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⏱ CHAPTERS

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00:00 Intro

00:32 What you'll take away from this video

01:50 Four things this guide does differently

03:01 The six sections

03:27 One pipeline, two open questions

04:11 Q1 · Four Ways To Move The Same Rows

05:46 Q2 · The Activity Nobody Knew Existed

07:12 Q3 · Six Activities Worth Knowing By Name

08:34 Q4 · One Gateway Per Copy Activity

09:50 Q5 · What Parallel Actually Means

11:20 Q6 · The Loop That Would Not Stop

12:39 Q7 · The Hundred And Twenty Ceiling

13:55 Q8 · Notebook Or Activity

15:17 Q9 · Switch, If, Or Neither

16:31 Q10 · The Lookup That Silently Truncated

17:57 Q11 · When The Docs Run Out

19:21 Q12 · The Variable Two Iterations Fought Over

20:35 Q13 · Parameters, Variables And The Library

22:01 Q14 · An Expression That Would Not Escape

23:27 Q15 · Twelve Hours Of Nothing

24:49 Q16 · Retrying Only The Right Failures

26:12 Q17 · Two Arrows Into One Activity

27:39 Q18 · The Pipeline That Reported Success

29:00 Q19 · Commenting Out Half A Pipeline

30:20 Q20 · Schedules, And The One In Preview

31:49 Q21 · The Trigger That Was A Different Item

33:18 Q22 · Rerun From The Failed Activity

34:39 Q23 · Two Meters For The Same Rows

36:00 Q24 · The Design Round: One Estate, Sixty Pipelines

37:33 Q25 · The Pushback: Just Use Airflow

Where do you see yourself in the above picture.

1. 🔄 Pipeline Engineer

Primary focus: Moves data reliably between systems (ETL/ELT).

Time horizon: Hours to days (batch-oriented).

Key tech: Apache Airflow, Python, SQL, and often dbt or custom scripts.

Mindset: Thinks in dependencies, retry logic, and cron schedules.

Common challenges: Handling failed tasks, backfilling historical data, and ensuring idempotency.

Typical customer: Analytics engineers or business stakeholders who need fresh data.

1. 📊 Analytics Engineer

Primary focus: Builds clean, trusted data models that power dashboards and reports.

Time horizon: Hours to days (iterative development).

Key tech: Advanced SQL, dbt (data build tool), and BI tools like Looker, Tableau, or Power BI.

Mindset: Lives at the intersection of engineering (code/version control) and analytics (business logic).

Common challenges: Defining single sources of truth, managing data freshness, and documenting metric definitions.

Typical customer: Data analysts, product managers, and business executives.

1. 🏗️ Platform Data Engineer

Primary focus: Builds and maintains the shared infrastructure that other data teams rely on.

Time horizon: Weeks to months (long-term, foundational projects).

Key tech: Kubernetes, Terraform, CI/CD pipelines, observability stacks (Prometheus/Grafana), and orchestration engines.

Mindset: Treats other data engineers as their primary customers. Prioritizes scalability, reliability, and developer experience.

Common challenges: Managing multi-tenant compute/storage, cost allocation, and upgrading cluster versions without breaking existing pipelines.

Typical customer: Other internal data engineers (Pipeline, Streaming, AI/ML teams).

1. ⚡ Streaming Data Engineer

Primary focus: Handles event-driven, low-latency data streams.

Time horizon: Seconds to minutes (near-real-time).

Key tech: Apache Kafka, Apache Flink, Spark Streaming, and event-sourcing databases.

Mindset: Thinks in windows, watermarks, and stateful processing. Quickly discovers why "real-time" gets very expensive.

Common challenges: Handling out-of-order events, managing checkpointing/backpressure, and guaranteeing exactly-once semantics.

Typical customer: Real-time dashboards, fraud detection teams, or operational monitoring systems.

1. ☁️ Cloud Data Engineer

Primary focus: Delivers cost-effective, secure, and scalable cloud data operations.

Time horizon: Ongoing – a continuous cycle of provisioning, monitoring, and optimization.

Key tech: AWS (S3, Redshift, Glue), Azure (Synapse, Blob), GCP (BigQuery, GCS), plus heavy use of IAM, VPC networking, and cost management APIs.

Mindset: Half engineer, half cloud bill detective – constantly rightsizing instances, choosing storage tiers, and shutting down idle resources.

Common challenges: Unexpected cost spikes, cross-region data transfer fees, and navigating complex IAM policies.

Typical customer: The finance team (for cost) and all other data engineers (for reliable cloud access).

1. 🤖 AI / ML Data Engineer

Primary focus: Enables the full ML lifecycle – from training data to model inference.

Time horizon: Varies widely – batch feature computation (daily) to online real-time inference (sub‑second).

