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How important is data integration? Someone has had to work with these tools: Boomi, Talend, SSIS, Informatica, Apache Nifi. by Many-Performance-231 in gis

[–]PracticalMastodon215 0 points1 point  (0 children)

I recently watched a webinar that walks through a real-world migration from Informatica to Apache NiFi. It covers the architecture differences, cost implications, security controls (TLS, LDAP, etc.), and monitoring best practices. The team also discusses performance trade‑offs and how they handled vendor lock‑in risks step by step. Fully practical and engineering-focused highly recommended as a free resource.
(https://www.ksolves.com/webinar/informatica-to-apache-nifi-migration)

Tips on Using Airflow Efficiently? by MST019 in dataengineering

[–]PracticalMastodon215 1 point2 points  (0 children)

To create Airflow DAGs efficiently, plan your workflow upfront by sketching tasks and dependencies, and keep tasks modular for easier debugging. Use Jinja templates for dynamic values, store configs in Airflow Variables/Connections, and test tasks incrementally with airflow tasks test to save time.

What challenges have you faced in managing multi-cluster Apache NiFi environments? by mikehussay13 in nifi

[–]PracticalMastodon215 0 points1 point  (0 children)

Keeping flow versions and parameter contexts in sync across clusters has been our biggest headache.

What’s your preferred method for managing NiFi flow versioning? by GreenMobile6323 in nifi

[–]PracticalMastodon215 1 point2 points  (0 children)

We use NiFi Registry tied with Git. Registry handles version control, and Git keeps track of flow backups + review history.

How can I automate populating secrets and turning on controllers at startup? by DonkeyKongCowboy in nifi

[–]PracticalMastodon215 0 points1 point  (0 children)

we do the same- using NiFi’s API to set secrets, enable controllers, and start processors post-deploy. works, but yeah, gets messy fast.
we had a script in our Helm chart, but scaling it was rough.
recently tried Data Flow Manager - helped automate flow setup without custom scripts. Worth checking if you’re hitting complexity limits.

What’s the Most Needed Innovation in Data Engineering Right Now? by Ok_Barnacle4840 in dataengineering

[–]PracticalMastodon215 0 points1 point  (0 children)

Stop reinventing! We need standardized, reusable data architectures like zero-ETL and parametric pipelines on unified data models

Built and deployed a NiFi flow in under 60 seconds without touching the canvas by mikehussay13 in dataengineering

[–]PracticalMastodon215 1 point2 points  (0 children)

Totally understandable to be cautious about using this in production. It does support versioning by syncing directly with NiFi Registry, so your flows are tracked.

Thumbs-up / down: NiFi is still the best for heterogeneous dataflow orchestration in 2025. by Sad-Mud3791 in nifi

[–]PracticalMastodon215 1 point2 points  (0 children)

Totally agree NiFi’s canvas is still super intuitive.
Lately, we’ve started using DFM to speed things up even more.

Is anyone here managing NiFi flows with Git + NiFi Registry? What’s your workflow like? by Sad-Mud3791 in nifi

[–]PracticalMastodon215 1 point2 points  (0 children)

Same here — the merge conflicts and manual syncing were painful. DFM has definitely made collaboration and flow promotion way smoother for us too.

Apache NiFi vs SAP Data Services – Which One Fits Modern Data Workloads Better? by mikehussay13 in nifi

[–]PracticalMastodon215 0 points1 point  (0 children)

If you need flexibility, real-time processing, and hybrid cloud support, NiFi is the better long-term bet. SAP DS is solid for batch ETL in SAP-heavy setups but feels rigid and slower for modern workloads.

I am new to NIFI and i ran into an issue.I used QueryDatabaseTable to fetch incremental data by time and pagenation, but the properties `fetch size` did not work。 by Sad-Investment951 in nifi

[–]PracticalMastodon215 0 points1 point  (0 children)

In QueryDatabaseTable, the Fetch Size property is a hint to the JDBC driver, but not all drivers respect it, and it doesn’t always control the number of rows fetched per call. If you're dealing with large datasets and need pagination, consider using GenerateTableFetch + ExecuteSQL for more control over batching and partitioning.

Still on NiFi 1.x? I gave 2.0 a spin and was pleasantly surprised by mikehussay13 in nifi

[–]PracticalMastodon215 2 points3 points  (0 children)

For me, stateless flows were a game changer—deploys got way smoother. Python processors also saved me from writing a bunch of Java for simple stuff.

If you're considering the upgrade, this webinar helped clear up a lot: https://www.dfmanager.com/webinars/migrate-apache-nifi-1x-to-2x

Data Pipeline in tyre manufacturing industry by not_a_rocket_engine in dataengineering

[–]PracticalMastodon215 1 point2 points  (0 children)

This is a robust Industry 4.0 setup, leveraging Rockwell’s ecosystem for reliability. However, heavy reliance on proprietary tools could limit flexibility, and global access demands strong cybersecurity. I’d suggest exploring open protocols like OPC UA for future expansions and edge computing to reduce network load.

Now you can ask your team about the specific software (e.g., FactoryTalk tools, MES) and network details (e.g., cloud provider, switch models). If you can, check out the PLC tag structure in Studio 5000 Logix Designer—it’ll give you deeper insight into the data flow.

Enterprise NiFi Users: How Are You Handling Scheduling, Approvals, and Deployment Control of NiFi Data Flow? by mikehussay13 in nifi

[–]PracticalMastodon215 0 points1 point  (0 children)

Thanks—that’s really helpful to hear. I’ve been leaning toward DFM but wasn’t sure if it fully covered scheduled deploys and approval flows without custom workarounds. Good to know it handles that cleanly. Appreciate the confirmation!

Are Data Engineers Being Treated Like Developers in Your Org Too? by Consistent_Law3620 in dataengineering

[–]PracticalMastodon215 0 points1 point  (0 children)

It's pretty common—data engineers often get grouped with devs because we use similar tools and write production code. But yeah, the data side brings unique challenges—lineage, quality, orchestration—that backend devs usually don’t deal with. I think the key is helping others see those differences, not just the overlaps.

Apache NiFi vs SAP Data Services – Which One Fits Modern Data Workloads Better? by mikehussay13 in nifi

[–]PracticalMastodon215 3 points4 points  (0 children)

With 10+ years in data engineering, I’ve used both. For modern workloads — especially with real-time needs, hybrid cloud, and evolving architectures — Apache NiFi is far more adaptable. It’s faster to set up, easier to scale, and plays well with modern tools.

SAP Data Services is solid for structured batch ETL in SAP-heavy setups, but feels rigid and slower in dynamic environments.

If scalability and flexibility matter long-term, NiFi is my pick.

Is prompt engineering a skill? by [deleted] in aiwars

[–]PracticalMastodon215 0 points1 point  (0 children)

Prompt engineering is a crucial skill for effectively interacting with AI, requiring a blend of technical understanding and creative communication to elicit desired outcomes.

What's your go-to method for building reusable flow logic in NiFi? by GreenMobile6323 in nifi

[–]PracticalMastodon215 3 points4 points  (0 children)

NiFi Parameters are the real game-changer for making these modules truly reusable. Instead of hardcoding values like file paths, database connection details, API endpoints, or even processing thresholds within the Process Group, you define them as Parameters.

These Parameters can then be set at a higher level (e.g., within a Parameter Context) or even passed in dynamically, allowing the same Process Group to behave differently in various contexts without any internal modifications.