Data Engineering Overview
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Questions and Answers

What does ETL stand for in data processing?

  • Extract, Transform, Load (correct)
  • Evaluate, Transform, Load
  • Extract, Transfer, Load
  • Evaluate, Transfer, Load

    • A Data Lake only stores structured data.

    False

    What is the purpose of a data warehouse?

    To aggregate and store large amounts of data for analysis.

    In data processing, the second step of the ETL process is __________.

    <p>Transform</p> Signup and view all the answers

    Which of the following is NOT a source of raw data?

    <p>Processing Outputs</p> Signup and view all the answers

    Match the following data storage solutions with their characteristics:

    <p>Data Warehouse = Structured data storage for analytics Data Lake = Storage for structured and unstructured data ETL Tools = Used for data extraction, transformation, and loading Data Ecosystem = Collection of tools and services for data processing</p> Signup and view all the answers

    Self-service data preparation allows end-users to independently clean and analyze data.

    <p>True</p> Signup and view all the answers

    What is the concept of 'source of truth' in data management?

    <p>A single source where data is deemed to be authoritative and up-to-date.</p> Signup and view all the answers

    Which of the following describes a Data Lake?

    <p>A storage repository that holds vast amounts of raw data in its native format</p> Signup and view all the answers

    Data Warehouses are optimized for both raw data storage and on-the-fly data processing.

    <p>False</p> Signup and view all the answers

    What term describes the process of transforming and cleaning raw data into a usable format?

    <p>ETL (Extract, Transform, Load)</p> Signup and view all the answers

    In a modern data ecosystem, data __________ refers to the set of tools and practices for collecting, preparing, and analyzing data.

    <p>integration</p> Signup and view all the answers

    Match the following components to their roles in the data ecosystem:

    <p>Data Lake = Storage for unstructured data ETL = Data transformation process Data Warehouse = Analytical processing Data Governance = Management of data access and usage</p> Signup and view all the answers

    Which of the following is NOT a feature of a Data Lake?

    <p>Structured query capabilities</p> Signup and view all the answers

    The steady addition of new data creators contributes to data growth in the modern data ecosystem.

    <p>True</p> Signup and view all the answers

    What does the acronym 'ETL' stand for in data engineering?

    <p>Extract, Transform, Load</p> Signup and view all the answers

    Which of the following should be enforced to ensure data integrity in a data warehouse?

    <p>No two products can have the same product ID</p> Signup and view all the answers

    Data lakes primarily store structured data.

    <p>False</p> Signup and view all the answers

    What is the main purpose of data discovery in the context of a data warehouse?

    <p>To explore and understand the types and qualities of the data available.</p> Signup and view all the answers

    Data that is __________ is essential for analyzing and managing changes in various data sources.

    <p>governed</p> Signup and view all the answers

    Match the following metadata types with their definitions:

    <p>Application Metadata = Relationships and constraints between data entities Behavioral Metadata = Tracking the origin of the data Data Quality Metadata = Standards and procedures to ensure data integrity Procedural Metadata = Methods of data processing and management</p> Signup and view all the answers

    What does ETL stand for in data integration techniques?

    <p>Extract, Transform, Load</p> Signup and view all the answers

    Name one technique used for data integration.

    <p>ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform)</p> Signup and view all the answers

    Self-service data preparation allows end-users to land and label data without IT intervention.

    <p>True</p> Signup and view all the answers

    Study Notes

    Data Engineering: What? Why?

    • Data engineering is a crucial component of real-world data science projects.
    • It involves a wide range of activities, such as collecting, collating, extracting, moving, transforming, cleaning, integrating, organizing, representing, storing, and processing data.
    • Data engineers handle large, often messy datasets across various teams and organizations, frequently with unclear or ill-defined objectives.
    • Data engineering tasks are often underappreciated compared to machine learning activities in data science projects, despite the vital role they play.
    • The role of data engineers is expanding rapidly with more roles now available than data scientists.

    Data Science: The Conventional View

    • A data scientist traditionally works alone, using one static, rectangular dataset in main memory.
    • Statistical and machine learning algorithms are applied to predefined objectives.
    • While valuable, this approach often ignores the full picture, particularly when large, dynamic datasets are involved.

    Data Science Today with Data Engineering

    • Modern data science often involves data engineering.
    • Data engineering's activities encompass collecting, collating, extracting, moving, transforming, cleaning, integrating, organizing, representing, storing, and processing data.
    • Data engineering happens across teams and requires working with large, messy datasets that are often non-rectangular.
    • The objectives are frequently unclear. and not well-defined.

    Why Learn Data Engineering?

    • Most time in real-world data science projects is spent on data-related tasks, such as cleaning, moving, and processing data.
    • Data engineering lays the groundwork for machine learning and AI models.
    • There is significant demand for data engineers.
    • Data engineering requires fundamental skills needed for effective data-driven decision-making.

    Modern Data Ecosystem

    • The constant acceleration of data processing speeds and bandwidth leads to the constant creation and improvement of tools for creating, sharing, and consuming data.
    • The increase of data creators and consumers worldwide continuously contributes to the expansion of data.
    • Data's value increases with more data.
    • Organizations leverage data to understand and capitalize on opportunities to distinguish themselves from competitors.

    Key Players in the Modern Data Ecosystem

    • Data Engineers
    • Data Analysts
    • Data Scientists
    • Business Analysts
    • Business Intelligence Analysts

    Data Engineering Lifecycle

    • Data preparation process involves moving raw data into usable formats, often in a process called ETL (Extract, Transform, Load).
    • Data must integrate into existing systems (e.g., warehousing), then be verified and audited.

    ETL vs ELT

    • ETL (Extract, Transform, Load) is a traditional approach.
    • Data transformations are done in SQL, handling structured and unstructured data but requiring deep knowledge of the warehousing tools.
    • ELT (Extract, Load, Transform) is a newer method.
      • Transformations are more flexible, faster, and scalable.

    Metadata

    • Metadata is critical for managing the storage of data and includes information about data entities and their relationships, constraints, lineage, and usage trails.

    Operationalization and Feedback

    • A core aspect of real-world data science is its long-term development and use.
    • Feedback loops provide continuous updates to processes based on the results of experiments, tests, jobs and predictions.
    • Data “products” are crucial, as they often generate insights needed for decision-making.

    Modern Data Solutions

    • Most modern data solutions combine data warehousing and data lakes with many-to-many ETLT transformations.
    • Data flows move between and into these systems as necessary.

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    Data Engineering: What? Why? PDF

    Description

    Explore the crucial role of data engineering in data science projects. This quiz delves into the various activities undertaken by data engineers and contrasts them with the traditional view of data science. Understand the significance of data engineering in handling complex datasets and its impact on the field.

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