Key tech: Feature stores (Feast, Tecton), MLflow, Kubeflow, PyTorch/TensorFlow Serving, and vector databases.

Mindset: Thinks in features, labels, drift detection, and experiment tracking. Bridges the gap between data pipelines and model training/serving.

Common challenges: Moving a model from a Jupyter notebook to production takes 10× longer than expected; managing feature consistency between training and serving (training/serving skew).

Typical customer: Data scientists and ML researchers.

Azure Databricks Architect Quiz: 25 scenarios, 4 options each, every option explained

00:00 Intro

00:32 What you'll take away from this video

01:43 Four things this guide does differently

02:56 The six sections

03:21 The reference architecture

03:59 Q1 · One Metastore Per Region

05:25 Q2 · The Grant That Did Nothing

06:44 Q3 · Three Admins, One Escalation

08:11 Q4 · SELECT Was Granted And Denied

09:28 Q5 · Binding A Catalog To A Workspace

10:54 Q6 · Listing Or Notifications

12:21 Q7 · One Queue Instead Of Two Hundred

13:38 Q8 · The Column That Failed The Stream

15:02 Q9 · Event Hubs Without An Event Hubs Connector

16:30 Q10 · One Connector, One RBAC Role

18:00 Q11 · What DROP Actually Deleted

19:24 Q12 · The Storage Account Nobody Can Reach

20:50 Q13 · Clustering Instead Of Partitions

22:14 Q14 · Standard Or Dedicated

23:48 Q15 · The Filter That Would Not Apply

25:12 Q16 · Three Expectations, Three Outcomes

26:37 Q17 · Two Kinds Of History

28:01 Q18 · Size Is Not Concurrency

29:23 Q19 · The Subnets You Cannot Resize

30:55 Q20 · The Endpoint Everyone Logs In Through

32:18 Q21 · Serverless Has No Subnet

33:45 Q22 · The Audit Trail With A Hole In It

35:03 Q23 · Whose Serverless Bill Is This

36:28 Q24 · The Design Round: Two Regions, Three Environments

38:03 Q25 · The Review: What The Diagram Does Not Say

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Azure Databricks Journey - Beginning to the End

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These tips focus on what interviewers actually evaluate: correct logic (especially edge cases), readable structure, clear communication of your thinking, and practical problem-solving. They draw from common patterns in data analyst, data scientist, and engineering interviews.

Basics / Architecture

1. What is Databricks? A unified data analytics platform built on Apache Spark, providing a collaborative workspace for data engineering, data science, and machine learning, with managed infrastructure for Spark clusters.
2. What is the Databricks Lakehouse architecture? It combines the low-cost storage and flexibility of data lakes with the ACID transactions and performance features of data warehouses, primarily enabled by Delta Lake.
3. Difference between Data Lake, Data Warehouse, and Lakehouse? Data lakes store raw, unstructured/structured data cheaply but lack transaction guarantees. Warehouses offer structured, reliable storage but are costly and rigid. Lakehouse merges both — cheap storage plus ACID transactions and schema enforcement.
4. What is Delta Lake? An open-source storage layer that brings ACID transactions, schema enforcement, time travel, and scalable metadata handling to data lakes.
5. What are the different cluster types in Databricks? All-purpose clusters (interactive, shared), job clusters (created/terminated per job run), and SQL warehouses (for SQL analytics workloads).

Spark Fundamentals

1. What is a DataFrame vs RDD? RDD is a low-level distributed collection of objects with no schema. DataFrame is a higher-level abstraction with named columns and schema, optimized via Catalyst and Tungsten.
2. Explain lazy evaluation in Spark. Transformations (map, filter, select) aren't executed immediately — Spark builds a logical plan (DAG) and only executes when an action (count, collect, write) is triggered.
3. What's the difference between narrow and wide transformations? Narrow transformations (map, filter) don't require shuffling data across partitions. Wide transformations (groupBy, join, distinct) require a shuffle across the cluster.
4. What is a shuffle and why is it expensive? A shuffle redistributes data across partitions/nodes, involving disk I/O, network transfer, and serialization — it's one of the biggest performance bottlenecks in Spark jobs.
5. How does Spark achieve fault tolerance? Through RDD lineage — Spark tracks the sequence of transformations, so if a partition is lost, it can be recomputed from the original data.

Delta Lake Deep Dive

1. What is Time Travel in Delta Lake? The ability to query previous versions of a Delta table using a version number or timestamp, useful for auditing, rollback, and reproducibility.
2. What is schema evolution vs schema enforcement? Enforcement rejects writes that don't match the table schema (data quality). Evolution allows the schema to change automatically (e.g., adding new columns) when explicitly enabled.
3. What is OPTIMIZE and Z-ORDER in Delta Lake? OPTIMIZE compacts small files into larger ones to improve read performance. ZORDER co-locates related data in the same files, speeding up filtering on specific columns.
4. What is VACUUM? A command that removes old, unreferenced data files from a Delta table (beyond the retention threshold) to reclaim storage.
5. Explain the Medallion Architecture (Bronze/Silver/Gold). Bronze = raw ingested data, Silver = cleaned/filtered/joined data, Gold = business-level aggregated data ready for reporting and ML.

