Data Engineering: Types and Handling

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Questions and Answers

In data engineering, how is data primarily utilized?

  • To create artistic visualizations.
  • To archive historical documents.
  • To directly control physical machinery.
  • To structure, store, and enable efficient processing, analytics, and decision-making. (correct)

Which characteristic is most indicative of structured data?

  • Organization within a fixed schema. (correct)
  • Storage in object storage systems.
  • Lack of predefined organization.
  • Incompatibility with SQL databases.

What type of database is typically used for storing structured data?

  • Document databases.
  • NoSQL databases.
  • Relational databases. (correct)
  • Graph databases.

Which of the following is an example of structured data?

<p>Customer records in a relational database. (B)</p>
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Which statement best describes semi-structured data?

<p>Data that has some organizational properties but does not fit a strict schema. (A)</p>
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Where might you typically find semi-structured data stored?

<p>NoSQL databases, Cloud storage, and Data Lakes. (C)</p>
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How is unstructured data best described?

<p>Data without a predefined format. (B)</p>
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Where is unstructured data typically stored?

<p>Data Lakes and Object Storage. (C)</p>
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Which of the following is considered unstructured data?

<p>Images and audio files. (B)</p>
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What is the primary purpose of metadata?

<p>To provide information describing other data. (D)</p>
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Where is metadata typically stored?

<p>Metadata repositories and Catalogs. (A)</p>
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Which of the following is an example of metadata?

<p>File size, format, and creation date. (B)</p>
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What characterizes operational data?

<p>Its role in real-time transaction logs. (A)</p>
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What is the primary use of analytical data?

<p>To process reports for decision-making. (C)</p>
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For what purpose is historical data primarily used?

<p>To store past sales records for trend analysis. (B)</p>
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What type of data is typically associated with streaming IoT sensor logs?

<p>Real-Time data. (B)</p>
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Which processing tool is commonly used with SQL databases for structured data?

<p>Apache Airflow. (B)</p>
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Which data processing tools are commonly associated with NoSQL databases for semi-structured data?

<p>Spark and Kafka. (D)</p>
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What processing methods are typically applied to unstructured data stored in data lakes?

<p>AI/ML Processing. (A)</p>
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What is the easiest method to extract structured data?

<p>SQL (C)</p>
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What is the common first step in analyzing unstructured data?

<p>Preprocessing (B)</p>
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What tools are needed to analyze streaming data?

<p>Kafka or Flink (D)</p>
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Which of the following is an example of a numeric data type?

<p>Order ID (D)</p>
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Which of the following is an example of a text/string data type?

<p>Product Name (B)</p>
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Which of the following is an example of a Date/Time data type?

<p>Order Date (D)</p>
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Which of the following is an example of a Boolean data type?

<p>Is Shipped (C)</p>
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Which of the following is an example of a Binary data type?

<p>Image Files (B)</p>
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What is one characteristic of transactional data?

<p>Records of events (D)</p>
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What is one characteristic of master data?

<p>Core entities (A)</p>
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What is one characteristic of reference data?

<p>Standardized lookups (B)</p>
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What is one characteristic of streaming data?

<p>Real-time flows (D)</p>
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How is batch data processed?

<p>Chunks (C)</p>
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How is streaming data processed?

<p>Real-time (B)</p>
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How is historical data processed?

<p>Past records (B)</p>
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How is real-time data used?

<p>Immediate updates (C)</p>
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In the context of data engineering, which statement most accurately reflects the role of data?

<p>Data acts as a dynamic resource, undergoing continuous transformation to generate actionable insights and drive complex systems. (C)</p>
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Which of the following characteristics is most definitive of structured data's utility in data engineering workflows?

<p>Its rigid adherence to a predefined schema facilitates efficient querying and manipulation via standard SQL. (B)</p>
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Consider a scenario involving customer records, sales transactions, and employee databases. From a data engineering perspective, how should this dataset be categorized based on structure?

<p>Strictly structured, characterized by a fixed relational schema enabling SQL-based querying. (C)</p>
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How does semi-structured data distinguish itself from structured data in terms of schema adherence and querying methodologies?

<p>It accommodates a flexible schema amenable to NoSQL storage and query approaches. (D)</p>
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In a data engineering context, how does the absence of a predefined format in unstructured data impact its handling and analytical potential?

<p>It mandates specialized techniques such as AI and ML. (A)</p>
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Given a scenario where a data engineer extracts raw images and videos from AWS S3, what classification best describes this data?

<p>Unstructured, demanding specialized preprocessing and data lake storage. (D)</p>
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In the realm of data engineering, how does metadata augment the utility and manageability of raw data assets?

