A colleague says: "The team migrated from ETL to ELT after moving to Snowflake."
What is the key architectural difference between ETL and ELT?
ETL vs. ELT — the critical architectural distinction:
Pattern
Flow
Where transformation happens
ETL
Extract → Transform → Load
In a separate pipeline/tool before writing to warehouse
ELT
Extract → Load → Transform
Inside the data warehouse (BigQuery, Snowflake) using its native compute
ELT became practical with scalable cloud DW compute — raw data lands in a "raw zone" first, then SQL/dbt transforms it into a "refined zone". Key vocabulary: transformation layer, raw zone vs. refined zone, dbt for ELT transformations, warehouse-native compute.
2 / 18
A data engineer explains: "The data pipeline ingests events in micro-batches every 30 seconds."
How does this differ from true "real-time streaming"?
A data engineer says: "The SLA requires the reporting dataset to be fresh by 6am."
What does "data freshness" mean in a pipeline context?
Data freshness SLO = the maximum allowed lag before data is considered stale.
Concept
Definition
Freshness check
Compare max(updated_at) vs. current time
Freshness SLO
e.g., data must be no older than 2 hours
Staleness
Data has exceeded the freshness threshold
Tools: dbt source freshness, Great Expectations freshness check. Key vocabulary: freshness SLO, staleness, freshness alert, data availability SLA, pipeline latency.
6 / 18
PR Description
Subject: Update to User Profile Data Pipeline - Added Delta Lake Support
Hi Team,
Please review. We've integrated Delta Lake for improved data consistency and rollback capabilities within the user profile data pipeline. This allows us to efficiently handle schema changes without disrupting downstream consumers.
Thanks,
John
This question tests understanding of a common PR description related to Delta Lake integration. The correct answer highlights that Delta Lake is used for atomic schema evolution – crucial when dealing with changing data structures in pipelines. Options A and B misrepresent Delta Lake's core function; it's not about partitioning or reducing reliance on databases, but rather about managing schema changes *within* the pipeline itself. Option D is incorrect as Delta Lake focuses heavily on transactional capabilities and consistency.
7 / 18
During a code review of the new user profile pipeline PR, Sarah asks: 'Why did we switch to using Change Data Capture (CDC) instead of polling the CRM database? The previous approach was simpler.' What is the primary advantage of CDC over periodic polling in this scenario?
The core benefit of Change Data Capture (CDC) lies in its efficiency and reduced impact on source systems. Polling involves repeatedly querying a database – this creates significant load, especially with large datasets. CDC, conversely, only captures *new* data changes as they occur, minimizing the strain on the CRM database and significantly improving performance; Sarah's concern about simplicity is misleading because CDC offers more robust and scalable change management.
8 / 18
PR Description
Subject: Update to User Profile Data Pipeline - Added Delta Lake Support
Hi Team,
Please review. We've integrated Delta Lake for improved data consistency and rollback capabilities within the user profile data pipeline. This allows us to efficiently handle schema changes without disrupting downstream consumers.
Thanks,
John
This question tests understanding of a common PR description related to Delta Lake integration. The correct answer highlights that Delta Lake is used for atomic schema evolution – crucial when dealing with changing data structures in pipelines. Options A and B misrepresent Delta Lake's core function; it's not about partitioning or reducing reliance on databases, but rather about managing schema changes *within* the pipeline itself. Option D is incorrect as Delta Lake focuses heavily on transactional capabilities and consistency.
9 / 18
During a code review of the new user profile pipeline PR, Sarah asks: 'Why did we switch to using Change Data Capture (CDC) instead of polling the CRM database? The previous approach was simpler.' What is the primary advantage of CDC over periodic polling in this scenario?
The core benefit of Change Data Capture (CDC) lies in its efficiency and reduced impact on source systems. Polling involves repeatedly querying a database – this creates significant load, especially with large datasets. CDC, conversely, only captures *new* data changes as they occur, minimizing the strain on the CRM database and significantly improving performance; Sarah's concern about simplicity is misleading because CDC offers more robust and scalable change management.
10 / 18
PR Description
Subject: Update to User Profile Data Pipeline - Added Delta Lake Support
Hi Team,
Please review. We've integrated Delta Lake for improved data consistency and rollback capabilities within the user profile data pipeline. This allows us to efficiently handle schema changes without disrupting downstream consumers.
Thanks,
John
This question tests understanding of a common PR description related to Delta Lake integration. The correct answer highlights that Delta Lake is used for atomic schema evolution – crucial when dealing with changing data structures in pipelines. Options A and B misrepresent Delta Lake's core function; it's not about partitioning or reducing reliance on databases, but rather about managing schema changes *within* the pipeline itself. Option D is incorrect as Delta Lake focuses heavily on transactional capabilities and consistency.
11 / 18
During a code review of the new user profile pipeline PR, Sarah asks: 'Why did we switch to using Change Data Capture (CDC) instead of polling the CRM database? The previous approach was simpler.' What is the primary advantage of CDC over periodic polling in this scenario?
The core benefit of Change Data Capture (CDC) lies in its efficiency and reduced impact on source systems. Polling involves repeatedly querying a database – this creates significant load, especially with large datasets. CDC, conversely, only captures *new* data changes as they occur, minimizing the strain on the CRM database and significantly improving performance; Sarah's concern about simplicity is misleading because CDC offers more robust and scalable change management.