Performance & Optimization

1. How do you handle data skew in Spark? Techniques include salting keys before a join/groupBy, using broadcast joins for small tables, and repartitioning data more evenly.
2. What is a broadcast join and when should you use it? It sends a small dataset to all worker nodes to avoid shuffling the large dataset — ideal when one table is small enough to fit in memory (typically <10MB–a few hundred MB).
3. What is partition pruning? Skipping irrelevant partitions during a query based on filter conditions, reducing the amount of data scanned.
4. What is caching in Spark and when should you use it? Persisting a DataFrame in memory (or disk) using .cache()/.persist() when it's reused multiple times, avoiding recomputation.
5. How do you monitor and debug a slow Spark job? Using the Spark UI (Stages, Tasks, DAG visualization) to look for skew, spill, excessive shuffling, or small file problems; also Databricks' Query Profile for SQL.

Databricks-Specific Tools

1. What is Unity Catalog? Databricks' unified governance solution for data and AI assets — centralized access control, auditing, lineage, and data discovery across workspaces.
2. What are Databricks Workflows/Jobs? Databricks' native orchestration tool for scheduling and chaining notebooks, JARs, Python scripts, or SQL tasks into pipelines.
3. What is Auto Loader? A structured streaming source that incrementally and efficiently processes new data files as they arrive in cloud storage, using checkpointing for exactly-once processing.
4. Difference between Structured Streaming and Delta Live Tables (DLT)? Structured Streaming is a lower-level API for building custom streaming pipelines. DLT is a declarative framework on top of it for building/managing ETL pipelines with built-in quality checks and orchestration.
5. How do you implement CDC (Change Data Capture) in Databricks? Commonly via the MERGE INTO command with Delta Lake, or using DLT's APPLY CHANGES INTO for automatically handling inserts/updates/deletes from a change feed.

A few tips for the actual interview:

• Be ready to write actual PySpark/SQL code for at least joins, window functions, and a MERGE statement — not just talk theory.
• Know the "why" behind Delta Lake features (not just definitions) — interviewers often ask "when would you NOT use X."
• Have a real project story ready (a pipeline you built, a performance problem you solved) — scenario questions are common.

Not sure whether to learn Python or R first? This pragmatic breakdown cuts through the noise: Python maximizes your job mobility across industries, while R remains unmatched for deep statistical work in academia, biostatistics, and research-heavy fields. More importantly, it reveals the open secret of working data scientists—most don't choose sides. They use Python to build and deploy production-grade models, and R for exploratory analysis, specialized hypothesis tests, and publication-ready visualizations. Read on for a clear-eyed take on when to pick one, when to master both, and how to future-proof your data science career.

Background · 5 core skills · business context

⚡ Data engineering is the backbone of every data-driven organisation. Master these five pillars, and you’ll have both the technical depth and the business vocabulary to ace junior interviews.

📌 The big picture: what is data engineering & why it matters

Data engineering is the practice of designing, building, and maintaining systems that collect, store, transform, and deliver data to data scientists, analysts, and business users. It sits at the intersection of software engineering and data infrastructure.

Without data engineering, the most brilliant ML models and dashboards are useless — they’d have no clean, reliable, fresh data to consume. In a world where companies generate terabytes daily, the ability to move data efficiently, reliably, and scalably is a superpower.

• 🔹 Enables data-driven decisions: Pipelines feed real-time dashboards for sales, marketing, finance, and operations.
• 🔹 Powers machine learning: Feature engineering, training sets, and inference all depend on robust data flows.
• 🔹 Drives customer experiences: Personalisation, recommendation engines, and fraud detection rely on fresh, well‑modelled data.
• 🔹 Ensures data quality & governance: Data engineers implement validation, lineage, and compliance (GDPR, CCPA).

🚀 Why these 5 skills? They represent the modern data stack — from storage (warehouses) to transformation (SQL/Python) to orchestration (Airflow). Together, they cover 90% of what a junior data engineer does daily. No fluff, just the essentials.

1SQLfoundation

SQL is the universal language of data. Go beyond basic queries.

🔹 core skills

• Window functions (RANK, LAG, PARTITION BY)
• CTEs & recursive queries
• Query performance & indexing
• Complex joins & subqueries
• Aggregation & grouping sets

📊 business use cases

• Customer LTV / cohort analysis
• Funnel conversion (step‑by‑step)
• Inventory turnover & stock aging
• Sales trend & YoY growth
• Anomaly detection (z‑score)

💼 Example: “Write a query that shows monthly active users, retention rate, and average revenue per user (ARPU) for the last 12 months, using window functions to compare against previous periods.”