<p>By providing contextual information that enhances searchability, governance, and analytical workflows. (C)</p>
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Consider a data engineering task involving file size, format, and creation date. How are these data elements typically categorized and utilized?

<p>Metadata, informing data governance and management practices. (D)</p>
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In the classification of data within data engineering, how would you characterize real-time transaction logs from a high-frequency trading platform?

<p>Operational data, powering immediate system actions. (C)</p>
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When considering past sales records used for trend analysis, how should this data be classified within the context of data engineering?

<p>Historical data, reserved for retrospective examination and forecasting. (D)</p>
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In the context of data storage and processing, what is the most appropriate tool to pair with SQL databases for structured data transformation?

<p>Apache Airflow with ETL Pipelines, designed for structured data transformation. (C)</p>
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When dealing with NoSQL databases storing semi-structured data, which processing tools are most aligned for data transformation and analysis?

<p>Spark and Kafka, designed for semi-structured data. (B)</p>
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Considering the Big Data context, how does processing data in 'chunks' impact the ETL/ELT pipeline?

<p>Facilitates efficient processing of large static datasets (B)</p>
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Within data engineering, what is the most critical characteristic of processing streaming data in real-time?

<p>Supporting decisions based on immediate data updates (C)</p>
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When standardizing country codes and categories, which of the following data classifications should be applied?

<p>Reference (C)</p>
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In the context of retail data engineering, how would product images and customer reviews predominantly exist?

<p>As unstructured data, stored in S3 for preprocessing with AI tools (C)</p>
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In an ELT (Extract, Load, Transform) paradigm, what strategic rationale underlies the decision to load raw CSV and JSON data into a data lake before transforming it?

<p>All of the above. (D)</p>
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Concerning data storage, which option offers the most scalable and cost-effective solution?

<p>Object Storage (D)</p>
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How does real-time data processing impact the ability to manage fraud scenarios effectively?

<p>It alerts on potential fraud as it occurs (B)</p>
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Which data pipeline tool mentioned streamlines ETL operations?

<p>Airflow (B)</p>
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In the context of data engineering, which of the following reflects the most critical role of structuring data?

<p>Facilitating efficient storage, processing, analytics, and decision-making. (D)</p>
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Which distinction primarily differentiates semi-structured data from structured data?

<p>Semi-structured data does not conform to a fixed schema but contains tags or markers, whereas structured data adheres to a rigid schema. (B)</p>
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Considering the challenges in data processing, what is the main reason unstructured data typically requires specialized tools?

<p>Unstructured data lacks a predefined format, making it difficult to analyze directly with standard analytical tools. (D)</p>
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How does the use of metadata enhance data management practices in a data engineering context?

<p>By providing descriptive information that facilitates data discovery, usage, and governance. (C)</p>
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In what key aspect does analytical data differ from operational data?

<p>Analytical data is derived from operational data and used for decision-making, while operational data captures immediate transactional details. (D)</p>
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What is the primary reason historical data is crucial in data engineering?

<p>It supports the discovery of trends and patterns to inform future strategies. (B)</p>
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What is a significant challenge when processing streaming IoT sensor logs?

<p>The high velocity and volume of data necessitates real-time processing and analysis. (B)</p>
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In the processing of semi-structured data, what challenge does 'JSON flattening' specifically address?

<p>Simplifying nested JSON structures into a tabular format for easier querying. (C)</p>
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What preprocessing step is often required when dealing with unstructured data such as images, and why is it necessary?

<p>OCR (Optical Character Recognition), to extract text and make the content analyzable. (A)</p>
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When extracting data through SQL for structured data, what advantage does this approach offer?

<p>SQL provides a standardized way to query, manipulate, and extract data from relational databases. (A)</p>
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Which of the following is a critical consideration when choosing tools for analyzing streaming data?

<p>The capability to process data in real-time with low latency. (C)</p>
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In the context of data engineering, how does classifying data by its 'nature' or 'data type' (e.g., numeric, text, boolean) primarily aid in data processing?

<p>It informs the appropriate methods for data validation, transformation, and storage. (A)</p>
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How does the classification of data by 'source or usage' (e.g., transactional, master, reference) enhance data governance?

<p>By defining roles, access controls, and compliance requirements based on data sensitivity and purpose. (D)</p>
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In what way does 'volume and time' context (Batch, Streaming, Historical, Real-Time) affect the choice of data processing technologies in big data?

<p>By influencing the selection of architectures and tools optimized for varying velocities, volumes, and latency requirements. (D)</p>
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What is the most significant rationale for why data engineers should understand the different ways to classify data?

