Data Engineering Data Ingestion Patterns Questions and Answers

Question 1

A financial services company needs to replicate its transactional OLTP database to a cloud data warehouse for real-time analytics.
The key requirements are to capture every individual row-level change (inserts, updates, and deletes) with minimal performance impact on the source database and to ensure data arrives at the destination with very low latency.
Which ingestion pattern is most suitable for this use case?

Accepted Answer

Change Data Capture (CDC) utilizing the database's transaction log.

Answer

Scheduled hourly queries against a `last_modified` timestamp column.

Answer

Nightly full database backups followed by a restore to the analytics environment.

Answer

Trigger-based logging that writes every change to a separate audit table, which is then queried hourly.

Question 2

In the context of ingesting data from a third-party SaaS application's API, which statement best describes the fundamental difference between a webhook-based approach and a polling-based approach?

Accepted Answer

A webhook is a 'push' mechanism where the source system sends data upon an event, while polling is a 'pull' mechanism where the consumer periodically requests data.

Answer

Webhooks are a 'pull' mechanism where the data consumer repeatedly requests data, while polling is a 'push' mechanism where the source sends data.

Answer

Polling is event-driven and provides the lowest possible latency, while webhooks operate on a fixed schedule, introducing delays.

Answer

Webhooks are more resource-intensive for the source system because they must respond to constant requests, whereas polling is more efficient.

Question 3

A large e-commerce platform runs its application on hundreds of virtual machines. The operations team needs to centralize all application logs in real-time for monitoring and security analysis. Which of the following represents the most common and effective architectural pattern for this task?

Accepted Answer

Installing a lightweight agent (e.g., Fluentd, Filebeat) on each server to tail log files and stream events to a central log aggregator.

Answer

Configuring a cron job on each server to batch-compress and upload log files to cloud storage every hour.

Answer

Directly writing logs from the application on each server to a central relational database.

Answer

Writing a custom script on each server to periodically use SSH/SCP to copy log files to a central server.

Question 4

A data engineer is designing an ingestion pipeline for a large operational database that does not have Change Data Capture (CDC) enabled. The goal is to create a daily updated copy in the data warehouse. The source table is very large and has a `last_updated_at` timestamp column that is reliably updated on every change. Which ingestion strategy is most efficient for minimizing both the load on the source system and the amount of data transferred daily?

Accepted Answer

An incremental load using a watermark based on the `last_updated_at` column.

Answer

A full table dump and reload each day.

Answer

Exporting the entire table to a file and using a `diff` utility against the previous day's file.

Answer

Implementing trigger-based replication on every row operation within the source database.

Question 5

An IoT company is ingesting high-throughput telemetry data from millions of devices. The data arrives in unpredictable bursts and needs to be processed by a downstream analytics application that can sometimes experience slowdowns or require maintenance. Which architectural component is crucial for decoupling the data producers from the consumers and preventing data loss during consumer downtime?

Accepted Answer

A distributed message queue or pub/sub system like Apache Kafka or Google Cloud Pub/Sub.

Answer

A load balancer directing traffic directly to multiple instances of the analytics application.

Answer

A relational database to immediately persist the raw telemetry data upon arrival.

Answer

A distributed in-memory cache like Redis to hold the most recent data.

Question 6

In the context of data ingestion pipelines, why is designing for idempotency a critical principle, especially when dealing with batch processing that may need to be re-run after a failure?

Accepted Answer

It ensures that re-running a pipeline with the same input data multiple times produces the same final result without creating duplicates or errors.

Answer

It ensures that data is encrypted during transit to meet security requirements.

Answer

It guarantees that data is processed in the exact order it was generated by the source system.

Answer

It reduces the latency of data ingestion by processing multiple inputs in parallel.