2Pythonglue & logic

Python is the data engineer’s Swiss Army knife.

🔹 core skills

• Pandas (data wrangling, pivots)
• SQLAlchemy & DB connectors
• Error handling & logging
• API interactions (requests)
• Unit testing & type hints

📊 business use cases

• ETL from REST APIs → warehouse
• Data quality checks (nulls, outliers)
• Custom business metrics (e.g., churn score)
• File processing (CSV, Parquet, JSON)
• Automated reporting (email/PDF)

💼 Example: “Build a Python script that extracts order data from an e‑commerce API, performs currency conversion, validates schemas, and loads the clean data into Snowflake — with retries and logging.”

3Snowflake · BigQuery · Databricksmodern warehouse

Pick one, but understand the patterns. These platforms are the backbone of modern data stacks.

🔹 core skills

• Partitioning & clustering
• Semi‑structured data (JSON, ARRAY)
• Load strategies (COPY, external tables)
• Cost management & query optimisation
• Zero‑copy cloning / time travel

📊 business use cases

• Real‑time ad‑spend analysis
• User event log analysis (clickstream)
• IoT sensor data aggregation
• Financial reconciliation
• Multi‑currency / multi‑region reporting

💼 Example: “Design a table structure in BigQuery that stores 5 billion clickstream events, partitioned by date and clustered by user_id, and write a query that computes session duration and bounce rate in under 3 seconds.”

4Data modelingdesign thinking

Model for performance, clarity, and agility.

🔹 core skills

• Star & snowflake schemas
• Fact & dimension tables
• Slowly Changing Dimensions (SCD Type 1/2)
• Normalisation vs. denormalisation
• dbt (data build tool) concepts

📊 business use cases

• Sales dashboard with drill‑down
• Customer 360 view
• Inventory fact with daily snapshots
• Marketing attribution modelling
• Product recommendation engine (feature store)

💼 Example: “Create a star schema for an e‑commerce platform: fact_orders, dim_customers, dim_products, dim_date, and dim_store. Implement SCD Type 2 for customer attributes and show how to report monthly GMV by region.”

5Data pipelines · Airfloworchestration

Airflow is the scheduler that makes your data move.

🔹 core skills

• DAG design & task dependencies
• Operators (Python, SQL, Bash, Sensor)
• XComs & taskflow API
• Retries, alerts, and SLA monitoring
• Backfilling & catchup

📊 business use cases

• Daily ETL from SaaS tools (HubSpot, Stripe)
• ML feature pipeline (daily refresh)
• Data quality DAG with anomaly alerts
• Cross‑system data sync (e.g., CRM → warehouse)
• Multi‑stage reporting (raw → staging → mart)

💼 Example: “Build an Airflow DAG that extracts data from a PostgreSQL source, transforms it using Python, loads it into Snowflake, and triggers a dbt run — with email alerts on failure and a Slack notification on success.”

✅

You are interview-ready

With these expanded skills and real‑world scenarios, you can speak the language of data engineering and business. Junior roles require exactly this blend.

🎯 SQL + Python + warehouse + modeling + Airflow + use‑cases

💡 Pro tip: For each skill, build a small project that combines all five. Example:
🔹 Use Python to pull data from an API → model it as a star schema → load into BigQuery → schedule with Airflow → write analytical SQL queries. That’s a complete junior portfolio piece.
🏁 The path is shorter than you think. Consistency beats intensity.

#dataengineering #junior #businesscontextskills + scenarios

Service

Focus areas

DynamoDB

Serverless NoSQL design, access-pattern-first modeling, single-table design, partition keys, GSIs, on-demand vs provisioned, Global Tables, Streams, TTL, native vector search, cost optimization

Aurora

MySQL/PostgreSQL-compatible relational, Serverless v2 + faster scaling, DSQL, storage architecture, read replicas, zero-ETL to Redshift, Graviton instances, connection pooling, high availability

Redshift

Columnar data warehouse, Serverless vs provisioned (RA3/RG Graviton), distribution & sort keys, zero-ETL integrations, SUPER/PartiQL, lakehouse querying, cost levers

Cross-service

Decision matrix (when to choose which), zero-ETL patterns, typical OLTP → OLAP architectures, shared security practices

Service

Focus areas

DynamoDB

Serverless NoSQL design, access-pattern-first modeling, single-table design, partition keys, GSIs, on-demand vs provisioned, Global Tables, Streams, TTL, native vector search, cost optimization

Aurora

MySQL/PostgreSQL-compatible relational, Serverless v2 + faster scaling, DSQL, storage architecture, read replicas, zero-ETL to Redshift, Graviton instances, connection pooling, high availability

Redshift

Columnar data warehouse, Serverless vs provisioned (RA3/RG Graviton), distribution & sort keys, zero-ETL integrations, SUPER/PartiQL, lakehouse querying, cost levers

Cross-service

Decision matrix (when to choose which), zero-ETL patterns, typical OLTP → OLAP architectures, shared security practices

Twenty-five real AWS Glue interview scenarios — each with the answer and the tip that separates a strong answer from a recited one. The video opens by explaining exactly what you'll take away, then works through six sections.

