The Data Engineering Blog
In-depth technical articles on Spark, Kafka, system design, production ML pipelines, and the tools that power modern data stacks.
How Spark's Catalyst Optimizer Rewrites Your Query Plan
A step-by-step breakdown of Catalyst's four phases — analysis, logical optimisation, physical planning, and code generation — with real query plan examples and what each Explain() output means for your jobs.
5 Production-Proven Ways to Fix Data Skew in Spark
Salting, broadcast joins, skew hints, AQE adaptive skew join, and repartitioning strategies. Includes benchmark comparisons on a 2TB dataset and when each technique makes sense.
Spark AQE: Adaptive Query Execution Explained with Diagrams
AQE rewrites your physical plan at runtime using actual statistics. This post explains coalescing partitions, converting sort-merge joins to broadcast, and skew join handling.
Spark Memory Management: Execution vs Storage Regions
The JVM heap is split into execution and storage memory. Learn how to tune spark.memory.fraction, storageFraction, and when to use off-heap to stop constant GC pauses in production.
Spark Structured Streaming vs Apache Flink: An Honest Comparison
Both can handle real-time pipelines, but they make very different trade-offs. Latency, exactly-once semantics, state backends, and operational complexity compared side by side.
Kafka Exactly-Once Semantics: What It Actually Means
Idempotent producers prevent duplicates within a session. Transactions extend this across partitions and topics. Here's where EOS actually breaks down and what you cannot protect against.
Diagnosing and Fixing Consumer Group Lag in Kafka
Lag is the difference between log-end-offset and committed offset. We cover monitoring with Burrow and Prometheus, setting meaningful alerts, and the common causes of runaway lag.
Kafka Log Compaction: How It Works Under the Hood
Compaction retains only the latest value per key. This post walks through the compaction cycle, tombstone records, min.cleanable.dirty.ratio, and CDC use cases.
Kafka Partitioning Strategy: Keys, Custom Partitioners, and Ordering
Default round-robin vs key-based partitioning, ordering guarantees within partitions, and writing a custom partitioner for geographic or tenant-based routing.
Kafka Connect in Production: Patterns and Pitfalls
Distributed mode, SMTs for transformations, handling schema evolution with Schema Registry, and the most common connector failures we see in production data pipelines.
Data Lakehouse Architecture: Why It's Replacing Warehouses
A lakehouse unifies the flexibility of a data lake with the reliability guarantees of a warehouse. We compare Delta Lake, Apache Iceberg, and Apache Hudi on ACID, time-travel, and schema evolution.
Lambda vs Kappa Architecture: Which Should You Choose in 2026?
Lambda separates batch and streaming into two codebases. Kappa bets everything on a single stream processor. With Flink maturing, Kappa is winning — here's when the trade-off makes sense.
Data Mesh: Principles, Organisational Patterns, and When It Fails
Data mesh is domain ownership + data as a product + self-serve infrastructure + federated governance. We cover real implementations, the organisational prerequisites, and the common failure modes.
CAP Theorem for Data Engineers: Real Systems, Real Choices
Cassandra chooses AP. HBase chooses CP. Kafka provides tunable consistency. This post maps real databases to their CAP trade-offs and explains what that means when a partition happens.
Building Scalable Microservices Architecture
Best practices for designing and implementing microservices that scale with your business needs and team size. Service boundaries, inter-service communication, and deployment strategies.
Polars vs Pandas: A Data Engineer's Honest Benchmark
Polars uses lazy evaluation, Apache Arrow, and multi-threading by default. We ran 6 benchmarks on 10M+ row datasets. The results will change how you think about Pandas.
Building Production Data Pipelines with dlt (data load tool)
dlt automatically infers schema, handles nested JSON, manages incremental loading, and writes to 30+ destinations with one pip install. A full end-to-end example from REST API to BigQuery.
Great Expectations: Data Quality Validation That Actually Works
Setting up expectation suites, running checkpoints in Airflow, integrating with Slack for alerts, and the difference between warning vs failure modes in a real pipeline.
