Question 1

What is the difference between ETL and ELT?

Accepted Answer

ETL (Extract, Transform, Load) transforms data before loading it into the warehouse, ideal for privacy compliance. ELT loads raw data first and transforms it inside the warehouse, leveraging warehouse compute power.

Question 2

Which data warehouses do you support?

Accepted Answer

We build pipelines for all major data warehouses, including Snowflake, Google BigQuery, Amazon Redshift, and Microsoft Azure Synapse, as well as traditional databases like PostgreSQL.

Question 3

How do you handle schema changes in source databases?

Accepted Answer

We build resilient schemas using schema evolution features in modern connectors. We also implement alerts in dbt to notify engineering teams when source columns change unexpectedly.

Question 4

Can you handle real-time streaming data?

Accepted Answer

Yes, we implement streaming pipelines using Apache Kafka or AWS Kinesis to ingest and process high-frequency event streams for real-time analytics.

Question 5

How do you ensure data security during transfer?

Accepted Answer

We encrypt data in transit using TLS and at rest. We also implement secure credentials management systems like AWS Secrets Manager to protect database keys.

Question 6

How do you check for data quality issues?

Accepted Answer

We write automated validation rules within dbt to verify key constraints, check for null values, and flag abnormal schema patterns automatically during execution.

Question 7

Can we connect custom legacy systems?

Accepted Answer

Yes, when pre-built connectors do not exist, we write custom Python-based ingestion scripts to pull data from legacy databases and files securely.

Question 8

How do you prevent duplicate data?

Accepted Answer

We implement upsert patterns and unique constraints within the staging tables, ensuring that data is deduplicated properly during pipeline transformation steps.

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