Data Engineering Concepts and Practices Quiz
48 Questions
1 Views

Choose a study mode

Play Quiz
Study Flashcards
Spaced Repetition
Chat to lesson

Podcast

Play an AI-generated podcast conversation about this lesson

Questions and Answers

Which of the following is NOT a characteristic of real-time data processing?

  • Data analysis provides insights for immediate action.
  • Data is processed in large chunks. (correct)
  • Data is available to downstream systems shortly after it is produced.
  • Data is processed as it arrives.

    • In a push model, data is retrieved from the source system by the target system.

    False (B)

    What are the three core phases of transforming data?

    Map, Clean, and Normalize

    Data has value when it's used for ______ purposes.

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

    Match the type of analytics with its corresponding description:

    <p>Business Intelligence (BI) = Focuses on the past and present state of a business Operational Analytics = Provides real-time insights into ongoing operations Embedded Analytics = Provides customer-facing insights and reports</p> Signup and view all the answers

    Multitenancy involves storing data for different customers in separate, dedicated tables.

    <p>False (B)</p> Signup and view all the answers

    The ______ is a specialized tool that combines data engineering and machine learning engineering.

    <p>feature store</p> Signup and view all the answers

    What is the primary function of Reverse ETL?

    <p>Reverse ETL takes processed data from the data engineering lifecycle and feeds it back into source systems like production systems and SaaS platforms.</p> Signup and view all the answers

    What is the primary focus of data engineering?

    <p>Movement, manipulation, and management of data (A)</p> Signup and view all the answers

    Data engineering is considered a subdiscipline of data science.

    <p>False (B)</p> Signup and view all the answers

    What does data maturity refer to?

    <p>The progression toward higher data utilization, capabilities, and integration across the organization.</p> Signup and view all the answers

    Data engineering includes the development, implementation, and maintenance of systems to turn raw data into __________ information.

    <p>high-quality</p> Signup and view all the answers

    Match the terms related to data engineering with their descriptions:

    <p>DataOps = Processes that improve data flow throughout the organization Data Architecture = Structure that defines data storage and usage Orchestration = Management of data processes and workflows Data Management = Administrative tasks related to data governance</p> Signup and view all the answers

    Which of the following best describes the trend in data engineering since the 2020s?

    <p>Decentralized and highly abstracted tools (A)</p> Signup and view all the answers

    The field of data engineering includes elements from both software engineering and business intelligence.

    <p>True (A)</p> Signup and view all the answers

    What era marked the beginning of 'big data' in data engineering?

    <p>The early 2000s</p> Signup and view all the answers

    Which stage of the data engineering lifecycle is responsible for turning raw data into a useful product?

    <p>Transformation (C)</p> Signup and view all the answers

    Data ingestion from source systems is typically within direct control of the data engineer.

    <p>False (B)</p> Signup and view all the answers

    Name one essential characteristic that must be evaluated when assessing source systems for data generation.

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

    Data that is seldom queried and appropriate for archival storage is referred to as ______ data.

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

    What is the term for data that is frequently accessed?

    <p>Hot data (B)</p> Signup and view all the answers

    Match the type of data storage with its description:

    <p>Data warehouse = Organized for rapid querying and analysis Data lakehouse = Combines benefits of data lakes and warehouses Object storage = Scalable storage for unstructured data Archival system = Long-term storage for rarely accessed data</p> Signup and view all the answers

    There is a universal storage recommendation that fits all data engineering needs.

    <p>False (B)</p> Signup and view all the answers

    What are the stages encompassed in the data engineering lifecycle?

    <p>Generation, Storage, Ingestion, Transformation, Serving data</p> Signup and view all the answers

    Which of the following is NOT a main category of data governance?

    <p>Quality Assurance (D)</p> Signup and view all the answers

    DataOps aims to decrease the quality of data products.

    <p>False (B)</p> Signup and view all the answers

    What is the purpose of data governance?

    <p>To ensure the quality, integrity, security, and usability of collected data.</p> Signup and view all the answers

    An orchestration engine, such as ______, builds job dependencies.

