What's Next in Data Engineering Trends 2025?
Table of Contents
- Modern Data Engineering: Built for What’s Next
- Latest Trends of Data Engineering
- AI & Automation: Help, Not a Magic Fix
- More Products, More Complexity
- The Open Data Format Debate
- The Hidden Risks of Low-Code Tools
- FinOps: Stop Throwing Away Money
- Data Governance: No Longer Optional
- AI’s Hidden Risk: The CoPilot Effect
- The Data Engineering Skill Shift
- Shifting from Fixing Problems to Using Data for Growth
- New Technologies and Tools in Data Engineering
- Future of Data Engineering 2025
- Final Thoughts
Companies are drowning in data but struggling to make sense of it—confusing reports, overloaded dashboards, and AI demands their systems can’t handle. McKinsey says businesses with strong data practices make 20% more money, yet most mid-sized companies are stuck dealing with skill gaps and rising costs.
The real problem? Too many choices. Build a team, hire experts, automate, or train? Every option has risks, and waiting only makes it worse.
The good news? Automation cuts busywork, smart budgeting keeps costs down, and better rules make compliance easy. Now, mid-sized businesses can compete—without wasting time or money.
This is your 2025 data guide. Let’s break it down.
Modern Data Engineering: Built for What’s Next
Another report won’t help if your data keeps breaking. Leaders need reliability, not quick fixes—yet many mid-sized businesses still run on fragile systems.
The shift? Strong data foundations are no longer optional. Companies now use tested processes to keep data flowing smoothly—without constant breakdowns.
A retailer once lost sales because of one bad script. After proper testing, the failures stopped, and stability returned.
Fixing problems isn’t enough. A better system means fewer surprises, clearer accountability, and room to grow—without extra costs.
The right team changes everything. That’s why our data engineering consulting services matter. We don’t just fix issues—we build solutions that last.
Latest Trends of Data Engineering
AI & Automation: Help, Not a Magic Fix
AI speeds things up, but it still needs human oversight. GitHub Copilot helps coders, but it can also introduce mistakes. In data, AI can automate tasks like setting up databases, but without proper checks, it can create bigger problems.
Automation is useful—just don’t let it run wild.
More Products, More Complexity
Businesses are adding products to stay flexible—some by buying companies, others by bundling cloud tools. More revenue streams, but also more moving parts. Fewer vendors mean simpler management but bigger risks. If one fails, everything can break.
Balance is key.
The Open Data Format Debate
Iceberg, Delta Lake, and Hudi were supposed to give companies freedom from vendors. Now, Databricks and Snowflake control most updates. These formats are great for real-time analytics, but the rules keep changing.
Stay flexible so you don’t get locked in.
The Hidden Risks of Low-Code Tools
Tools like Upsolver make data pipelines easier, but they also limit control. A streaming company relied on one platform—until a vendor glitch froze their data for weeks.
Less manual work sounds great, until something breaks and you can’t fix it.
FinOps: Stop Throwing Away Money
Cloud costs add up fast. FinOps helps businesses track spending and cut waste. One company saved thousands just by turning off idle servers overnight.
Simple fixes, big savings.
Data Governance: No Longer Optional
Messy data causes compliance issues and bad AI results. Good governance means clean, reliable data that keeps businesses safe and AI-ready. No one wants to base decisions on junk.
AI’s Hidden Risk: The CoPilot Effect
AI helps code faster, but it also hides small mistakes that add up over time. Relying too much on it weakens real expertise. Use AI for routine tasks, but don’t let it replace human judgment.
The Data Engineering Skill Shift
DIY fixes aren’t enough anymore. Companies need real expertise in databases, cloud systems, and performance tuning. A single skilled expert beats a team of beginners.
Shifting from Fixing Problems to Using Data for Growth
Most companies spend years patching issues, meeting regulations, and keeping pipelines from breaking. Now, they want to use data to create new revenue and drive business forward. That shift takes strategy, not just technology.
New Technologies and Tools in Data Engineering
Big Data is No Longer Just Hype—It’s About What Works
The buzz around big data has faded, and companies now focus on what actually delivers results.
Traditional databases still handle structured data well—PostgreSQL, Oracle, and SQL Server lead the way. If your business relies on images and videos, you’ll need cloud storage like Azure Blob Storage, AWS S3, or Google Cloud Storage.
These files are often queried using AWS Athena, BigQuery, or Microsoft Fabric (since Synapse is being phased out).
Moving Data: Extraction & Pipelines
How you pull and move data depends on your budget and needs.
- Free & Open-Source: Meltano, Airbyte
- Paid & Managed: Fivetran, Rivery
- Cloud-Native: AWS Glue, Azure Data Factory
For managing workflows, Dagster and Apache Airflow are still top choices. Some teams also use ADF (Azure Data Factory) or AWS Glue based on their cloud setup.
Lakehouses & Cloud Data Processing
Forget old-school Hadoop clusters—nobody builds them anymore.
Apache Spark has taken over, and Databricks turned its platform into a Lakehouse with Delta Lake (available on both Azure and AWS). A newer player, Dremio, offers another option using Apache Iceberg for more flexibility.
Business Intelligence (BI) & Analytics Tools
Different tools fit different budgets and needs:
- Microsoft Power BI dominates in Azure but has expensive licensing ($5K+ for Premium, $8K+ for embedded reports).
- AWS Quicksight is solid but confusing to price.
- Tableau (owned by Salesforce) is widely used but pricey and complex.
- Free options: Looker Studio (best for Google Cloud), Apache Superset, and Metabase (open-source but with trade-offs).
Real-Time Data & Cloud Databases
For handling real-time data, Apache Kafka is still king, with managed versions available on AWS, Azure, and Google Cloud.
For cloud databases, companies use:
- Azure CosmosDB (works with SQL, MongoDB, Cassandra)
- Google Cloud Datastore
- AWS DynamoDB
Bottom line: Companies aren’t chasing trends anymore. They’re choosing tools that actually fit their business. Want help making the right picks? Let’s talk.
Future of Data Engineering 2025
Will AI Replace Data Engineers? Not Anytime Soon.
Some people worry AI will take over data engineering.
Reality check: Most companies still struggle with messy, undocumented data. Full automation isn’t possible when old systems, strict regulations, and business rules still need human oversight.
By 2025, data engineering will be in three stages:
- Leaders will experiment with AI automation.
- Most companies will slowly upgrade their tools and improve their processes.
- Some will still be stuck fixing basic problems.
AI Won’t Replace Data Engineers—It Will Help Them
AI can write code, automate tasks, and speed up workflows—but it doesn’t understand business needs, solve complex problems, or think critically.
Here’s what will change:
- AI will make engineers more productive, not replace them.
- Low-code tools will handle routine work but create new challenges.
- The best engineers won’t fight AI—they’ll learn how to use it effectively.
Final Thoughts
More vendors, more AI tools, and stricter rules—midsized businesses have more data challenges than ever. But without a strong foundation, none of it works. That’s where the right partner makes all the difference.
With our data engineering services, you get clear, reliable answers. We don’t just manage data—we connect it to real business results, giving you a smarter, stronger way forward.
Your data should work as hard as you do. Let’s build something that lasts—together.
A solid data strategy turns uncertainty into opportunity.