Writing from our team. The latest news, insights, and resources.
Referring a friend to something you genuinely believe in is one of the simplest yet most powerful ways to create opportunities. With that in mind, we’re excited to introduce the Data Engineer Academy Referral Program—a way to reward you for sharing the benefits of industry-leading data engineering training with the people you know. We designed...
In today’s digital world, both individuals and businesses require a powerful website. However, finding a trustworthy hosting company is an important step in creating a website. Amazon Web Services (AWS) EC2 provides a strong and scalable infrastructure for hosting websites, making it a great alternative for your hosting requirements. Step-by-step instructions for how to host...
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...
A semantic layer in data engineering gives you one shared place to define metrics, business logic, and model relationships, so BI tools show the same numbers everywhere. When two dashboards disagree on revenue or active users, the problem is usually not the chart. The problem is inconsistent logic. That mismatch creates copied SQL, repeated reviews,...
Partitioning and clustering help a warehouse scan less data, which usually means faster queries and lower cost. In plain terms, warehouse partitioning and clustering are table layout choices that improve pruning, not magic fixes for bad SQL or weak models. That matters when dashboards slow down, fact tables keep growing, and cloud bills rise with...
SQL MERGE matches incoming rows to existing rows and then updates, inserts, or deletes them in one statement. Data engineers use it to write upsert logic, finish CDC loads, and make repeat runs safe. In data engineering, SQL MERGE helps you keep warehouse tables current without chaining together separate update and insert jobs. It also...
Data quality tests in SQL help you catch bad rows early, before they break dashboards, audits, or machine learning work. The four checks that matter most are nulls, duplicates, range rules, and referential integrity. They work well in Snowflake, BigQuery, Redshift, and Postgres because the logic stays close to the tables. One null customer_id can...
A slowly changing dimension type 2 keeps the full history of a dimension row. When a tracked value changes, you close the old row and insert a new one instead of overwriting the past. That matters in analytics because you often need to know what was true on a given date. SQL handles the change...
dbt incremental models load only new or changed rows, so you don’t rebuild a full table on every run. That makes pipelines faster, lowers warehouse cost, and helps large tables stay fresh. In practice, most teams choose between three patterns: append for immutable data, merge for rows that change, and snapshots for history. The right...
Most Snowflake real-time projects fail for a simple reason: teams move too fast, skip planning, and treat streaming data like batch data with shorter timing. That works in a demo. It falls apart in production, where late events, duplicates, bad timestamps, and recovery gaps show up fast. If you’re building one of these pipelines, you...
A CDC pipeline captures row changes in a source database, publishes those changes as events, and applies them to a warehouse table. Instead of reloading full tables, it moves only inserts, updates, and deletes. If you’re learning cdc pipeline data engineering, this is one of the clearest patterns to understand because it shows how modern...