Writing from our team. The latest news, insights, and resources.
A strong answer in a design CDC pipeline interview starts with the core job: capture row-level changes from a source system, move them safely and in order, and land them in a warehouse or lake with low delay and high trust. CDC, or change data capture, means tracking inserts, updates, and deletes instead of reloading...
To debug a data pipeline in an interview, start with the symptom, not the fix. Clarify what broke, trace the pipeline from source to output, isolate the break with evidence, fix the root cause, then prevent a repeat. That’s the clearest way to answer a debug data pipeline interview question. Interviewers want structured thinking, not...
Cloud data governance is the mix of access rules, metadata, classification, lineage, and audit controls that keep cloud data safe and usable. Lake Formation, Unity Catalog, Microsoft Purview, and Snowflake Horizon all help with that job, but they solve it from different starting points. The best choice usually depends on where your data already lives...
Data engineers can control BigQuery spend by combining slot management, partition pruning, and query limits. That’s the core of bigquery cost optimization. The goal isn’t to block useful analysis, it’s to stop surprise bills, runaway joins, and scheduled jobs that read far more data than expected. Modern teams run ad hoc SQL, BI refreshes, dbt...
Microsoft Fabric is usually the better choice for new data engineering projects in 2026. Synapse still makes sense when you already have Azure SQL pools, pipelines, and permissions working in production. In the Microsoft Fabric vs Synapse decision, the real tradeoffs are speed, cost control, governance, and how much legacy work your team can safely...
Snowflake dynamic tables are a serverless way to keep transformed data fresh without wiring up a pile of scheduled jobs. You define the result you want, set a freshness target, and Snowflake manages refreshes for you. For data engineers, Snowflake dynamic tables mean simpler pipelines, less orchestration, and cleaner incremental updates. That matters when you’re...
AWS Glue is best for large batch ETL. Lambda is best for small event-driven transforms. Step Functions is best when your pipeline has many steps, retries, or branches. If you’re comparing AWS Glue, Lambda, and Step Functions for ETL, the right choice comes down to data size, workflow complexity, cost, and how much control your...
In the Databricks vs Snowflake choice, Databricks usually wins for raw data pipelines, Spark-heavy processing, and machine learning support. Snowflake often wins for fast SQL analytics, cleaner warehouse workflows, and lower day-to-day platform effort. That doesn’t make one “better” in every case. The right pick depends on the jobs your team handles, how your data...