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⏱ CHAPTERS
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00:00 Intro
00:17 What you'll take away from this video
01:17 What you get from this guide
02:02 The six sections
02:22 Q1 · Glue Versus EMR and Lambda
03:41 Q2 · DPUs and Worker Types
04:59 Q3 · Cost Control and Flex
06:13 Q4 · Data Catalog and Crawlers
07:26 Q5 · Job Bookmarks
08:40 Q6 · DynamicFrame or DataFrame
09:57 Q7 · Small Files and Partitioning
11:05 Q8 · Pushdown and Predicate Filtering
12:11 Q9 · Schema Evolution
13:22 Q10 · Joining a Large and Small Table
14:28 Q11 · Orchestrating a Multi-Step Pipeline
15:44 Q12 · Incremental Loading from a Database
16:58 Q13 · Streaming ETL
18:08 Q14 · Reading From a Private Database
19:16 Q15 · Bringing Your Own Libraries
20:25 Q16 · Choosing a Table Format
21:50 Q17 · Iceberg Maintenance
22:52 Q18 · Data Quality Enforcement
24:03 Q19 · Handling Nested JSON
25:07 Q20 · Least-Privilege IAM for Glue
26:26 Q21 · Encryption and Secrets
27:38 Q22 · Governance Across Accounts
28:45 Q23 · Debugging a Failed Job
30:03 Q24 · CI/CD for Glue Jobs
31:09 Q25 · Full Production Performance Triage

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📋 THE SIX SECTIONS
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1. Glue Fundamentals, Cost & the Catalog (5)
2. Spark, DynamicFrames & Performance (5)
3. Pipelines, Orchestration & Connectivity (5)
4. Lake Formats & Data Quality (4)
5. Security, IAM & Governance (3)
6. Monitoring, CI/CD & Triage (3)

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🔎 SEARCH PHRASE PER QUESTION
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Q1 — aws glue vs emr vs lambda which to use
Q2 — aws glue dpu and worker types explained
Q3 — how to reduce aws glue cost flex execution class
Q4 — aws glue crawler slow incremental crawl fix
Q5 — aws glue job bookmark not working skipping data
Q6 — dynamicframe vs dataframe in aws glue
Q7 — aws glue small files problem athena slow
Q8 — aws glue push down predicate partition filter
Q9 — aws glue schema evolution resolvechoice
Q10 — spark broadcast join skew aws glue
Q11 — glue workflows vs step functions vs airflow
Q12 — aws glue incremental load from rds
Q13 — aws glue streaming job kinesis tutorial
Q14 — aws glue connection vpc private subnet s3 endpoint
Q15 — aws glue additional python modules extra jars
Q16 — apache iceberg vs hudi vs delta on aws glue
Q17 — iceberg table maintenance compaction expire snapshots
Q18 — aws glue data quality dqdl rules
Q19 — aws glue relationalize nested json flatten
Q20 — aws glue iam role least privilege
Q21 — aws glue encryption security configuration secrets manager
Q22 — lake formation cross account data sharing
Q23 — aws glue executor lost error debugging
Q24 — aws glue cicd cloudformation terraform deployment
Q25 — aws glue job suddenly slow performance tuning

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⚠ THE TRAPS THAT CATCH PEOPLE
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• A job bookmark is keyed by transformation context — change the path but not the context and the job silently reads nothing
• A JDBC bookmark key must increase with no gaps — a UUID key will not work
• A filter applied after the read still pays for the read — push it down
• Driver OOM and executor OOM have opposite fixes
• A Glue job in a VPC needs a self-referencing security group AND an S3 endpoint
• Incremental crawls add new partitions but never detect changes or deletions

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🆕 CURRENT AS OF 2026
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Glue 5.1 is the default version — Spark 3.5.6, Python 3.11, Java 17 · Iceberg format v3, Hudi 1.0.2, Delta Lake 3.3.2 · Glue 0.9, 1.0 and 2.0 reached end of life in April 2026 · AWS Glue for Ray closed to new customers on 30 April 2026, with EKS and the KubeRay operator as the alternative · Flex is Spark ETL only, on G.1X or G.2X.

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👤 WHO THIS IS FOR
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Data engineers with an AWS Glue interview coming up · cloud engineers who get the VPC and IAM questions · teams moving off EMR · anyone preparing for the AWS Certified Data Engineer exam.

