In my previous role at a retail analytics firm, we needed to store large batches of log files for batch processing and also serve low‑latency lookups for user profiles.

We evaluated storage options to decide between HDFS for batch analytics and Cassandra for fast key‑value access.

I outlined that HDFS is a distributed file system optimized for high‑throughput sequential reads/writes, provides write‑once-read‑many semantics, and uses a relaxed consistency model suitable for batch jobs. Cassandra is a distributed NoSQL database offering tunable consistency, row‑level reads/writes, and low‑latency random access, making it ideal for serving user profiles. I highlighted replication strategies, fault tolerance, and query patterns for each.

We adopted a hybrid architecture: raw logs were ingested into HDFS for Spark batch processing, while user profile data lived in Cassandra for real‑time personalization, improving query latency by 70% and reducing batch processing errors.

Our company needed to process millions of IoT sensor events per minute and store them for downstream analytics in AWS S3.

Create a reliable, low‑latency pipeline that reads from Kafka, transforms data, and lands it in the data lake while handling spikes and failures.

I proposed using Kafka Connect with a Flink streaming job. Kafka Connect captures raw events, Flink performs real‑time enrichment and schema validation, and writes Parquet files to S3 using a rolling window (e.g., 5‑minute intervals). I added checkpointing in Flink and enabled exactly‑once semantics. For fault tolerance, I configured Kafka replication factor of 3, used S3 versioning, and set up CloudWatch alerts for lag. To scale, the Flink job runs on a Kubernetes cluster with autoscaling based on CPU and lag metrics.

The pipeline achieved sub‑second end‑to‑end latency, handled a 3× traffic surge without data loss, and reduced downstream query costs by 25% due to columnar Parquet storage.

While working on a nightly aggregation pipeline for marketing metrics, a Spark job that processed 500 GB of data started taking over 4 hours, missing the SLA.

Reduce the job runtime to under 90 minutes without compromising data accuracy.

I profiled the job using Spark UI, identified a skewed join on a high‑cardinality key, and introduced a salting technique to distribute the data. I also persisted intermediate DataFrames, tuned the shuffle partitions to match the executor cores, and switched from the default CSV parser to the faster Spark‑SQL built‑in parser. Finally, I enabled Kryo serialization and increased executor memory.

Runtime dropped to 78 minutes, a 65 % improvement, and the job consistently met the SLA for the next three months. The changes also reduced cluster CPU usage by 30 %.

Our analytics team received daily feeds from three vendors: a relational DB, a NoSQL store, and flat CSV files, each with differing schemas and quality standards.

Implement a unified data‑quality framework to detect anomalies, enforce schema conformity, and report issues to stakeholders.

I built a metadata catalog in Apache Atlas, defined schema contracts for each source, and created validation scripts using Great Expectations. The scripts ran as part of the Airflow DAGs, flagging missing fields, out‑of‑range values, and duplicate records. I set up Slack alerts and a weekly dashboard for data‑quality metrics, and conducted cross‑team workshops to align on data‑quality definitions.

Data‑quality issue detection improved from ad‑hoc manual checks to automated alerts, reducing critical data errors by 80 % and increasing stakeholder trust in the data pipeline.