What are the different data sources you have used?

I've worked with a diverse range of data sources, broadly categorized into databases, APIs, files, streaming platforms, SaaS applications, and modern data lake/lakehouse formats, covering both batch and real-time ingestion.

Databases

I frequently extract data from OLTP databases like PostgreSQL, MySQL, and Oracle, and sometimes OLAP systems like Snowflake or BigQuery. Ingestion patterns include Change Data Capture (CDC) using tools like Debezium for near real-time updates, and bulk extracts via SQL queries or database-specific utilities (COPY in Postgres) for batch processing. Challenges include schema evolution and ensuring transactional consistency.

-- Example: Bulk extract for incremental loading
SELECT id, name, created_at, updated_at
FROM public.users
WHERE updated_at >= '{{ last_successful_run_timestamp }}';

APIs

RESTful and GraphQL APIs are common for integrating with external services. Key considerations include managing rate limiting, pagination (e.g., cursor, offset), authentication (OAuth, API keys), and ensuring idempotency for retries.

Files

Files, including CSV, JSON, and especially columnar formats like Parquet and ORC, are stored in cloud object storage (S3, GCS) or HDFS. Columnar formats are preferred for analytical workloads due to superior compression and predicate pushdown, enhancing query performance with engines like Spark.

Streaming Platforms

For real-time data, Apache Kafka and AWS Kinesis are critical for event-driven architectures, ingesting high-throughput streams from application logs or IoT devices. Key considerations involve managing offsets, consumer groups, and ensuring message ordering and delivery guarantees.

SaaS Applications

SaaS platforms like Salesforce or Stripe often require integration via native APIs, webhooks, or managed connectors (Fivetran, Airbyte) which handle schema discovery and incremental loading.

Data Lakes and Lakehouses

Modern data architectures leverage data lakes (e.g., S3 with Parquet) and lakehouse formats like Delta Lake or Apache Iceberg. These provide ACID transactions, schema evolution, and time travel over object storage, serving as robust sources for analytics.

Legacy Systems

Legacy sources like mainframe systems (DB2, flat files) or FTP servers present unique challenges in connectivity, data format parsing, and scheduling.

In the interview, also mention how you handle data quality, error handling, and monitoring for each source type.