<p>To efficiently manage data and align data processing strategies with specific data characteristics. (C)</p>
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In the context of ETL/ELT pipelines, what is the advantage of extracting structured data using SQL?

<p>SQL enables precise, efficient data retrieval and transformation using joins and aggregations, ideal for structured data. (D)</p>
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What is one of the primary reasons for utilizing tools such as Kafka or Flink, instead of traditional batch ETL processes, when dealing with streaming data?

<p>Tools like Kafka and Flink offer real-time processing capabilities that traditional batch ETL lacks, essential for managing high-velocity data streams. (D)</p>
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During data engineering, what best describes the use of semi-structured data in a practical retail scenario involving APIs?

<p>Data from API responses is extracted, transformed by flattening, and then loaded into a NoSQL store or data warehouse. (C)</p>
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In processing unstructured data within a data lake, what functionality does AI often provide after raw extraction and storage?

<p>AI tools support OCR, sentiment analysis, and other deep content analysis to extract insights. (D)</p>
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In an ELT (Extract, Load, Transform) approach, what is the significance of loading raw CSV and JSON data into a data lake before transformation?

<p>It allows transformations to leverage SQL within a robust, scalable environment like Snowflake, enabling flexibility and efficiency. (B)</p>
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Flashcards

Data

Raw facts or observations collected from different sources, processed to generate insights.

Structured Data

Data organized in a fixed schema with tables, rows, and columns, typically stored in relational databases (SQL).

Semi-Structured Data

Data that doesn't fit a strict schema but still has structure (e.g., JSON, XML), stored in NoSQL databases or data lakes.

Unstructured Data

Data without a predefined format, stored in Data Lakes or Object Storage (AWS S3, Google Cloud Storage).

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Metadata

Information describing other data, stored in metadata repositories or catalogs.

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Transactional Data

Records of events such as sales, clicks, and logins.

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Master Data

Core entities, such as customer information and product catalogs.

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Reference Data

Standardized lookups such as country codes and categories.

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Metadata

Data about data, such as file size and creation date.

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Streaming Data

Real-time flows of data, such as sensor readings and stock prices.

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Batch Data

Data that has been processed in chunks, like daily sales reports.

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Streaming Data

Data that is continuous and real-time, like website traffic.

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Historical Data

Past records for trends, such as yearly sales.

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Real-Time Data

Data providing immediate, live updates, such as fraud alerts.

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Data engineering

Data structured and stored efficiently for processing and decision-making.

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Numeric Data Type

Integers or decimals, representing quantifiable values.

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Text/String Data Type

Names or descriptions represented as text.

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Date/Time Data Type

Timestamps representing specific points in time.

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Boolean Data Type

True or False values indicating a state.

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Binary Data Type

Raw bytes, (e.g., image files).

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What is Structured Data?

Organized in a fixed schema with tables, rows and columns and easily queried using SQL

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What is Semi-Structured Data?

Has some organization using tags and keys, is flexible and self-describing, and commonly seen as APIs or document stores.

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What is Unstructured Data?

It has no predefined structure, is raw and messy, and requires specialized tools to process.

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Study Notes

  • Data is raw information that is gathered from various sources.
  • Data is processed and analyzed, and used to generate insights.
  • Data engineering involves structuring and storing data efficiently.
  • Efficient data handling is essential for processing, analytics, and decision-making.
  • Data is raw facts or observations that represent something about the world.
  • Data can be numbers, text, images, timestamps, or sensor readings.
  • Data is typically stored in systems like databases, files, or streams.
  • Without context, data is just noise; data engineering gives it structure and meaning.
  • Data engineering deals with a variety of data types, classified by their structure, source, or nature.

Types of Data in Data Engineering

  • Data types include structured, semi-structured, unstructured, and metadata.
  • Data categorization by structure is the most common way data engineers categorize data.

Structured Data

  • Structured Data is organized in a fixed format, usually tables with rows and columns.
  • Structured data is easy to query with tools like SQL.
  • Relational databases (SQL) are often used to store structured data.
  • Examples include customer records (name, email, phone).
  • Sales transactions (order_id, amount, date) are also an example of structured data.
  • Employee databases (emp_id, salary, department) are also an example of structured data.
  • A database table of orders (order_id, customer_id, amount, date) is an example.
  • CSV files with sales records are an example of structured data.
  • Use cases include relational databases (e.g., MySQL, PostgreSQL).
  • CREATE TABLE sales ( order_id INT PRIMARY KEY, customer_id INT, amount DECIMAL(10,2), order_date DATE );
  • Structured data is easy to extract, transform, and load into a warehouse with SQL.

