Data Engineering
104 articles tagged with "Data Engineering"
Presto vs Trino Interview Questions
Explains the PrestoDB vs Trino split, rename, shared architecture, deployment differences, and interview-focused workload guidance.
30 Snowflake Interview Questions for Data Engineers
Core Snowflake interview topics: architecture, warehouses, recovery, loading, and security — emphasize trade-offs in cost, speed, and risk.
How to Use Context Mapping for Data Architecture
Map bounded contexts, classify relationships, and choose integration patterns to reduce rework, schema drift, and pipeline breakage.
Snowflake vs. Databricks: Monitoring Features Compared
SQL-first platforms favor low-touch monitoring and credit controls, while Spark-heavy stacks demand deeper job and streaming observability.
How CQRS Works with Event Sourcing
Commands change state, events record facts, and projections build read models—covers aggregates, snapshots, concurrency, and replay.
ETL vs ELT: Pros, Cons, and Use Cases
Quickly compare ETL and ELT: when to transform data, plus trade-offs in cost, security, scalability, and use cases.
AWS Data Engineer Practice Questions Explained
Matching AWS services to workload beats memorization—use access pattern, latency, and control to choose S3, Glue, Redshift, or Athena.
Ultimate Guide to Data Storytelling with Dashboards
Turn dashboards into decision tools: start with one business question, design for one audience, show the insight and next steps.
Scaling Self-Service Analytics with Databricks
Standardize Gold tables, Unity Catalog metric views, and SQL Warehouses to deliver governed, consistent self-service analytics and BI access.
Case Study: Caching with Databricks for Faster Analytics
Cut scans from 2.3TB to 8GB and reduce compute costs 73% using Disk Cache, Spark cache, SQL result cache and improved file layout.
Git Workflows for Data Teams
Use one Git branch model, short-lived branches with reviews and CI, map Dev/Stage/Prod, and keep notebooks and large files out of Git.
Top Tools for Data Lakehouse and Data Warehouse
Choose a lakehouse for unified SQL, ML, and streaming - use open formats and governance to avoid lock-in and control costs.