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...
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...
Are you considering a switch to data engineering and wondering how AI might fit in? You’re not alone. As AI technologies surge in popularity, the demand for skilled data engineers is rising in tandem. In fact, data engineering roles are projected to grow by 21% by 2028, adding hundreds of thousands of positions. This growth...
Terraform matters when your data stack needs repeatable setup, fewer manual mistakes, and easier teamwork. In data engineering, Terraform helps you create storage, warehouses, permissions, and network pieces with code instead of console clicks. It starts to matter when pipelines move past one-off scripts. Once you have shared environments, cloud complexity, or audit needs, manual...
OpenLineage gives teams a standard way to track lineage across tools, and Marquez gives them an open-source place to store and view that lineage. If you’re trying to make OpenLineage data lineage useful in a real platform, this pairing is one of the clearest options. It matters because modern pipelines cross schedulers, SQL models, Spark...
Production Airflow problems usually come from three places: bad retry settings, risky backfills, and weak recovery plans after failures. The fix is a short set of airflow production best practices that keep reruns safe, reduce alert noise, and stop duplicate writes. If your DAGs fail at 2 a.m., the hard part is not clicking “clear”...
Data engineers choose batch, streaming, or micro-batch by matching the pipeline to business timing. The batch vs streaming data pipeline decision depends on latency, cost, data volume, and what the team can support. Batch runs on schedules, streaming handles events as they arrive, and micro-batch groups small bursts every few seconds or minutes. The best...
You don’t need a heavy streaming stack to get fresh analytics. In many teams, Snowflake Streams and Tasks are enough to move new data through a pipeline every few minutes. That makes this a great project for beginners in data engineering, analysts moving into ELT, and job seekers building a portfolio. New rows land in...