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 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...
Quick Summary Not sure which cloud path will open more doors for your career — AWS or Azure? Explore our guide to the top data engineering platforms for career changers and discover where to begin. Overview of AWS and Azure in Data Engineering AWS provides an extensive array of services that cater to the diverse...
You do not need to learn every tool in a junior data engineer job description before you apply. Most postings mix must-haves, preferred skills, and a team’s wish list. Your job is to spot the few skills that show up more than once and focus there first. That is usually enough to get interview-ready faster....
Strong data engineer resume bullets show business impact with numbers, not job duties. Hiring managers want proof that you improved speed, scale, reliability, cost, or data quality. If your resume reads like a task tracker, it won’t stand out. The fix is simple. Tie your work to results, then make those results easy to scan....
Cloud engineering has quickly become one of the most lucrative and in-demand career paths in the tech industry. As businesses across the globe increasingly rely on cloud computing to power their operations, the demand for skilled professionals in platforms like AWS (Amazon Web Services), Microsoft Azure, and Google Cloud Platform (GCP) continues to grow. By...
Are you dreaming of becoming a data engineer but unsure where to start? The Data Engineer Academy’s 1-on-1 Coaching Program is a proven path to learn how to code and land your dream data engineer role in as little as 3 months. This in-depth coaching experience is motivational, beginner-friendly, and results-driven, designed to take you...
Data observability is the practice of checking data systems so teams catch bad data before people trust it. It tells you when data is late, missing, changed, or wrong. For beginners, the foundation is four checks: freshness, volume, schema, and quality. Those checks help stop quiet failures from reaching dashboards, reports, and downstream models. Once...
Fast data is useless if nobody trusts it. A strong real-time data project proves you can move events, keep raw history, and turn messy streams into tables people will use. A Snowflake project with Kafka and dbt does that well. In 2026, teams want fresh data, clean models, and tests that catch bad rows before...