L4: Data Literacy

Question 1

ACID-compliance

Answer 1

Ensuring data accuracy and consistency through Atomicity, Consistency, Isolation, and Durability (ACID) in database transactions.

Question 2

Cloud-based Integration Platform as a Service (iPaaS)

Answer 2

Cloud-hosted integration platforms that offer integration services through virtual private clouds or hybrid cloud models, providing scalability and flexibility.

Question 3

Column-based Database

Answer 3

A type of NoSQL database that organizes data in cells grouped as columns, often used for systems requiring high write request volume and storage of time-series or IoT data.

Question 4

Data at rest

Answer 4

Data that is stored and not actively in motion, typically residing in a database or storage system for various purposes, including backup.

Question 5

Data integration

Answer 5

A discipline involving practices, architectural techniques, and tools that enable organizations to ingest, transform, combine, and provision data across various data types, used for purposes such as data consistency, master data management, data sharing, and data migration.

Question 6

Data Lake

Answer 6

A data repository for storing large volumes of structured, semi-structured, and unstructured data in its native format, facilitating agile data exploration and analysis.

Question 7

Data mart

Answer 7

A subset of a data warehouse designed for specific business functions or user communities, providing isolated security and performance for focused analytics.

Question 8

Data pipeline

Answer 8

A comprehensive data movement process that covers the entire journey of data from source systems to destination systems, which includes data integration as a key component.

Question 9

Data repository

Answer 9

A general term referring to data that has been collected, organized, and isolated for business operations or data analysis. It can include databases, data warehouses, and big data stores.

Question 10

Data warehouse

Answer 10

A central repository that consolidates data from various sources through the Extract, Transform, and Load (ETL) process, making it accessible for analytics and business intelligence.

Question 11

Document-based Database

Answer 11

A type of NoSQL database that stores each record and its associated data within a single document, allowing flexible indexing, ad hoc queries, and analytics over collections of documents.

Question 12

ETL process

Answer 12

The Extract, Transform, and Load process for data integration involves extracting data from various sources, transforming it into a usable format, and loading it into a repository.

Question 13

Graph-based Database

Answer 13

A type of NoSQL database that uses a graphical model to represent and store data, ideal for visualizing, analyzing, and discovering connections between interconnected data points.

Question 14

Key-value store

Answer 14

A type of NoSQL database where data is stored as key-value pairs, with the key serving as a unique identifier and the value containing data, which can be simple or complex.

Question 15

Portability

Answer 15

The capability of data integration tools to be used in various environments, including single-cloud, multi-cloud, or hybrid-cloud scenarios, provides flexibility in deployment options.

Question 16

Pre-built connectors

Answer 16

Cataloged connectors and adapters that simplify connecting and building integration flows with diverse data sources like databases, flat files, social media, APIs, CRM, and ERP applications.

Question 17

Relational databases (RDBMSes)

Answer 17

Databases that organize data into a tabular format with rows and columns, following a well-defined structure and schema.

Question 18

Scalability

Answer 18

The ability of a data repository to grow and expand its capacity to handle increasing data volumes and workload demands over time.

Question 19

Schema

Answer 19

The predefined structure that describes the organization and format of data within a database, indicating the types of data allowed and their relationships.

Question 20

Streaming data

Answer 20

Data that is continuously generated and transmitted in real-time requires specialized handling and processing to capture and analyze.

Question 21

Use cases for relational databases

Answer 21

Applications such as Online Transaction Processing (OLTP), Data Warehouses (OLAP), and IoT solutions where relational databases excel.

Question 22

Vendor lock-in

Answer 22

A situation where a user becomes dependent on a specific vendor’s technologies and solutions, making it challenging to switch to other platforms.