    <p>Apache Airflow</p> Signup and view all the answers

    Match the following elements of DataOps with their descriptions:

    <p>Automation = Streamlining processes to enhance efficiency Monitoring and Observability = Tracking metrics and analyzing performance Incident Response = Addressing and managing issues quickly</p> Signup and view all the answers

    What does the process of orchestration primarily aim to achieve?

    <p>Coordinating jobs for efficiency (A)</p> Signup and view all the answers

    Infrastructure as Code (IaC) applies engineering practices to the configuration and management of infrastructure.

    <p>True (A)</p> Signup and view all the answers

    What are the three core technical elements of DataOps?

    <p>Automation, monitoring and observability, incident response.</p> Signup and view all the answers

    Which of these best describes a data warehouse?

    <p>A collection of data for reporting and analysis (B)</p> Signup and view all the answers

    Data marts are designed to serve analytics and reporting for multiple suborganisations.

    <p>False (B)</p> Signup and view all the answers

    What architecture is known for separating analytics processes from production databases?

    <p>Data warehouse architecture</p> Signup and view all the answers

    A ____ architecture allows for batch and streaming processing of data.

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

    What is one of the main advantages of a modern data stack?

    <p>Uses cloud-based, plug-and-play components (A)</p> Signup and view all the answers

    Match the following data architectures with their key features:

    <p>Data Lake = Stores unstructured and structured data Data Lakehouse = Integrates data management of warehouses with storage systems Data Mart = Refined subset of a data warehouse Kappa Architecture = Real-time data processing for streaming data</p> Signup and view all the answers

    Data lakes are designed primarily for structured data only.

    <p>False (B)</p> Signup and view all the answers

    What is the primary function of ETL in data architecture?

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

    Enterprise architecture is solely focused on technology design and implementation.

    <p>False (B)</p> Signup and view all the answers

    Which of the following is NOT a principle of good data architecture according to AWS best practices?

    <p>Prioritize user interface design. (D)</p> Signup and view all the answers

    What is the core concept behind pipeline as code in data engineering orchestration?

    <p>Pipeline as code represents the automation and management of data engineering pipelines using code, enabling version control, collaboration, and repeatable processes.</p> Signup and view all the answers

    Data architecture is the design of systems to support the ______ data needs of an enterprise.

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

    What is the main purpose of enterprise architecture?

    <p>To create a roadmap for organizational success. (A)</p> Signup and view all the answers

    Match the following principles of good data architecture with their respective concepts.

    <p>Build loosely coupled systems. = Prioritize security. Be smart with state. = Choose common components wisely. Make reversible decisions. = Plan for failure. Favor managed services. = Architecture is leadership.</p> Signup and view all the answers

    What is the significance of data architecture in relation to enterprise architecture?

    <p>Data architecture is a crucial component of enterprise architecture, as it defines the underlying data systems and how they support the organization's overall strategic goals and operational needs.</p> Signup and view all the answers

    Google's best practices emphasize that data architecture should be static and remain unchanged over time.

    <p>False (B)</p> Signup and view all the answers

    Study Notes

    Data Engineering Definition

    • Data engineering is the movement, manipulation, and management of data.
    • It involves creating interfaces and mechanisms for data flow and access.
    • Data engineers are specialists dedicated to maintaining data availability and usability.
    • Data engineering can be considered a superset of business intelligence and data warehousing, incorporating elements from software engineering.
    • It's the development, implementation, and maintenance of systems that take raw data and produce high-quality, consistent information supporting downstream use cases (e.g., analysis, machine learning).
    • Data engineering involves the intersection of security, data management, DataOps, data architecture, and software engineering.
    • A data engineer manages the entire data engineering lifecycle, beginning with extracting data from source systems and ending with serving data for usage like analysis or machine learning.

    The Data Engineering Lifecycle

    • The lifecycle includes stages: generation, storage, ingestion, transformation, and serving data.
    • These are interrelated and interdependent
    • Undercurrents (e.g., security, data management, DataOps, data architecture, and software engineering) cut across multiple lifecycle stages.

