Prática de Shadowing: Database vs Data Warehouse vs Data Lake | Explaining the Differences - Aprenda a falar inglês com vídeo
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We live in a world that runs on data.
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It's how Amazon and Netflix know which movies and products to recommend, how Starbucks manages a global supply chain,
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and how Uber connects drivers with passengers in real time.
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But the thing is, data skills aren't just for tech companies or professional analysts anymore.
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Everyone works with data to some degree, and everyone can benefit from data literacy skills.
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In this video, we're covering an important topic that will help you take your data literacy to the next level.
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Alright, so accessing data is one thing, but organizing and storing it is a completely different beast.
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So at a high level, some of the most common storage modes that you're likely to encounter include flat files, databases, data warehouses, and data lakes.
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Now, there are other, less common storage methods and hybrid variations of these as well, but our goal here is really just to paint some broad strokes
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and help you understand the key similarities and differences between them.
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Now, the simplest method of data storage is a flat file,
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which is typically a static tabular data extract saved either to a local drive or cloud storage like Google Drive or OneDrive.
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And this is very common for one off projects or quick ad hoc analyses that don't require multiple data sources, ongoing maintenance or complex data models.
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The most common types of flat files you'll see are CSVs and Excel workbooks.
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Now, a database is a collection of related tables stored in a database management system, or DBMS.
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Databases are typically used for recording and collecting data tied to a single application or business process,
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like capturing transactional records or real-time website activity.
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And they're usually optimized for online transactional processing,
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known as OLTP, meaning that they're built to receive and store data as efficiently as possible, but aren't really ideal for querying or analysis.
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Common database options include MySQL, Amazon RDS, Microsoft Access or SQL Server, and many, many more.
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A data warehouse, on the other hand, is a database
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or a collection of data sourced from multiple databases that's typically structured to support specific analytics needs
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and is optimized for online analytical processing, or OLAP.
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That basically just means it's built in a way
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that makes it very easy and very fast for users to access the data they need for analytical purposes.
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examples of data warehouse tools include Amazon Redshift, Google BigQuery, Snowflake, Teradata, and others.
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And finally we have data lakes which are essentially a repository of both structured
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and unstructured data sources often stored in their raw unprocessed state.
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So we're talking about everything from CSV files to MP4s, PDFs, text documents, and so on.
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Instead of getting Being cleaned and prepped on the way in, data is typically extracted from the lake and transformed for specific purposes,
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often relating to machine learning or AI.
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Some examples of data lakes include Amazon S3, Azure Data Lake, Google Cloud, and more.
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Now, if you're feeling a little overwhelmed about what this all means, no need to worry.
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I've worked in data analytics and business intelligence for over 15 years, and I still get confused by this stuff.
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The good news is that unless you plan to become a data engineer, it's extremely unlikely that you would be the one building or configuring databases or warehouses.
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In fact, in most cases, data engineers or architects are the ones primarily responsible for standing up these systems
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and building the pipelines and automations that help the data flow from one place to another.
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If you enjoyed this content and want to see more, we've got a brand new Data Literacy Foundations course, and it's entirely free.
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You can check it out at mavenanalytics.io.
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So whether you're an individual looking to build confidence, a leader seeking to empower and upskill your team, or a data professional just trying to to stay ahead of the curve,
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this is the course for you.
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We've got a lot to cover, so let's dive in.
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Vocabulário e dicas de fala para esta lição
Este vídeo tem 42 frases e 674 palavras para praticar shadowing. A fala dura 4:14. O falante fala em ritmo natural, cerca de 159 palavras por minuto, próximo de uma conversa do dia a dia. Apenas 78% das palavras estão entre as 3.000 mais comuns do inglês, por isso o vocabulário é exigente.
Vocabulário principal deste vídeo
15 palavras menos comuns do vídeo, com pronúncia e significado:
- database /ˈdeɪtəˌbeɪs/ (substantivo) — banco de dados, base de dados. A collection of (usually) organized information in a regular structure, usually but not necessarily in a machine-readable format accessible by a computer.
- warehouse /ˈwɛə(ɹ)haʊs/ (substantivo) — armazém. A facility for storing large amounts of merchandise or products.
- literacy /ˈlɪtəɹəsi/ (substantivo) — alfabetização, alfabetismo. The ability to read and write.
- cloud /ˈklaʊ̯d/ (substantivo) — nuvem, nimbo. A visible mass of water droplets suspended in the air.
- skill /skɪl/ (substantivo) — habilidade, talento. A capacity to do something well; a technique, an ability, usually acquired or learned, as opposed to abilities that are regarded as innate.
- extract /ˈɛkstɹækt/ (substantivo) — extrato. Something that is extracted or drawn out.
- analytics /ˌæn.əˈlɪt.ɪks/ (substantivo) — análise. The principles governing any of various forms of analysis.
- topic /ˈtɑpɪk/ (substantivo) — tópico, assunto. A subject; a theme; a category or general area of interest.
- confused /kənˈfjuːzd/ (adjetivo) — confuso. unable to think clearly or understand
- ideal /aɪˈdi(ə)l/ (adjetivo) — ideal. Pertaining to ideas, or to a given idea.
- capture /ˈkæp.(t)ʃɚ/ (verbo) — capturar. To take control of; to seize by force or stratagem.
- essentially /ɪˈsɛnʃəli/ (advérbio) — em essência, essencialmente. In essence: by nature.
- connect /kəˈnɛkt/ (verbo) — conectar. To join (to another object): to attach, or to be intended to attach or capable of attaching, to another object.
- passenger /ˈpæs.ɪn.d͡ʒɚ/ (substantivo) — passageiro, passageira. One who rides or travels in a vehicle, but who does not operate it and is not a member of the crew.
- collect /ˈkɑlɪkt/ (verbo) — juntar, reunir. To gather together; amass.
Phrasal verbs que você vai ouvir
- stand up (verbo) — levantar. To rise from a lying or sitting position.
Pronúncia para ficar de olho
O falante usa 14 contrações e formas reduzidas, como you're, aren't, they're. Diga-as na forma curta, do jeito que você ouve.
- Os sons de “sh” e “zh”: capture /ˈkæp.(t)ʃɚ/, essentially /ɪˈsɛnʃəli/, variation /ˌvɛəɹiˈeɪʃn̩/, automation /ˌɔ.təˈmeɪ.ʃən/, transactional /trænˈzækʃ(ə)nəl/
- Palavras longas — acerte a sílaba tônica: literacy /ˈlɪtəɹəsi/, analytics /ˌæn.əˈlɪt.ɪks/, analytical /ˌæn.əˈlɪt.ɪ.kəl/, primarily /ˈpɹaɪ.mə.ɹə.li/, essentially /ɪˈsɛnʃəli/
Como praticar com este vídeo
- Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
- Comece na velocidade 0,75×, faça shadowing frase por frase e volte à velocidade normal quando ficar fácil.
- Grave a sua voz e compare com o original, prestando atenção a palavras como database, warehouse, literacy.
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.