Data Engineering Overview

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

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

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

<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

<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

<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

<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

<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

<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

<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

<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

<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

<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

<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

<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

<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

<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

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

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

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

<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

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

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

<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

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

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

<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

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

<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

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

<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

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

<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

<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

<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

<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

<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

<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

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

<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

<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

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

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

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

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

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

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

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

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

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

The origin point from which data is collected or ingested.

The endpoint where data is stored or used after processing.

The process of converting data into a usable format.

Options provided by Google Cloud for storing and managing data.

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

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

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

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

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

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

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

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

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

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

A professional who ingests, transforms, and stores data.

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

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

The process of pulling raw data into a system.

Processing raw data to make it useful.

The final step where processed data is saved.

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

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

A highly scalable NoSQL database designed for large analytical workloads.

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

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

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

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

Virtual tables created based on SQL queries without storing data.

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

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

The project that hosts datasets for sharing in Analytics Hub.

The project that consumes datasets shared through Analytics Hub.

Facilitates easy data sharing while monitoring data usage and access.

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

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

A serverless, fully managed enterprise data warehouse for analytics.

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

The capability of BigQuery to analyze geographical data.

BigQuery can process and analyze streaming data instantly.

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

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

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

BigQuery provides a REST API accessible in seven programming languages.