As a data engineer, it’s not enough to just build pipelines and manage big data—you also need the right words to explain your work clearly. Whether you’re in a client meeting, job interview, or daily stand-up, using the right English vocabulary helps you sound professional and confident. This Data Engineering Vocabulary Guide: Essential English Phrases for Data Engineers covers the most important technical terms, business phrases, and communication expressions you need to master. From ETL pipelines and schema evolution to data governance and compliance, this guide will help you speak the language of data engineering fluently.
Core Data Engineering Vocabulary
- ETL / ELT – Extract, Transform, Load (classic pipeline) / Extract, Load, Transform (modern pipelines).
- Ingestion – bringing raw data into your system (from APIs, DBs, files).
- Data Pipeline – an automated flow of data from source → transformation → destination.
- Batch Processing – processing large chunks of data at scheduled times.
- Streaming / Real-time Processing – processing data continuously as it arrives.
- Data Lake – a central storage for raw/semi-structured data (e.g., S3, ADLS, GCS).
- Data Warehouse (EDW) – structured, analytics-optimized storage (e.g., Redshift, Snowflake, BigQuery).
- Data Mart – subject-specific subset of a warehouse.
- Schema – structure of a table (columns, data types).
- Partitioning – splitting data for faster queries.
- Indexing – optimizing lookup performance.
- CDC (Change Data Capture) – tracking and applying data changes.
- Metadata – data about data (e.g., column definitions, lineage).
🔹 Cloud & Tools Vocabulary
- AWS Glue – serverless ETL service.
- Athena – serverless SQL query engine for S3.
- Redshift – Amazon’s data warehouse.
- Databricks – Spark-based data platform.
- Lake Formation – data lake governance.
- Airflow / Orchestration – workflow scheduling and monitoring.
- Terraform / IaC – Infrastructure as Code.
- Kafka / Kinesis – streaming platforms.
🔹 Performance & Optimization Vocabulary
- Scalability – ability to handle growth in data/users.
- Throughput – amount of data processed per second.
- Latency – delay in data processing.
- Bottleneck – the slowest part that limits performance.
- Parallelism – processing tasks simultaneously.
- Data Skew – uneven distribution of data across partitions.
- Repartitioning – redistributing data to balance workload.
- Predicate Pushdown – filtering data early at source for performance.
🔹 Governance & Security Vocabulary
- Data Lineage – tracking where data comes from and how it changes.
- Data Quality – accuracy, completeness, consistency of data.
- Data Masking / Anonymization – hiding sensitive info (PII).
- Encryption (at rest / in transit) – securing stored and transferred data.
- Access Control – managing user permissions.
- Compliance – meeting standards (GDPR, HIPAA, SOC2).
🔹 Communication Phrases for Data Engineers
- “This pipeline ensures scalable and fault-tolerant data processing.”
- “We applied partitioning and predicate pushdown to optimize query performance.”
- “The solution integrates Glue, S3, and Athena in a serverless architecture.”
- “We identified a data skew issue and resolved it using salting.”
- “The job failed due to a schema mismatch; we applied schema evolution handling.”
- “We need to implement CDC to capture incremental changes.”
- “The client requires GDPR compliance, so we designed a PII masking strategy.”
- “We use job bookmarking to ensure only new data is processed.”
AWS Glue Pipeline Challenges, Issues & Fixes
Q1. What are common challenges in building AWS Glue pipelines?
Answer:
- Schema Evolution Issues → Source systems may add/remove columns.
- Data Skew → Uneven partition distribution leading to performance bottlenecks.
- Small Files Problem → Many tiny files in S3 hurt performance.
- Job Failures → Out-of-memory errors, schema mismatches, permission issues.
- Debugging → Hard to debug since Glue is serverless and logs must be tracked in CloudWatch.
- Latency → Batch jobs not suitable for near real-time use cases.
- Cost → Over-provisioning DPUs increases costs unnecessarily.
Q2. How do you fix schema evolution errors in AWS Glue?
Answer:
- Use DynamicFrames instead of DataFrames for flexible schema handling.
- Enable schema evolution in the Glue Data Catalog.
- Apply
resolveChoicetransformation for ambiguous fields. - Validate schema in staging before pushing to production.
- Senior Tip: Keep “schema registry” in Glue Catalog and enforce contracts between source & downstream systems.
Q3. How do you troubleshoot missing rows when reading from a database with Glue?
Answer:
- Check partition column boundaries when using parallel JDBC reads.
- Validate fetch size (too high may cause drops).
- Monitor CloudWatch for silent JDBC exceptions.
- Use job bookmarking to ensure incremental loads don’t overwrite.
- Senior Tip: When consistent row loss occurs, reduce partitions or try hash-based partitioning.
Q4. How do you fix Glue job out-of-memory errors?
Answer:
- Increase DPU allocation or switch to G.2X workers.
- Repartition data to reduce skew.
- Use
pushdown_predicateto limit source reads. - Write intermediate results to S3 instead of holding everything in memory.
- Senior Tip: Profile data volume before load; don’t overestimate Spark memory.
Q5. How do you handle the small files problem in Glue pipelines?
Answer:
- Use coalesce() or repartition() in PySpark to reduce output files.
- Configure S3 DistCp / compaction job.
- Use Athena CTAS queries to merge small files.
- Senior Tip: Plan partitioning strategy upfront (avoid over-partitioning by timestamp → millions of folders).
Q6. What are best practices for monitoring and debugging Glue pipelines?
Answer:
- Use CloudWatch Logs for job execution details.
- Enable Glue Job Metrics (DPU hours, success/failure counts).
- Add custom error logging inside scripts (
try/exceptwith S3 log writes). - Integrate with CloudTrail for data access tracking.
- Senior Tip: Use AWS Glue Workflow with retries and failure triggers for resilience.
🔹 Kinesis / Kafka Approach in Data Pipelines
Q7. When would you use AWS Glue vs. Kinesis/Kafka?
Answer:
- Glue → Best for batch ETL (e.g., daily/hourly jobs).
- Kinesis/Kafka → Best for real-time streaming (low-latency ingestion).
- Typical pattern:
- Use Kinesis/Kafka to capture raw data in real time.
- Store into S3 (landing zone).
- Run Glue batch ETL jobs to clean, enrich, and store in curated zone.
Q8. What are challenges in Kinesis/Kafka pipelines?
Answer:
- Ordering → Guaranteeing correct event sequence.
- Exactly-Once Processing → Avoiding duplicates.
- Backpressure → Consumers not keeping up with producers.
- Schema Evolution → Changes in Avro/JSON/Protobuf payloads.
- Scalability → Handling spikes in data volume.
Q9. How do you troubleshoot lag in Kinesis/Kafka consumers?
Answer:
- Check consumer parallelism (increase shards in Kinesis / partitions in Kafka).
- Optimize batch size and poll intervals.
- Scale consumers horizontally.
- Senior Tip: Monitor CloudWatch (Kinesis) or Kafka Lag Exporter metrics to spot bottlenecks early.
Q10. How do you combine Glue with Kafka/Kinesis?
Answer:
- Use Glue Streaming ETL jobs (Spark Structured Streaming under the hood).
- Read directly from Kafka/Kinesis into Glue job.
- Apply transformations → write to S3 (Parquet) or Redshift.
- Senior Tip: For mission-critical pipelines, use checkpointing + exactly-once semantics in Glue to avoid reprocessing events.
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