data
Practical resources about data for software engineers.
86 results
Understanding Data Processing in Software Engineering
Every application eventually deals with data — parsing user input, serializing objects for APIs, validating form submissions, or transforming records between formats. Getting data handling right is what separates fragile scripts from production-grade systems.
This collection covers practical recipes and patterns for working with JSON, CSV, XML, YAML, and custom formats. You will find copy-paste solutions for common tasks like flattening nested objects, sanitizing user input, generating slugs, and formatting dates across Python, Java, and JavaScript.
Complete Guide to Kafka Stream Processing
Build real-time event pipelines with Kafka: producers, consumers, Kafka Streams, Connect, schema...
Data Lake vs Data Warehouse — Architecture Guide
How data lakes store raw data at scale: medallion zones, lakehouse tables, ETL vs ELT,...
Data Mesh Architecture — Decentralized Data Ownership
A practical guide to Data Mesh: decentralizing data ownership to domain teams, treating data as a...
Lakehouse Architecture — The Best of Both Worlds
A practical guide to Lakehouse architecture: combining data lake storage flexibility with data...
Blob Storage: S3, GCS, and Azure Blob Patterns for Engineers
A practical guide to cloud blob storage: bucket design, access control, lifecycle policies,...
Apache Airflow: DAGs, Operators, Scheduling
Master Apache Airflow: DAGs, operators, sensors, XCom, scheduling, backfilling, connections,...
Data Pipeline Architecture: Batch, Streaming, Lambda, Kappa
Design batch and streaming data pipelines — working Airflow, Flink and dbt code, Lambda vs Kappa...
Data Quality Guide: Validation, Profiling, Monitoring
Master data quality: validation frameworks, profiling, schema enforcement, anomaly detection, and...
dbt: Models, Tests, Macros, Materializations
Learn dbt for data transformations: models, tests, macros, materializations, seeds, snapshots, and...
Data Migration: Zero-Downtime Strategies That Work
Plan, dual-write, backfill, and validate a zero-downtime data migration — with schema evolution...
ETL Pipelines: Extract, Transform, Load for Data Engineers
A practical guide to ETL pipelines: extracting data from multiple sources, transforming with...
Full-Text Search — Implement Search That Actually Works
A practical guide to full-text search: PostgreSQL tsvector, Elasticsearch indexing, query design,...
Real-Time Analytics: From Events to Dashboards in Seconds
A practical guide to real-time analytics: event collection, stream processing, data warehousing,...
Stream Processing: Event-Driven Data Pipelines with
A practical guide to stream processing: choosing between Kafka Streams, Flink, and Spark Streaming,...
Complete Guide to SQL Query Optimization
Find and fix slow SQL queries: read EXPLAIN plans, index the right way, kill N+1 queries, and apply...
Graph Databases — Neo4j and Property Graph Modeling
A practical guide to graph databases: property graph model, Cypher query language, modeling...
NoSQL Data Modeling Patterns
A practical guide to NoSQL data modeling: embedding vs referencing, access pattern-driven design,...
SQL CTEs: Common Table Expressions Explained
A practical guide to SQL CTEs: non-recursive and recursive expressions, readability, performance,...
SQL Joins — Visual Guide with Examples
A visual guide to SQL joins: INNER, LEFT, RIGHT, FULL OUTER, CROSS, and SELF joins with practical...
SQL Window Functions — Complete Guide
A practical guide to SQL window functions: ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG, SUM, AVG over...
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