Podcast
Questions and Answers
Which zone in the data lake is responsible for applying metadata and protecting sensitive attributes?
Which zone in the data lake is responsible for applying metadata and protecting sensitive attributes?
What is the primary function of the Trusted data zone in a data treatment process?
What is the primary function of the Trusted data zone in a data treatment process?
Which of the following is NOT a type of data source utilized in Data treatment 2?
Which of the following is NOT a type of data source utilized in Data treatment 2?
In the context of managed software delivered via the internet, which of the following is an example?
In the context of managed software delivered via the internet, which of the following is an example?
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What is the purpose of the Discovery sandbox in data treatment processes?
What is the purpose of the Discovery sandbox in data treatment processes?
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What does data capturing NOT involve?
What does data capturing NOT involve?
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Which of the following statements is true regarding data warehouses and data lakes?
Which of the following statements is true regarding data warehouses and data lakes?
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Which type of data source is NOT typically included as a main type?
Which type of data source is NOT typically included as a main type?
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What aspect is NOT considered important in data presentation?
What aspect is NOT considered important in data presentation?
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Which of the following best describes on-premise data management?
Which of the following best describes on-premise data management?
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Which method does NOT represent a way of automated data collection?
Which method does NOT represent a way of automated data collection?
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What is NOT a necessary characteristic of effective data capturing?
What is NOT a necessary characteristic of effective data capturing?
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Which process is NOT typically a part of data processing?
Which process is NOT typically a part of data processing?
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What is the primary function of the Transient zone in a data lake?
What is the primary function of the Transient zone in a data lake?
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Which of the following best represents what occurs in the Raw data zone during data treatment processes?
Which of the following best represents what occurs in the Raw data zone during data treatment processes?
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How is data typically prepared for presentation in modern architectures?
How is data typically prepared for presentation in modern architectures?
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What is a characteristic of the Trusted data zone in data treatment processes?
What is a characteristic of the Trusted data zone in data treatment processes?
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In the context of Big Data Architecture, which option describes Consumer systems?
In the context of Big Data Architecture, which option describes Consumer systems?
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Which of the following accurately describes the main types of data sources?
Which of the following accurately describes the main types of data sources?
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What is the primary distinction between data warehouses and data lakes?
What is the primary distinction between data warehouses and data lakes?
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Which method of data capturing is most likely to ensure high accuracy when collecting data?
Which method of data capturing is most likely to ensure high accuracy when collecting data?
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Which aspect of data processing is crucial for ensuring the quality of data before analysis?
Which aspect of data processing is crucial for ensuring the quality of data before analysis?
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What characteristic is essential for effective data presentation?
What characteristic is essential for effective data presentation?
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Which data management approach requires users to manage all aspects of their infrastructure?
Which data management approach requires users to manage all aspects of their infrastructure?
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What type of data source includes APIs as a primary method for accessing data?
What type of data source includes APIs as a primary method for accessing data?
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Which method of data capturing is least likely to enable on-site data collection?
Which method of data capturing is least likely to enable on-site data collection?
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Study Notes
Data Collection and Processing
- Data is gathered from various sources: web browsers, smartphones, search engines, the internet, banking transactions, and gaming activities.
- Data collection is conducted by government agencies, pharmaceutical companies, consumer product companies, large retailers (e.g., big box stores), and credit card companies.
- Big Data infrastructure encompasses the processes of collection, ingestion, preparation, computation, and presentation.
- A data source provides raw data for analysis, reporting, and other applications. Sources include structured and unstructured data: databases, APIs, flat files, cloud storage, data warehouses, spreadsheets.
Data Capture Methods
- Data capturing involves collecting and entering data from diverse sources into a system.
- Manual data entry: Humans input data into systems (spreadsheets, databases).
- Automated data collection: Sensors, IoT devices, APIs, surveys, forms, and point-of-sale systems.
- Mobile data capture: Utilization of mobile devices for on-site data gathering.
- Data capturing methods must adhere to accuracy, privacy, security, and integration standards.
Data Processing Steps
- Data processing encompasses collection, cleaning, transformation, storage, and analysis.
Data Presentation
- Data presentation effectively displays data for insightful understanding, showcasing clarity, engagement, context, interactivity, and storytelling.
Data Warehouses vs. Data Lakes
- Data warehouses consolidate, clean, and organize incoming data into a consistent schema, optimizing analysis.
- Data lakes store raw data in its original format, allowing flexible selection and organization.
Data Infrastructure Models
- On-premises: Total management by the user, encompassing networking, storage, servers, virtualization, operating systems, middleware, runtime, data, and applications (e.g., private data centers).
- SaaS (Software as a Service): Vendor-managed systems, where users access applications through the internet (e.g., Gmail, Microsoft Office 365).
- Serverless: Cloud service providers manage servers, leveraging services like AWS, Azure, and GCP.
Data Treatment: Data Lake Zones
- Transient zone: Data ingestion, tagging, and cataloging for addition to the data lake.
- Raw zone: Metadata application, protecting sensitive attributes, and data identification.
- Trusted zone: Data quality and validation assessments, ensuring accuracy.
- Refined zone: Data enrichment and automated workflow processes.
Data Treatment: Data Lake Steps
- Transient loading zone: Ingestion and storage from various sources (e.g., streaming, file data, relational data) without transformations.
- Raw data zone: Unaltered data storage, applying tokenization.
- Refined data zone: Data integration for uniformity.
- Trusted data zone: Provision of reliable, high-quality data.
- Discovery sandbox: Support for experimental analysis and exploration.
Data Consumers
- Consumer systems: Data Catalog, data preparation tools, data visualization, and external connectors.
- Business analytics researchers and data scientists.
Technology Overview
- Data collection technologies from applications and IoT devices.
- Third-party ingestion systems (e.g., MQTT).
- Data preparation and computation within the data lake.
- Data transfer to data warehouses.
- Data presentation.
Other relevant data sources and treatments
- Data sources include OLTP/ODS, Enterprise Data Warehouse, logs, cloud services, streaming, and file data.
- Data sources like streaming, file data, and relational data are pertinent.
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Description
Test your knowledge on the different methods and sources of data collection and processing. This quiz covers aspects of big data infrastructure, data sources, and the various methods of data capture used in the industry. Dive into topics ranging from manual entry to automated systems.
