to protect privacy
Front
Data governance
Back
Policies and practices to ensure data quality, security, and compliance
Front
OLTP
Back
Online Transaction Processing focused on handling routine transaction data
Front
Normalization
Back
Process of organizing data to reduce redundancy and improve consistency
Front
OLAP
Back
Online Analytical Processing designed for complex data analysis and decision-making
Front
Non-relational database
Back
A database that stores unstructured or semi-structured data like key-value pairs or documents
Front
Data lake
Back
A storage solution that holds raw data in its native format before processing
Front
Data processing
Back
The act of converting raw data into meaningful information
Front
Foreign key
Back
A field in one table that links to the primary key in another table
Front
Data storage
Back
The method or technology used to save data such as HDD, SSD, or cloud storage
Front
Data audit trails
Back
Records showing the history and transformations of data
Front
Data modeling
Back
The process of creating a visual representation of a data system
Front
Big data
Back
Large and complex datasets that require advanced tools for storage, processing, and analysis
Front
Data pipeline
Back
A series of processes that move and transform data from source to destination
Front
Columnar database
Back
A database that stores data by columns, optimized for analytical workloads
Front
Distributed computing
Back
Using multiple machines to process large volumes of data
Front
Structured data
Back
Data organized in rows and columns often seen in relational databases
Front
Data encodings
Back
Methods for formatting data into a standardized representation such as UTF-8
Front
Data scalability
Back
The ability to handle increasing amounts of data without performance issues
Front
Replication in databases
Back
Creating duplicates of data for backup and high availability
Front
Batch processing
Back
Executing a series of commands on a large set of data at once
Front
Blockchain data storage
Back
Decentralized, secure method to store data across a network
Front
Data federation
Back
Integrating data from different sources into a virtual unified view
Front
Transactional data
Back
Data generated from business transactions like orders or payments
Front
Concurrent processing
Back
Performing multiple tasks or operations simultaneously in a system
Front
Data redundancy
Back
Storing the same data in multiple locations leading to inefficiency
Front
Relational database
Back
A type of database using tables with rows and columns to store structured data
Front
Snapshot in databases
Back
A point-in-time copy of the database for backup or analysis
Front
Data warehouse
Back
A centralized repository for storing large amounts of structured data for analysis
Front
Sharding
Back
Dividing a database into smaller pieces to distribute the load across servers
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This deck covers foundational topics such as data types, data storage, and data processing environments important for understanding data management.