12 / 18
PR Description
Subject: Update to User Profile Data Pipeline - Added Delta Lake Support
Hi Team,
Please review. We've integrated Delta Lake for improved data consistency and rollback capabilities within the user profile data pipeline. This allows us to efficiently handle schema changes without disrupting downstream consumers.
Thanks,
John
This question tests understanding of a common PR description related to Delta Lake integration. The correct answer highlights that Delta Lake is used for atomic schema evolution – crucial when dealing with changing data structures in pipelines. Options A and B misrepresent Delta Lake's core function; it's not about partitioning or reducing reliance on databases, but rather about managing schema changes *within* the pipeline itself. Option D is incorrect as Delta Lake focuses heavily on transactional capabilities and consistency.
13 / 18
During a code review of the new user profile pipeline PR, Sarah asks: 'Why did we switch to using Change Data Capture (CDC) instead of polling the CRM database? The previous approach was simpler.' What is the primary advantage of CDC over periodic polling in this scenario?
The core benefit of Change Data Capture (CDC) lies in its efficiency and reduced impact on source systems. Polling involves repeatedly querying a database – this creates significant load, especially with large datasets. CDC, conversely, only captures *new* data changes as they occur, minimizing the strain on the CRM database and significantly improving performance; Sarah's concern about simplicity is misleading because CDC offers more robust and scalable change management.
14 / 18
Mark is explaining a new data pipeline to the team. He says, "We're using Kafka Streams for real-time processing of clickstream events." What does 'Kafka Streams' primarily refer to in this context?
Kafka Streams is a powerful stream processing library built *on top* of Kafka. It allows developers to write applications that consume and transform streams of data in real-time, making it ideal for scenarios like clickstream analysis where immediate insights are needed. Options A, C, and D describe other related technologies or services; Kafka Streams specifically focuses on the *processing* aspect.
15 / 18
Sarah comments in a code review: 'This pipeline uses a nightly batch job to load data from our legacy system. It's inefficient and introduces latency.' What is the most likely issue Sarah is highlighting regarding this approach?
Sarah's comment points to the inefficiency of batch loading. Nightly batch jobs are inherently slow and create latency because they process data in large chunks at scheduled intervals. This contrasts with approaches that allow for more immediate updates or real-time processing. Options A, C, and D represent different technical aspects that aren't directly causing the problem described.
16 / 18
John sends a Slack message: 'Just ran into an issue with the staging pipeline – it's stuck in a retry loop. The logs show errors related to schema evolution.' What does 'schema evolution' typically indicate in the context of a data pipeline?
'Schema evolution' refers to changes in the structure or definition of the data being processed – for example, adding new fields or changing existing ones. These changes can break older components that were designed to work with a specific schema, leading to errors and retry loops when the pipeline attempts to process the updated data. Options A, C, and D represent other potential causes of pipeline issues.
17 / 18
A PR description reads: 'Implemented a new data quality check step using Great Expectations to validate incoming sensor readings. This helps ensure data integrity and prevents bad data from entering the downstream pipelines.' What is the primary purpose of using Great Expectations in this scenario?
Great Expectations is a powerful tool for defining and validating data quality rules. In this context, it's being used to ensure that incoming sensor readings meet predefined expectations – meaning it actively checks if the data conforms to expected patterns before allowing it to proceed into downstream pipelines. Options A, C, and D describe other functionalities associated with pipeline management.
18 / 18
During a stand-up meeting, David says: 'We're focusing on improving the latency of our data enrichment pipeline. We're investigating using a message queue to decouple the ingestion and enrichment stages.' What is the key benefit of decoupling these stages through a message queue?
Decoupling the ingestion and enrichment stages using a message queue allows them to operate independently. This reduces dependencies – meaning if one stage experiences issues, it won't directly impact the other. This improves resilience and overall pipeline performance by preventing bottlenecks and allowing for asynchronous processing. Options A, C, and D represent alternative benefits of message queues.
What does the "Data Pipeline Vocabulary" exercise practise?
Practice English vocabulary for data pipelines: ETL vs ELT, micro-batch vs streaming, DAGs, data lineage, and data freshness SLOs for data engineers and analysts.
How many questions are in this exercise?
This exercise has 18 questions, each multiple-choice with a full explanation shown after you answer.
What English level is this exercise for?
This exercise is tagged Intermediate. If the vocabulary feels difficult, browse the Data Engineering Language category page for an easier module to start with.
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Do I get feedback if I answer incorrectly?
Yes — whichever option you choose, right or wrong, you'll immediately see an explanation clarifying the correct term and why the other options don't fit.
Can I retry this exercise?
Yes — once you finish all the questions, a "Try again" button on the results screen resets the exercise so you can practise as many times as you like.
Do I need an account to track my progress?
No account is required. Your progress bar and score for this session are tracked in the browser as you go, but nothing is saved once you leave the page.
Is "Data Pipeline Vocabulary" part of a larger series?
Yes — it's one exercise in the Data Engineering Language category on CoderSlingo. See the category page for the full list of related exercises on similar terminology.
Can I link directly to this exercise?
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Where can I find more exercises like this one?
See the Data Engineering Language category page for related exercises, or browse the main Exercises hub for other IT English topics.