Data Engineering Overview

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Questions and Answers

What are the four stages of a data pipeline, as described in the provided text?

The four stages of a data pipeline are replicate and migrate, ingest, transform, and store.

What are the three main reasons why data engineers apply updates or transformations to raw data?

Data engineers transform raw data to make it usable, add new value, and ensure currency and accuracy.

What is the primary purpose of the 'replicate and migrate' stage in a data pipeline?

The 'replicate and migrate' stage aims to bring data from external or internal systems into Google Cloud for further processing.

Name three tools or options available for ingesting data into Google Cloud.

<p>Three options for ingesting data into Google Cloud are <code>gcloud storage</code>, <code>Datastream</code>, and <code>Transfer Appliance</code>.</p> Signup and view all the answers

What are the key differences between the 'replicate and migrate' stage and the 'ingest' stage of a data pipeline?

<p>'Replicate and migrate' focuses on transferring data into Google Cloud, while 'ingest' is about making that raw data accessible within a specific data source.</p> Signup and view all the answers

What are the three common methods for transforming data, as mentioned in the provided text?

<p>The three methods for transforming data are <code>EL</code> (Extract, Load, Transform), <code>ELT</code> (Extract, Load, Transform), and <code>ETL</code> (Extract, Transform, Load).</p> Signup and view all the answers

What is the key difference between 'EL' and 'ETL' methods of data transformation?

<p>In 'EL' transformation happens after data is loaded, while in 'ETL' data is transformed before it's loaded into the destination.</p> Signup and view all the answers

Explain the role of data sinks in the 'store' stage of a data pipeline.

<p>Data sinks are where processed data is stored after transformations, making the data available for analysis and further usage.</p> Signup and view all the answers

What are the five fundamental steps involved in data engineering?

<p>The five fundamental steps involved in data engineering are: <strong>raw data ingestion and storage</strong>, <strong>data transformation</strong>, <strong>data provisioning and enrichment</strong>, <strong>security, privacy, discovery, governance</strong>, and <strong>pipeline monitoring and automation</strong>.</p> Signup and view all the answers

What is the primary objective of a data engineer in building data pipelines?

<p>The primary objective is to enable data-driven decisions by getting data to where it can be useful and making it accessible for analysis, reporting, or machine learning.</p> Signup and view all the answers

Explain the concept of 'data provisioning and enrichment' as a step in data engineering.

<p>Data provisioning and enrichment involves adding value to the data by providing relevant context, historical information, or other insights that enhance its utility and analytical capabilities.</p> Signup and view all the answers

Why is 'pipeline monitoring and automation' a crucial aspect of data engineering?

<p>Pipeline monitoring and automation ensures the ongoing stability and reliability of the data pipeline, enabling continuous data flow and preventing data processing interruptions.</p> Signup and view all the answers

Provide a brief definition of a 'data source' in the context of data engineering.

<p>A data source is the origin of raw data, typically a system like a database, log file, API, or sensor, that provides the initial input for a data pipeline.</p> Signup and view all the answers

What is the purpose of a 'data sink' in data engineering?

<p>A data sink is the destination for processed data, such as a data warehouse, data lake, or machine learning model, where the data becomes readily available for analysis or further processing.</p> Signup and view all the answers

Describe the key benefits of sharing datasets using Analytics Hub.

<p>Analytics Hub allows for easy sharing of datasets within and outside of an organization, enabling collaboration and data access for a wider audience, fostering data-driven decision making across teams.</p> Signup and view all the answers

Explain the role of metadata management in data engineering.

<p>Metadata management involves organizing and tracking details about the data itself, such as its structure, format, origin, and quality, providing context and facilitating efficient data governance and utilization.</p> Signup and view all the answers

What is a data sink in the context of data processing?

<p>A data sink is the final stop in the data journey where processed and transformed data is stored for future use.</p> Signup and view all the answers

Name two Google Cloud products used in the store phase of a data pipeline.

<p>BigQuery and Bigtable.</p> Signup and view all the answers

What characterizes unstructured data?

<p>Unstructured data is stored in a non-tabular form, such as documents, images, and audio files.</p> Signup and view all the answers

How does structured data differ from unstructured data?

<p>Structured data is organized in tables, rows, and columns, while unstructured data lacks this organization.</p> Signup and view all the answers

What is the significance of the store stage in a data pipeline?

<p>The store stage is crucial as it is when data is deposited in its final form for future analysis and decision-making.</p> Signup and view all the answers

Identify a key benefit of using BigQuery for data storage.

<p>BigQuery provides serverless data warehouse capabilities, enabling scalable analysis without managing infrastructure.</p> Signup and view all the answers

Explain the role of data engineers in data management.

<p>Data engineers design and maintain systems for processing, storing, and analyzing data efficiently.</p> Signup and view all the answers

What types of data formats require different storage solutions on Google Cloud?

<p>Both unstructured and structured data formats require different storage solutions, with unstructured data typically suited for Cloud Storage.</p> Signup and view all the answers

What are the built-in features of BigQuery that enhance data analysis?