    Data Engineering History

    • Early days (1980s-2000s): Focused on data warehousing and business intelligence.
    • Early 2000s marked the start of contemporary data engineering and the rise of decentralization and breaking apart monolithic services with the emergence of "big data" technologies, like Apache Hadoop, AWS, Amazon S3, and DynamoDB.
    • 2000s/2010s: Saw the simplification of open-source big data tools.
    • 2020s: Trend towards decentralized, modularized, managed, and highly abstracted tools in data lifecycle engineering.
    • Modern data stack: Collection of off-the-shelf open-source and third-party tools assembled to simplify data analysis.

    Data Maturity and the Data Engineer

    • Data engineering complexity depends heavily on company data maturity.
    • Data maturity is the progression toward higher data utilization, capabilities, and organization-wide integration.
    • Data maturity models (e.g., DMM) have different versions.
    • Data maturity stages exist, such as Starting, Scaling, and Leading with data

    Data Engineer Background and Skills

    • Business Responsibilities: Communication (with technical and non-technical people), understanding business/product requirements, understanding agile/devops/dataops, cost control and continuous learning.
    • Technical Responsibilities: Building optimized performance and cost effective architectures using premade or in-house components. Possessing all skills related to the data engineering lifecycle.

    Data Engineers and Other Technical Roles

    • Data engineers are positioned between upstream data producers (like software engineers and data architects) and downstream consumers like data analysts, data scientists, and machine learning engineers.

    The Data Engineering Lifecycle Stages (Details)

    • Generation: Originating data from source systems (relational databases, NoSQL, IoT), and data streams (essential characteristics—persistence, frequency of generation, errors, schema presence).
    • Storage: Data storage choices like warehouses, lakehouses, and databases, with considerations for access frequency (hot, lukewarm, cold data) and storage systems suitability.
    • Ingestion: Data movement from sources with challenges like batch vs streaming and pull vs push models for optimal data flow.
    • Transformation: Processing data to meet downstream needs through mapping, cleaning, normalization, and creating new features. Often used in other lifecycle phases.
    • Serving Data (Analytics): Data value for practical purposes. Analytics core to many enterprises, including business intelligence. Operational analytics focuses on real-time operations in contrast to BI focusing entirely on past and present. Multitenancy commonly used to house customer and analytical data with logical views.
    • Serving Data (Machine Learning): Feature store is a tool that combines data engineering.
    • Reverse ETL: A process that takes processed data and feeds this back into source systems, allowing data to be fed back into production systems or SaaS platforms.

    Major Undercurrents Across the Data Engineering Lifecycle

    • Undercurrents:
    • Security: access control, data governance.
    • Data Management: discoverability, definitions, accountability.
    • DataOps: observability, monitoring, incident response.
    • Data Architecture: analyzing tradeoffs, agility design.
    • Orchestration: coordinating workflows, managing tasks.
    • Software Engineering: programming skills, design patterns.

    Data Management and Governance

    • Data management encompasses processes for planning, execution, and supervision related to data throughout its lifecycle.
    • Data governance focuses on data modeling and design.
    • Key aspects: data lineage, storage, operations, integration, lifecycle management, advanced analytics systems, ethics, and privacy

    Orchestration in Data Engineering

    • Coordinating various data processing jobs efficiently on a schedule, different from simple schedulers.
    • Builds in metadata about job dependency relationships and utilizes DAGs (directed acyclic graphs).
    • Provides job history, visualization, and alerting for improved workflow management. Also includes coordinating workflows.

    DataOps

    • Applying Agile, DevOps, and SPC (statistical process control) principles and practices to data by streamlining data flows.
    • Enables rapid, high-quality data delivery and collaboration for faster insights. Includes collaboration; measuring, monitoring and transparency of results; and automation, incident response, and monitoring as core practices.

    Software Engineering in Data Engineering

    • Core data processing code (SQL) and development of open-source frameworks (like Hadoop ecosystem).
    • Infrastructure as Code (IaC) for configuring and managing infrastructure, pipelines as code for data orchestration, and general-purpose problem-solving skills.

    Enterprise Architecture

    • Holistic view of the entire enterprise information and technology, including services, processes, and infrastructure. This creates a roadmap for success.
    • Enterprise Architecture (EA) is a model aligned with strategy, operations, and technology. It's about designing systems to handle change flexibly with reversible decisions.

