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...
I get asked some version of this question a lot: “you work with data, so why didn’t you go into data science?” It’s a fair question, since from the outside the two careers can look almost identical. Both work with data pipelines, both touch Python and SQL, both show up on the same team. But...
If you’re learning data engineering, you’ve probably hit this question at some point: do I need to learn machine learning too? Do I need statistics? Should I be doing Kaggle competitions along with my SQL and Airflow practice? The honest answer is: some data science knowledge will make you a better data engineer, but most...
If you’re trying to break into the data field, you’ve probably run into the same wall everyone else does; the terms “data engineer” and “data scientist” get used interchangeably in job posts, LinkedIn bios, and course marketing even though the jobs themselves are not the same. The confusion isn’t your fault. Both roles work with...
Can you write a SELECT query? Good. Can you trust that query inside a nightly pipeline that feeds finance, product, and machine learning tables? That’s the real bar. SQL still matters in 2026 because warehouses, lakehouses, dbt models, and data quality checks still run on it every day. This guide stays practical, with the SQL...
Data engineers usually earn more than data analysts. In the data engineer vs data analyst comparison, engineering tends to win because companies pay a premium for people who build pipelines, warehouses, and cloud data systems. That doesn’t make analytics a low-paying path. Strong analysts in product, finance, and tech can still earn excellent salaries, especially...
Privacy-aware data engineering means finding personal data early, limiting who can see it, and exposing only what people truly need. In practice, that means PII detection, data masking, and access controls built into the data pipeline, not added after a dashboard ships. This matters in warehouses, lakehouses, logs, and BI tools because copied data spreads...
Data SLAs and SLOs set clear reliability targets for your pipelines. An SLA is the promise users can count on, while an SLO is the internal target your team tracks to keep that promise. When a dashboard is late or a table is missing rows, vague complaints do not help. A good data SLAs and...
Data mesh is a way to organize data work around business domains, not one central data team. In data engineering, that means sales data stays close to the sales domain, finance data stays close to finance, and each team owns what it publishes. You should care because if you build pipelines, models, or platforms, a...
Introduction If you are already working with data as an analyst, a BI developer, a QA engineer, or a SQL-fluent IT professional you have probably noticed something: data engineering jobs pay well, the demand is strong, and a meaningful portion of the role overlaps with work you already do. But there is a gap between...
Introduction Data engineering is one of the fastest-growing technical disciplines in the industry, and the demand for qualified professionals is outpacing the supply. That gap is an opportunity but only for people who show up with the right skills. If you are transitioning into data engineering from a neighboring role – analytics, IT, software-adjacent work,...
Introduction Most content about becoming a data engineer focuses on the technical stack: SQL, Python, dbt, Airflow, Spark, cloud platforms. That is appropriate. The technical foundation matters. But there is a reason experienced data engineers and hiring managers consistently say the same thing: the candidates who struggle in their first year are not usually struggling...
Data catalogs help data engineers find trusted data faster, understand where it came from, and keep pipelines easier to debug. For dxata engineering teams, the right catalog turns scattered metadata into searchable context, so you spend less time chasing table owners and more time fixing real issues. DataHub, OpenMetadata, Collibra, and Alation all solve that...
Synthetic data testing helps when you need safe, fast, repeatable pipeline tests. It fails when the data is too clean, too random, or too simple to expose what production will do. If you build ETL, ELT, or streaming jobs, synthetic data can speed up development, but it can’t replace reality checks. The safest approach is...
LLM observability is the practice of tracking what a model request saw, how it moved through your system, what it returned, and what happened next. Data engineers need it because LLMs don’t behave like normal batch jobs. Two requests that look the same can still produce different answers, costs, or failures. Basic logs won’t catch...