<p>BigQuery has built-in machine learning, geospatial analysis, and business intelligence capabilities.</p> Signup and view all the answers

Explain how security is managed in BigQuery.

<p>Security in BigQuery is managed at the dataset, table, column, and row levels.</p> Signup and view all the answers

What is the significance of BigQuery being serverless and fully managed?

<p>Being serverless and fully managed means users do not need to manage servers or infrastructure, allowing them to focus on data analysis.</p> Signup and view all the answers

What types of workloads is BigQuery well-suited for?

<p>BigQuery is well-suited for online analytical processing (OLAP) workloads, big data exploration, and processing.</p> Signup and view all the answers

How can a user access data in BigQuery?

<p>Users can access data in BigQuery via the Google Cloud console’s SQL editor, the bq command line tool, or a REST API.</p> Signup and view all the answers

What is the performance capability of BigQuery regarding data scanning?

<p>BigQuery can scan terabytes in seconds and petabytes in minutes.</p> Signup and view all the answers

Describe the role of the bq command line tool in BigQuery.

<p>The <code>bq</code> command line tool allows users to execute queries and manage data within BigQuery via command line.</p> Signup and view all the answers

What advantages does BigQuery offer for real-time analytics?

<p>BigQuery provides real-time analytics capabilities on streaming data.</p> Signup and view all the answers

What challenges does data sharing outside an organization entail?

<p>Security and permissions, destination options for data pipelines, data freshness and accuracy, and usage monitoring are key challenges.</p> Signup and view all the answers

How does Analytics Hub simplify data sharing across organizations?

<p>Analytics Hub allows for easy publishing and subscribing to datasets while maintaining control and monitoring over the data shared.</p> Signup and view all the answers

What is the role of a publisher project in Analytics Hub?

<p>The publisher project is responsible for creating and managing the datasets that are shared through Analytics Hub.</p> Signup and view all the answers

Explain the significance of self-service access to data in Analytics Hub.

<p>Self-service access allows users to easily obtain valuable and trusted data assets without relying on complex approval processes.</p> Signup and view all the answers

What is one benefit of sharing data 'in place' in Analytics Hub?

<p>Sharing data 'in place' allows data providers to maintain control over their datasets while enabling users to access them directly.</p> Signup and view all the answers

How does Analytics Hub support monetization of data assets?

<p>Analytics Hub provides the infrastructure needed for organizations to monetize their data assets without the overhead of building the necessary systems.</p> Signup and view all the answers

Identify two key steps users take when interacting with shared datasets in Analytics Hub.

<p>Users first search for datasets and then subscribe to them for access.</p> Signup and view all the answers

What complexities might arise from managing IAM in the context of Analytics Hub?

<p>Complex permissions and user roles can create challenges when trying to ensure that only authorized users have access to the shared data.</p> Signup and view all the answers

What is the purpose of the ingest stage in a data pipeline?

<p>The ingest stage is where raw data becomes a data source and is made available for downstream usage.</p> Signup and view all the answers

Name two Google Cloud products used during the ingest phase.

<p>Cloud Storage and Pub/Sub.</p> Signup and view all the answers

What does the transform stage in a data pipeline involve?

<p>The transform stage involves adjusting, modifying, joining, or customizing a data source for specific reporting needs.</p> Signup and view all the answers

List the three main transformation patterns commonly used.

<p>Extract and load, extract, load, and transform, extract, transform, and load.</p> Signup and view all the answers

What defines a data source within the Google Cloud environment?

<p>A data source is any system, application, or platform that creates, stores, or shares raw data.</p> Signup and view all the answers

How does asynchronous messaging contribute to data ingestion?

<p>Asynchronous messaging allows for the real-time delivery of data from external systems to the cloud.</p> Signup and view all the answers

What role does metadata management play on Google Cloud?

<p>Metadata management helps organize, manage, and interpret the data regarding its source, usage, and structure.</p> Signup and view all the answers

What are data sinks in the context of data engineering?

<p>Data sinks are endpoints where data is stored or consumed after passing through the data pipeline.</p> Signup and view all the answers

Flashcards

Role of a Data Engineer

A data engineer builds data pipelines for data-driven decisions.

Data Pipeline

A system that collects, processes, and transports data to where it's needed.

Data Source

The origin point from which data is collected or ingested.

Data Sink

The endpoint where data is stored or used after processing.

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Data Transformation

The process of converting data into a usable format.

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Google Cloud Storage Solutions

Options provided by Google Cloud for storing and managing data.

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Metadata Management

Processes for managing data about other data for organization and retrieval.

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Analytics Hub

A platform in Google Cloud for sharing datasets easily across organizations.

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Ingest Stage

The point in a data pipeline where data becomes a data source.

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Cloud Storage

A Google Cloud product that serves as a data lake holding various types of data sources.

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Pub/Sub

An asynchronous messaging system in Google Cloud that delivers data from external systems.