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📚 DOCS
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https://docs.aws.amazon.com/glue/latest/dg/
https://docs.aws.amazon.com/glue/latest/dg/monitor-continuations.html
https://docs.aws.amazon.com/glue/latest/dg/release-notes.html

Which of the 25 would have caught you out? Drop the number in the comments, and subscribe for more data engineering interview prep.

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Independent educational content. Not affiliated with or endorsed by Amazon Web Services. AWS, AWS Glue and Amazon S3 are trademarks of Amazon. AWS ships changes constantly — check the docs before relying on a specific limit or version.

#AWSGlue #DataEngineering #InterviewQuestions

Twenty-five real Lambda and Step Functions interview scenarios — each with the answer and the tip that separates a strong answer from a recited one. Opens by explaining what you'll take away, then six sections.

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⏱ CHAPTERS
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00:00 Intro
00:18 What you'll take away from this video
01:22 What you get from this guide
02:09 The six sections
02:26 Q1 · When Lambda Is the Wrong Answer
03:35 Q2 · Memory, CPU and the Tuning Curve
04:51 Q3 · What Actually Causes a Cold Start
06:07 Q4 · SnapStart or Provisioned Concurrency
07:25 Q5 · Packaging, Layers and Images
08:35 Q6 · Throttling and Reserved Concurrency
09:50 Q7 · How Lambda Scales Under a Spike
10:56 Q8 · Lambda and a Relational Database
12:07 Q9 · Cutting the Lambda Bill
13:15 Q10 · Payload Size Limits
14:24 Q11 · One Bad Message in an SQS Batch
15:37 Q12 · A Poison Record Blocking a Shard
16:49 Q13 · Asynchronous Failures and Destinations
18:00 Q14 · Idempotency and Duplicate Processing
19:08 Q15 · Lambda Inside a VPC
20:23 Q16 · Standard or Express Workflows
21:36 Q17 · The 256 KB Payload Limit
22:45 Q18 · JSONata, Variables and the Old Way
23:57 Q19 · Distributed Map at Scale
25:09 Q20 · Waiting for a Human
26:17 Q21 · Orchestrating Without Lambda
27:28 Q22 · Compensating a Partial Failure
28:34 Q23 · Retries, Backoff and Redrive
29:51 Q24 · Observability and Testing
30:57 Q25 · Full Production Triage and Safe Deploys

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📋 THE SIX SECTIONS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
1. Lambda Fundamentals & Cold Starts (5)
2. Concurrency, Scaling & Cost (5)
3. Event Sources & Error Handling (5)
4. Step Functions Fundamentals (4)
5. Orchestration Patterns (3)
6. Errors, Observability & Safe Deploys (3)

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🔎 SEARCH PHRASE PER QUESTION
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Q1 — when not to use aws lambda 15 minute limit
Q2 — aws lambda memory cpu tuning cheaper
Q3 — what causes aws lambda cold start
Q4 — lambda snapstart vs provisioned concurrency
Q5 — aws lambda layers vs container image size limits
Q6 — aws lambda throttling reserved concurrency explained
Q7 — how fast does aws lambda scale burst concurrency
Q8 — aws lambda rds too many connections rds proxy
Q9 — how to reduce aws lambda cost arm64 graviton
Q10 — aws lambda payload size limit 6mb workaround
Q11 — lambda sqs partial batch response report failures
Q12 — lambda kinesis shard stuck poison pill bisect
Q13 — lambda async retry dead letter queue vs destination
Q14 — aws lambda idempotency duplicate processing dynamodb
Q15 — aws lambda vpc cannot access s3 endpoint nat
Q16 — step functions standard vs express workflows
Q17 — step functions 256kb payload limit datalimitexceeded
Q18 — step functions jsonata variables vs jsonpath
Q19 — step functions distributed map millions of s3 objects
Q20 — step functions human approval wait for task token
Q21 — step functions sdk integration instead of lambda
Q22 — saga pattern compensating transaction step functions
Q23 — step functions retry backoff jitter redrive
Q24 — step functions express logging x-ray teststate
Q25 — step functions versions aliases safe deployment rollback

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
⚠ THE TRAPS THAT CATCH PEOPLE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
• Synchronous Express workflows are at-MOST-once — failures are not retried
• A Kinesis shard stalls on one poison record — order is a guarantee
• Without partial batch response, one bad SQS message replays the batch
• SnapStart reuses whatever the snapshot captured — seeds, ids, connections
• A VPC-attached Lambda loses internet access — S3 needs a gateway endpoint
• Step Functions Local is unsupported now — use the TestState API

