data
Practical resources about data for software engineers.
87 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.
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,...
Caching Strategies: From Browser to Database, a Complete
A practical guide to caching strategies: browser caching, CDN edge caching, application caching...
Apache Airflow: DAGs, Operators, Scheduling
Master Apache Airflow: DAGs, operators, sensors, XCom, scheduling, backfilling, connections,...
Data Pipeline Architecture: Batch, Streaming, Lambda, Kappa
Master data pipeline architecture: batch processing, streaming, lambda and kappa patterns, ETL vs...
Data Quality Guide: Validation, Profiling, Great
Master data quality: validation frameworks, profiling, schema enforcement, anomaly detection, and...
dbt: Models, Tests, Macros, Materializations
Master dbt for data transformations: models, tests, macros, materializations, seeds, snapshots,...
Data Migration: Zero-Downtime Strategies That Work
A practical guide to data migration: planning, dual-write patterns, backfill strategies, schema...
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 Elasticsearch Cluster Setup
Deploy and scale Elasticsearch clusters. Covers node roles, sharding, replicas, index templates,...
Complete Guide to SQL Query Optimization
Optimize SQL queries. Covers EXPLAIN plan analysis, index strategies, join optimization, N+1 query...
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 Common Table Expressions (CTEs): non-recursive and recursive CTEs,...
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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