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Transformation Services

Services that add new value to your data by modifying or adjusting it for downstream use.

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Transformation Patterns

Methods like extract and load, extract, transform, and load used in data processing.

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Extract and Load

A transformation pattern where data is extracted and loaded directly to storage.

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Extract, Transform, Load

A sequence where data is extracted, modified, and then loaded for use.

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Usable Data

Data that is in a condition suitable for decision-making.

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Data Engineer Role

A professional who ingests, transforms, and stores data.

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Data Pipeline Stages

Four key steps are replicate, ingest, transform, and store.

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Replication and Migration

The first stage of a data pipeline, focusing on moving data to cloud.

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Ingest

The process of pulling raw data into a system.

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Transform

Processing raw data to make it useful.

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Store

The final step where processed data is saved.

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Store Stage

The final step in a data pipeline where data is deposited in its final form.

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BigQuery

A serverless data warehouse used for storing structured and unstructured data.

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Bigtable

A highly scalable NoSQL database designed for large analytical workloads.

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Structured Data

Information stored in a tabular form, organized into rows and columns.

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Unstructured Data

Information stored in non-tabular formats such as documents and audio.

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Data Formats

Different ways in which data can be stored or processed, mainly structured and unstructured.

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Ingestion Process

The stage in data pipelines where data is collected and prepared for processing.

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Views in BigQuery

Virtual tables created based on SQL queries without storing data.

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ML Models in BigQuery

Machine learning algorithms built and executed in BigQuery using SQL queries.

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Data Sharing Challenges

Issues regarding security, permissions, and data freshness when sharing data.

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Publisher Project

The project that hosts datasets for sharing in Analytics Hub.

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Subscriber Project

The project that consumes datasets shared through Analytics Hub.

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Analytics Hub Features

Facilitates easy data sharing while monitoring data usage and access.

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Data Monetization

The process of generating revenue from data assets shared via Analytics Hub.

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Private vs Public Data Exchange

Conditions of sharing data either among organizations or publicly for wider access.

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BigQuery Overview

A serverless, fully managed enterprise data warehouse for analytics.

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Built-in Machine Learning

BigQuery includes features for machine learning directly in the data warehouse.

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Geospatial Analysis

The capability of BigQuery to analyze geographical data.

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Real-time Analytics

BigQuery can process and analyze streaming data instantly.

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OLAP Workloads

BigQuery is designed for online analytical processing and big data exploration.

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bq Command Line Tool

A tool that allows command-line access to BigQuery functionalities.

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Google Cloud Console SQL Editor

An easy-to-use interface for querying data in BigQuery.

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REST API Support

BigQuery provides a REST API accessible in seven programming languages.

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Study Notes

Data Engineering Tasks and Components

  • Data engineers build data pipelines to enable data-driven decisions
  • Data pipelines move data from sources to sinks
  • Stages in data pipeline: replicate and migrate, ingest, transform, and store
  • Data sources are the origin of raw data; examples include Cloud Storage and Pub/Sub
  • Data sinks store processed data; examples include BigQuery and Bigtable
  • Data can be structured or unstructured
  • Structured data is stored in tables, rows, and columns
  • Unstructured data is in formats like documents, images, and audio files

Role of a Data Engineer

  • Data engineers are responsible for building data pipelines
  • They get data into usable formats for decision-making
  • They manage data, apply transformations as needed, and ensure data currency

Data Sources vs. Data Sinks

  • Data sources are where raw data originates and is available
  • Data sinks are storage locations for processed data

Data Formats

  • Data can be structured (tables, rows, columns) or unstructured (documents, images, audio)

Storage Solutions on Google Cloud

  • Options for storing structured data: Cloud SQL, AlloyDB, Spanner, Firestore, BigQuery, Bigtable
  • Cloud Storage for unstructured data

Metadata Management on Google Cloud

  • Managing metadata is crucial for data discovery and governance
  • Dataplex is a solution for centrally discovering, managing, monitoring, and governing distributed data

Sharing Datasets Using Analytics Hub

  • Analytics Hub is for sharing data across organizations
  • Facilitates data usage monitoring and control

Data Lake versus Data Warehouse

  • The data lake is a vast repository for raw data in varied formats. It's ideal for data exploration, science, and decisions
  • The data warehouse houses pre-processed and aggregated data, optimized for analysis and reporting

BigQuery

  • BigQuery is a serverless enterprise data warehouse for analytics
  • It's highly scalable and efficient

BigQuery Features

  • Security features (dataset, table, column, row level)
  • Built-in machine learning, geospatial analysis, and business intelligence (BI) functionalities
  • Supports real-time analytics on streaming data

BigQuery Data Organization

  • BigQuery organizes data into projects, datasets, and tables
  • Access control is through IAM, allowing granular control at different levels (dataset, table, view, column)

Dataplex

  • Dataplex centralizes data management across various sources
  • This tool helps with data discovery, management, and governance
  • Facilitates better data sharing and access.

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Google Cloud Data Engineering Tasks and Components PDF

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