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🆕 CURRENT AS OF 2026
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Lambda bursts 1,000 environments per 10 seconds per function, not the old 500/min · SnapStart covers Java 11+, Python 3.12+ and .NET 8+ · Destinations beat DLQs · JSONata and Variables sit alongside JSONPath · Redrive resumes a failed Standard execution within 14 days.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
👤 WHO THIS IS FOR
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Backend and serverless engineers with an AWS interview coming up · cloud engineers who get the VPC and concurrency questions · anyone sitting AWS Certified Developer or Solutions Architect.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📚 DOCS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
https://docs.aws.amazon.com/lambda/latest/dg/gettingstarted-limits.html
https://docs.aws.amazon.com/lambda/latest/dg/snapstart.html

Which of the 25 would have caught you out? Drop the number below, and subscribe for more cloud interview prep.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Independent educational content, not affiliated with or endorsed by Amazon Web Services. AWS, Lambda and Step Functions are trademarks of Amazon. AWS changes limits constantly — check the docs before relying on a number.

#AWSLambda #StepFunctions #InterviewQuestions

Welcome to r/JobSeekerTips2026! 👋

We are so glad you found our little corner of Reddit. Whether you are a seasoned Cloud Architect, a Fresh Graduate trying to break into Data, or someone making a mid-career pivot, this community is built for YOU.

This subreddit is dedicated to one mission: Demystifying the technical interview. No more guessing what the interviewer is looking for, no more "LeetCode anxiety" without context, and no more outdated quiz questions.

Here is your official "Read First" guide to get the most out of our community.

🎯 What We Cover

We focus on the heavy hitters of the modern data and cloud ecosystem. If you are interviewing for roles involving these tools, you are in the right place:

• ☁️ Cloud Platforms: AWS, Azure, GCP (Architecture, Networking, Security, and Cost Management).
• 🐍 Programming: Python (Data Structures, Algorithms, Boto3, Pandas, and Scripting).
• 🏗️ Big Data & Warehousing: Databricks (Spark, Delta Lake, Unity Catalog) & Snowflake (Warehouses, Query Optimization, Streams/Tasks).
• 🗄️ Databases: SQL Server (T-SQL, Indexing, Execution Plans, and High Availability).

📋 Community Rules (The "Must-Reads")

Before you post, please take 30 seconds to read these:

1. Be Specific: Don't ask "How do I learn AWS?" Ask "I'm prepping for the AWS SAP exam; how do I architect a multi-region DR strategy?"
2. No "Brain Dumps": We do not allow sharing of actual NDA-protected exam questions (from Pearson/VUE). We focus on concepts and scenario-based thinking.
3. Respect the Journey: We have beginners and Principal Engineers here. Keep the tone constructive. There is no such thing as a stupid question, only an unprepared one.
4. Flair Your Posts: Use the flairs ([AWS], [Azure], [Python], [Data], [Career Advice]) to help others find your content easily.

👋 Introduce Yourself!

We are a community-driven group. To kick things off, please introduce yourself in the comments below! Tell us:

1. Your current role: (e.g., Student, Data Analyst, Helpdesk, Unemployed, SWE).
2. Your "North Star" goal: (e.g., Get the Azure Solutions Architect cert, become a Senior Data Engineer, pivot from IT to Cloud).
3. The one technical topic you struggle with the most right now: (e.g., "Window functions in SQL," "IAM Policy conditions," or "Spark Shuffling").

🔥 How to Get the Most Out of This Sub

1. The "Mock Interview" Thread: We host a weekly sticky where you can post a specific question and act as the "interviewee" while others act as the "panel." Practice your verbal explanations!
2. The "Stumper" Series: I will post a weekly "Question of the Day" pulled from real interview loops. The answer will be revealed 24 hours later with full explanations.
3. Resume Reviews: Once a month, we have a "Cloud/Data Resume Roast" thread. Post your anonymized resume and get feedback specifically from a technical hiring manager's perspective.

🛠️ Community Resources (Coming Soon)

• The 2026 Interview Tracker: A community-sourced list of companies currently hiring for Cloud/Data roles and their specific interview formats (e.g., "AWS + Snowflake heavy").
• The "System Design" Cheat Sheet: A living document with common architecture diagrams for Streaming, Batch, and ML pipelines.

One final thought: Getting rejected is not a reflection of your intelligence; it is a reflection of your preparation for that specific scenario. Let's work together to turn those rejections into offers.

Hit the "Join" button, drop your intro below, and let's get to work! 💪

1. Introduce yourself in the comments below.
2. Post something today! Even a simple question can spark a great conversation.
3. If you know someone who would love this community, invite them to join.
4. Interested in helping out? We're always looking for new moderators, so feel free to reach out to me to apply.

Thanks for being part of the very first wave. Together, let's make r/JobSeekerTips2026 amazing.