    Data Architecture

    • Reflects the current and future state supporting organizational long-term data needs and strategy.
    • Defines structure and interaction among data types, sources, logical assets, and management resources.
    • Flexible and easily maintainable data architecture serves business needs, adaptable to change, and considered an ongoing process

    Principles of Good Data Architecture

    • Principles for data architecture, such as AWS (Operational excellence, Security, Reliability, and Cost Optimization) and Google (Automation, State design, and Managed Services).

    Examples & Types of Data Architectures

    • Data warehouse and data marts
    • Data lakes
    • Data lakehouses
    • Modern data stacks
    • Lambda Architecture
    • Kappa Architecture
    • IoT Architecture
    • Data Mesh

    Data Warehouse

    • Central hub for data used in reporting and analysis, often separates analytics from production tasks, and centrally stores data.
    • Cloud-based warehouses are increasingly accessible. Two primary characteristics are separating analytics from production tasks and centralizing data.

    ETL vs ELT

    • Data movement using ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) architecture, which differentiates based on where data transformation happens. ETL transforms first, then loads. ELT loads and transforms data later.

    Data Marts

    • A subset of a data warehouse tailored to a specific department or business unit, enhancing data accessibility to analysts. Data marts extend transformations beyond the main ETL pipeline.

    Data Lake vs Data Lakehouse

    • Data lake stores structured and unstructured data centrally, while the lakehouse introduces data management controls from data warehouses to manage structured and unstructured data in object storage while offering query and transformation engines.
    • Lakehouse maintains ACID compliance.

    Modern Data Stack

    • Cloud-based, modular, cost-effective data architectures using off-the-shelf components including pipelines, storage, transformation, governance, and monitoring, all with visualization and exploration capabilities.

    Lambda Architecture

    • A system employing batch, stream, and serving processes independently in a response to analyzing streamed data.
    • This method sends data to two destinations (stream and batch processing), creating independent processes for each. Includes several shortcomings.

    Kappa Architecture

    • A data architecture aiming to use a stream processing platform as the backbone for ingestion, storage, and serving that represents a true event-based system.
    • Real-time and batch processing seamlessly handled using a live stream, simplifying transformation for both types of processing.

    Architecture for IoT

    • IoT data architecture for handling distributed devices and collecting data from various internet-connected devices. A distributed environment.

    Data Mesh

    • Represents a recent approach that decentralizes data architecture addressing the challenges of centralized data systems, taking domain-driven design ideas.

    Unified Data Infrastructure (2.0)

    • Describes Unified Data Infrastructure (UDI) which contains data sources, ingestion, transport, storage, query and processing, transformation, and analysis stages in more detail on data structures, tools, and responsibilities.

    Machine Learning Infrastructure (2.0)

    • A diagram describing end-to-end machine learning, showing interconnected processes like data sources, data transformation, model training and development, model inference, and integration stages.

    Blueprint 1 - Modern Business Intelligence

    • Shows a complete data architecture for business intelligence including data sources, ingestion, storage, query and processing, transformation and output stages.

    Resources

    • Joe Reis and Matt Housley provide a book called Fundamentals of Data Engineering, offering practical guidance for constructing data systems that are robust.
    • Matt Bornstein et al. have written about Emerging Architectures for Modern Data Infrastructure.

    Studying That Suits You

    Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

    Quiz Team

    Related Documents

    Data Engineering PDF

    Description

    Test your knowledge on data engineering principles, including real-time data processing, analytics, and data transformation phases. This quiz covers essential concepts needed to understand the role of data engineering within data science. Challenge yourself to see how well you grasp these fundamental topics!

    More Like This

    Data Engineering Skills Quiz
    4 questions

    Data Engineering Skills Quiz

    FastPacedWoodland avatar
    FastPacedWoodland
    Data Engineering Concepts Quiz
    5 questions

    Data Engineering Concepts Quiz

    LuckyOlivine avatar
    LuckyOlivine
    Data Engineering CH01: Introduction
    30 questions

    Data Engineering Quiz: Introduction to CH01

    AltruisticAgate1442 avatar
    AltruisticAgate1442
    Data Engineering Fundamentals
    12 questions

    Data Engineering Fundamentals Quiz: Test Your Skills

    BeauteousBalalaika avatar
    BeauteousBalalaika
    Use Quizgecko on...
    Browser
    Open
    Browser
    Browser