Type Hints in Data Engineering Code: Why and How
Type hints enable mypy static analysis, make IDE completion accurate, and serve as inline documentation. Patterns we use in Spark, Airflow DAGs, and FastAPI services.
SQL Window Functions: The Complete Guide for Data Engineers
RANK, DENSE_RANK, ROW_NUMBER, LAG, LEAD, NTILE, and frames. Each one explained with a real business use case, the SQL, and the query plan it generates in BigQuery and Postgres.
Slowly Changing Dimensions: SCD Types 1, 2, 3, and 6 in dbt
SCD Type 2 is the most common — a new row per change, with valid_from and valid_to timestamps. We implement all four types in dbt with incremental models and snapshot strategies.
Recursive CTEs: Hierarchies, Trees, and Graph Traversal in SQL
Recursive CTEs are the cleanest way to traverse org charts, bill-of-materials trees, and route graphs. Includes anchor member, recursive member, and termination patterns.
DuckDB: The In-Process Analytics Database That Changes Everything
DuckDB runs in-process, reads Parquet directly from S3, and is faster than Pandas on single-node analytics. A practical guide to replacing heavy Spark jobs for datasets under 50GB.
dbt Best Practices: Staging, Marts, and Naming That Actually Scales
src_ staging models as the single source of truth, fct_ and dim_ naming, documentation via YAML, dbt tests, and orchestrating dbt runs in Airflow with the correct dependency graph.
RAG Architecture: Building Reliable LLM Applications on Private Data
Retrieval Augmented Generation combines vector search with LLMs. We cover chunking strategies, embedding models, Pinecone vs pgvector, re-ranking, and evaluating RAG quality with RAGAS.
Fine-Tuning LLMs in 2026: LoRA, QLoRA, and When Not To
Full fine-tuning is rarely the right call. LoRA adds low-rank adapters to reduce trainable parameters by 10,000x. QLoRA adds 4-bit quantisation. When to use each, and what evaluation looks like.
LangChain for Data Engineers: Building AI-Powered ETL Pipelines
Using LLMs to parse unstructured documents, generate SQL from natural language, classify records, and extract structured data — with LangChain chains, agents, and output parsers.
Pinecone vs Weaviate vs pgvector: Vector DB Comparison for Engineers
Managed vs self-hosted, HNSW vs IVF index types, metadata filtering, hybrid search, and cost at different scales. A decision framework for embedding-based applications.
MLflow in Production: Experiment Tracking, Model Registry, and Deployment
Logging params, metrics, and artifacts. Promoting models through Staging → Production in the Model Registry. Serving with mlflow models serve and integration with SageMaker.
Getting Started with Generative AI in Production
Comprehensive guide to understanding and implementing generative AI models in real-world production applications, including prompt engineering, evaluation, and cost management.
The Modern AWS Data Stack in 2026: Services, Patterns, and Costs
S3 as the foundation, Glue for ETL, EMR for Spark, Redshift for warehousing, Kinesis for streaming, and Athena for ad-hoc queries. Real cost benchmarks for a 10TB/day ingestion pipeline.
Snowflake Cost Optimisation: How We Cut Our Bill by 60%
Auto-suspend, warehouse sizing, clustering keys, materialized views, query result caching, and the Snowflake cost attribution query every team should be running weekly.
BigQuery Best Practices: Partitioning, Clustering, and Slot Management
Partition pruning saves money. Clustering reduces bytes scanned. Slot reservations vs on-demand billing. We profile three real-world queries and show the cost difference.
Databricks vs Amazon EMR: A 2026 Comparison for Spark Teams
Databricks has Photon, Delta Lake, and Unity Catalog. EMR is cheaper and closer to open source. We compare cluster start time, cost per query, and operational overhead.
Kubernetes for Data Engineers: Stateful Apps and Data Pipelines
Deploying Spark on Kubernetes, managing stateful services, persistent volumes, and the tradeoffs between Helm, Kustomize, and GitOps with ArgoCD for production pipelines.
Docker Best Practices for Data Engineers and ML Teams
Multi-stage builds, layer caching, security scanning with Trivy, pushing to registries, and orchestrating multiple containers with Docker Compose for local dev environments.