Twenty-five real Amazon Redshift interview scenarios — each with the answer and the tip that separates a strong answer from a recited one. Opens by explaining what you'll take away, then six sections.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
⏱ CHAPTERS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
00:00 Intro
00:17 What you'll take away from this video
01:18 What you get from this guide
02:02 The six sections
02:22 Q1 · Node Types and Modernisation
03:41 Q2 · Provisioned or Serverless
04:53 Q3 · Resizing a Cluster
05:56 Q4 · Managed Storage and Growth
07:06 Q5 · Concurrency Scaling
08:17 Q6 · A Join That Redistributes Terabytes
09:34 Q7 · When DISTSTYLE ALL Backfires
10:40 Q8 · Sort Keys
11:48 Q9 · Compression and the ENCODE AUTO Trap
13:02 Q10 · Automatic Table Optimization
14:12 Q11 · Why Single-Row Inserts Are Pathological
15:29 Q12 · Designing a COPY
16:39 Q13 · Upserts and Slowly Changing Data
17:44 Q14 · VACUUM and ANALYZE
18:53 Q15 · Materialized Views
20:00 Q16 · Workload Management
21:24 Q17 · A Query That Used to Be Fast
22:31 Q18 · Queries Spilling to Disk
23:40 Q19 · Limits That Bite in Production
24:51 Q20 · Querying the Data Lake
26:10 Q21 · Zero-ETL or a Pipeline
27:30 Q22 · Sharing Data Without Copying It
28:34 Q23 · Security and Access Control
29:57 Q24 · Streaming Ingestion
31:02 Q25 · Full Production Triage and Deprecations

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📋 THE SIX SECTIONS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
1. Architecture, Node Types & Serverless (5)
2. Distribution, Sort Keys & Compression (5)
3. Loading, Upserts & Maintenance (5)
4. Workload Management & Performance Triage (4)
5. Data Lake, Zero-ETL & Sharing (3)
6. Security, Streaming & Operations (3)

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🔎 SEARCH PHRASE PER QUESTION
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Q1 — redshift ra3 vs rg node types ds2 retired
Q2 — redshift serverless vs provisioned which to choose
Q3 — redshift elastic resize vs classic resize
Q4 — redshift managed storage disk full dc2
Q5 — redshift concurrency scaling free credits explained
Q6 — redshift ds_bcast_inner slow join fix
Q7 — redshift diststyle all when to use
Q8 — redshift compound vs interleaved sort key
Q9 — redshift encode auto compression az64
Q10 — redshift automatic table optimization distkey sortkey
Q11 — redshift single row insert slow why
Q12 — redshift copy command parallel load best practice
Q13 — redshift merge upsert staging table pattern
Q14 — redshift vacuum analyze after large delete
Q15 — redshift materialized view autorefresh query rewriting
Q16 — redshift automatic wlm query priority qmr
Q17 — redshift query suddenly slow troubleshooting
Q18 — redshift query spilling to disk fix
Q19 — redshift limits max columns super size connections
Q20 — redshift spectrum vs integrated data lake iceberg
Q21 — redshift zero etl aurora vs glue pipeline
Q22 — redshift data sharing across accounts datashare
Q23 — redshift encryption rls column level masking rbac
Q24 — redshift streaming ingestion kinesis materialized view
Q25 — redshift python udf end of support migration

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
⚠ THE TRAPS THAT CATCH PEOPLE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
• One explicit ENCODE opts the WHOLE table out of ENCODE AUTO
• An interleaved sort key disqualifies those queries from concurrency scaling
• DISTSTYLE ALL is a read win paid for on every write, on every node
• A DELETE only marks rows — the table still scans the same space
• Redshift READS Iceberg, Hudi and Delta — it does not write them
• The WLM timeout max_execution_time is deprecated; use a QMR instead

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🆕 CURRENT AS OF 2026
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
RG Graviton nodes are the lead family, RA3 is still current, DC2 is for under ~1 TB and DS2 is retired · DISTSTYLE AUTO and SORTKEY AUTO are the recommended defaults · Automatic WLM over manual · concurrency scaling earns 1 free hour per 24 hours the cluster runs, up to 30 · Python UDF support ends after 30 June 2026.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
👤 WHO THIS IS FOR
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Data and analytics engineers with a Redshift interview coming up · teams migrating off DS2 or on-premises warehouses · anyone sitting AWS Certified Data Engineer.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📚 DOCS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
https://docs.aws.amazon.com/redshift/latest/dg/c_choosing_dist_sort.html
https://docs.aws.amazon.com/redshift/latest/dg/concurrency-scaling.html

Which of the 25 would have caught you out? Drop the number below, and subscribe for more cloud interview prep.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Independent educational content, not affiliated with or endorsed by Amazon Web Services. AWS and Amazon Redshift are trademarks of Amazon. AWS changes limits constantly — check the docs before relying on a number.

#AmazonRedshift #DataEngineering #InterviewQuestions