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– 100+ Data Engineering Interview Experiences
You Should Know:
For 2+ Years Experience:
1. Spark Memory vs. Disk Handling
Cache DataFrame in memory df.cache() Persist to disk df.persist(StorageLevel.DISK_ONLY)
2. Repartition vs. Coalesce in PySpark
df.repartition(10) Full shuffle df.coalesce(5) Minimal shuffle
3. Daily Log Pipeline Design
Use Kafka for ingestion kafka-topics --create --topic logs --partitions 3 --replication-factor 2
4. Schema Evolution in Parquet
spark.read.option("mergeSchema", "true").parquet("path")
5. PySpark Joins
df1.join(df2, "key", "inner") Inner join df1.join(df2, "key", "left") Left join
6. Optimizing Slow Spark Jobs
Check executor memory spark-submit --executor-memory 8G --driver-memory 4G app.py
7. Narrow vs. Wide Transformations
Narrow: filter, map Wide: groupBy, join
8. Watermarking in Streaming
df.withWatermark("timestamp", "10 minutes")
9. Handling Late Data in Batch
-- Use window functions SELECT , LAG(value, 1) OVER (ORDER BY timestamp)
10. Cleaning Messy JSON
from pyspark.sql.functions import from_json
df.withColumn("parsed_json", from_json("raw_json", schema))
For 5+ Years Experience:
1. Real-Time Clickstream Architecture
Kafka + Spark Streaming + Delta Lake
spark.readStream.format("kafka").load()
2. Data Consistency in Distributed Systems
-- Use ACID transactions BEGIN TRANSACTION; UPDATE table SET value = new_value WHERE id = 1; COMMIT;
3. Fault-Tolerant Kafka Ingestion
Enable idempotent producer kafka-producer --enable-idempotence
4. Backfilling Historical Data
spark.read.format("delta").option("timeTravel", "timestamp").load()
5. End-to-End Data Platform
Terraform for IaC terraform apply -var="project_id=data-platform"
6. Data Governance & Lineage
Apache Atlas for metadata atlas-cli --entity-type=table --action=track
7. Cost Optimization in Pipelines
Use partitioning
df.write.partitionBy("date").parquet("output")
8. Data Lakehouse with Delta Lake
spark.sql("CREATE TABLE delta.<code>/path</code> USING DELTA")
9. Monitoring Data Quality
Great Expectations great_expectations checkpoint run my_checkpoint
10. Partitioning Best Practices
-- Partition by date and category CREATE TABLE logs PARTITIONED BY (date STRING, category STRING);
What Undercode Say:
Mastering these questions ensures readiness for top-tier Data Engineering roles. Key takeaways:
– Spark Optimization is critical (repartition, coalesce, persist).
– Streaming Architectures demand Kafka, watermarking, and Delta Lake.
– Data Governance tools like Apache Atlas ensure compliance.
– Cost Efficiency comes from partitioning and smart storage (Parquet/Delta).
Prediction:
Data Engineering interviews will increasingly focus on real-time processing, cost optimization, and lakehouse architectures as companies shift from batch to streaming.
Expected Output:
A structured, code-heavy guide for Data Engineering interviews, covering Spark, Kafka, and Delta Lake with actionable commands.
References:
Reported By: Shubhamwadekar I – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