Semi-Structured Data

  • Semi-Structured Data has some organization but does not fit a strict schema like JSON or XML.
  • Semi-structured data is flexible and self-describing.
  • NoSQL databases, cloud storage, and data lakes store semi-structured data.
  • JSON API responses are a type of semi-structured data ex. {"user": {"id": 101, "name": "Alice"}}.
  • XML configuration files, emails with attachments are types of semi-structured data.
  • Has some organization (e.g., tags, keys) but isn't as rigid as tables.
  • JSON: {"order_id": 1, "customer": {"id": 101, "name": "Alice"}}.
  • XML, log files, or NoSQL documents (e.g., MongoDB) are semi-structured.
  • Use cases include APIs, data lakes, or document stores.
  • Example:
{
    "order_id": 101,
    "customer": {
        "name": "Alice",
        "email": "[email protected]"
    },
    "items": ["Laptop", "Mouse"]
}
  • Semi-structured data may need parsing before loading.

Unstructured Data

  • Unstructured Data lacks a predefined format and is raw and messy.
  • Unstructured data is harder to process without specialized tools.
  • Data lakes and object storage systems like AWS S3 and Google Cloud Storage are used for storage.
  • Examples include images, videos, audio files, social media posts, and sensor data.
  • Raw images and videos stored in AWS S3 are considered unstructured data.
  • Text documents stored in Hadoop HDFS are also unstructured data.
  • Text files (e.g., emails, reports) are examples of unstructured data.
  • Images, videos, audio (e.g., product photos, call recordings) are examples of unstructured data.
  • Use cases include machine learning, content analysis (often stored in data lakes like S3).
  • Unstructured data often requires preprocessing and may be stored in a data lake for later analysis.

Metadata

  • Metadata is data that describes other data.
  • Metadata repositories and catalogs like Apache Hive and AWS Glue store metadata.
  • Examples of metadata include file size, format, creation date, and database schema details.
  • {"filename": "profile_pic.jpg", "size": "2MB", "format": "JPEG", "created_at": "2025-03-30"}.

Classification of Data in Data Engineering

  • Operational Data: Real-time transaction logs.
  • Analytical Data: Processed reports for decision-making.
  • Historical Data: Past sales records stored for trends.
  • Real-Time Data: Streaming IoT sensor logs.

Data Storage & Processing in Data Engineering

  • Structured Data: Stored in SQL databases (PostgreSQL, MySQL) and processed with Apache Airflow and ETL Pipelines.
  • Semi-Structured Data: Stored in NoSQL databases (MongoDB, DynamoDB) and processed with Spark and Kafka.
  • Unstructured Data: Stored in Data Lakes (AWS S3, Hadoop) and processed with AI/ML.

Data Types By Nature

  • Numeric: Integers, floats/decimals (e.g., amount: 75.50).
  • Text/String: Names, descriptions (e.g., product_name: "Widget").
  • Date/Time: Timestamps (e.g., order_date: "2025-03-31").
  • Boolean: True/False flags (e.g., is_shipped: True).
  • Binary: Raw bytes (e.g., image files in a database).

Data Types By Source or Usage

  • Transactional Data: Records of events (e.g., sales, clicks, logins).
  • Master Data: Core entities (e.g., customer info, product catalogs).
  • Reference Data: Standardized lookups (e.g., country codes, categories).
  • Metadata: Data about data (e.g., file size, creation date).
  • Streaming Data: Real-time flows (e.g., sensor readings, stock prices).

Data Types By Volume/Time

  • Batch Data: Processed in chunks (e.g., daily sales reports).
  • Streaming Data: Continuous, real-time (e.g., website traffic).
  • Historical Data: Past records for trends (e.g., yearly sales).
  • Real-Time Data: Immediate, live updates (e.g., fraud alerts).

Practical Examples

  • Structured Data: Easy to extract with SQL, transform with joins, and load into a warehouse.
  • Semi-Structured Data: Might need parsing (e.g., JSON flattening) before loading.
  • Unstructured Data: Often requires preprocessing (e.g., OCR for text in images) or storage in a data lake for later analysis.
  • Streaming Data: Needs tools like Kafka or Flink instead of traditional batch ETL.
  • Structured Data: Orders table (order_id: 1, customer_id: 101...) extracted via SQL to warehouse.
  • Semi-Structured Data: API response transformed, extracted using requests.
  • Unstructured Data: Processed with AI tools, extracted from a file system, stored in S3.
  • Streaming Data: Real-time inventory extracted via Kafka, loaded into a live dashboard.

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