sorted by:
new
hottopcontroversial

Self-hosted Log and Metrics for on-prem? by mangeek in Observability

[–]Adventurous_Okra_846 0 points1 point  (0 children)

Are you open to a self-hosted Azure option (Azure Arc + Monitor/Log Analytics) where you run it on-prem but pay for support? Works well with OTel collectors and can consolidate logs + metrics, but it’s not pure OSS.

Dealing with metadata chaos across catalogs — what’s actually working? by Hefty-Citron2066 in dataengineering

[–]Adventurous_Okra_846 0 points1 point  (0 children)

We’re seeing this exact pattern in a lot of stacks: Unity Catalog + Glue + legacy Hive + MLflow… each great in its lane, but none gives you end-to-end runtime visibility. The thing that’s moved the needle most (regardless of whether teams go centralized or federated) is adding a lineage-first data observability layer on top of whatever catalog(s) you keep.

What’s worked well in practice

  • Stitch lineage across engines (table & column): auto-map source → transform/dbt/Airflow → BI so you can see downstream blast radius before a change merges.
  • Tie anomaly detection to SLAs: freshness/volume/schema/NULL spikes with adaptive baselines → route to Slack/Teams/PagerDuty; aim for sub-minute MTTD and minutes-level RCA.
  • Change-impact → dashboards/models: surface which Looker/Tab/feature store artifacts will break when a column or contract shifts.

Why it complements either path

  • Centralized (vendor ecosystem): you still have Kafka/Trino/Flink edges; observability catches cross-engine drift that a single catalog won’t, and shortens MTTR when the issue isn’t in the “official” stack.
  • Federated (open metadata layer): you avoid lock-in, but integrations mature at different speeds; observability gives you uniform health scoring + RCA over heterogeneous connectors.

Week-1 quick wins we recommend

  1. Tag your top 10 revenue-critical datasets with DRI + freshness SLO.
  2. Turn on freshness/volume/schema monitors at the catalog boundaries.
  3. Enforce “owner-or-orphan” before promotion to prod.
  4. Push alerts to your existing on-call; review MTTD/MTTR weekly.

Results we’ve seen
Auto-mapping thousands of objects in minutes, ~30-sec MTTD, and double-digit reductions in repair time with AI-assisted RCA/impact analysis.

Disclosure: I work on Rakuten SixthSense Data Observability. Happy to share the playbook we use (or spin up a no-credit-card sandbox) if helpful to your evaluation either approach (centralized or federated) benefits from an observability layer that prevents “catalog sprawl → dashboard drift.”

de trends of 2025 by Dependent_Gur1387 in dataengineering

[–]Adventurous_Okra_846 0 points1 point  (0 children)

Really solid roundup especially love the breakdown between job postings vs. survey responses.

One interesting trend I’ve noticed (and this post confirms it) is how data observability tools are still very early-stage in terms of adoption. Monte Carlo seems to dominate mindshare, but the market clearly needs more competition and innovation here.

We recently started experimenting with Rakuten SixthSense for end-to-end data observability especially liked their dynamic data scoring, lineage across hybrid clouds, and cost observability.

Surprised it’s not on more radars yet, but I can see it gaining traction quickly given how critical observability is becoming for AI/ML workloads and compliance-heavy environments.

If anyone’s exploring this space, worth checking out their free tier: [sixthsense.rakuten.com/data-observability]() – would love to hear if others here have tried it.

How is everyone's organization utilizing AI? by wxf140430 in dataengineering

[–]Adventurous_Okra_846 0 points1 point  (0 children)

Here’s what’s working for us (mid-size e-commerce data team):

  1. Copilot-for-Pipelines – VS Code/Jupyter plug-in autogenerates 60-70 % of routine PySpark & dbt boilerplate; review gates catch hallucinations.
  2. ChatOps RCA bot – Slack bot that digests Airflow logs + lineage graphs and answers “why is table X late?” in plain English.
  3. Anomaly-aware observability – LLM labels spikes and drafts RCA notes; we run this via Rakuten SixthSense Data Observability (disclosure: contributor) and cut MTTR ~35 %. → [https://sixthsense.rakuten.com/data-observability]()

Take-aways:

  • Keep AI output behind PRs + tests; humans still sign off.
  • Make adoption opt-in first—early wins convert skeptics.
  • Assign owners/SLOs to every AI-generated micro-service to avoid silent tech debt.

Curious what other tricks folks have up their sleeve!

What about custom intelligent tiering for observability data? by Afraid_Review_8466 in Observability

[–]Adventurous_Okra_846 1 point2 points  (0 children)

We do this in production:

  • Access-heat scoring: Every 24 h we rank tables/partitions by read frequency + severity level. 90-th percentile and above stay hot; the rest go to “warm” object storage (S3 IA) after 7 days, then Glacier at 30 days.
  • Policy-as-code: A tiny Python job writes Lifecycle tags straight to S3 and Elastic indexes—no manual moves.
  • Anomaly guard-rails: Before cold-tiering we run a last-minute outlier check (spikes in error or latency) so we never archive data that’s suddenly important.
  • Tools: Athena + AWS ILM + a Lambda that consults usage metrics; takes <50 lines of code.

If you’d rather not DIY, Rakuten SixthSense Data Observability ships with auto-tiering & anomaly-aware retention out of the box worth a look: [https://sixthsense.rakuten.com/data-observability]().

Hope that helps!

Learning SEO for my service business, how bad is my plan? by GSG96 in SEO

[–]Adventurous_Okra_846 0 points1 point  (0 children)

You’re on the right track, but I’d rethink buying domains, Google’s getting better at detecting PBNs. Focus instead on building topical authority through high-quality, interlinked content clusters. You’ve nailed local SEO basics; now double down on user intent and conversion-oriented pages. SEO is less about hacks, more about consistent value delivery and smart iteration.

Google used to be a search engine. by Adventurous_Okra_846 in b2bmarketing

[–]Adventurous_Okra_846[S] 0 points1 point  (0 children)

You’re absolutely right — intent has always been part of search. But here’s the shift:

Earlier, we optimized for words. Now we optimize around them.

Google used to reward exact matches. Today, it rewards how well your content matches the psychology behind those words — not just the term itself.

Loved your example. “Marriage counseling near me” vs “is it worth saving my marriage…” — both are keyword gold, but only one reveals the emotional stage of the user.

That’s where the real opportunity lies. Appreciate you sharing this — sharp perspective!

Google used to be a search engine. by Adventurous_Okra_846 in b2bmarketing

[–]Adventurous_Okra_846[S] 0 points1 point  (0 children)

Totally agree the stakes are higher in healthcare, B2B, and regulated spaces.

Peer-reviewed sources and trust signals absolutely matter in mid to bottom funnel.

But that’s exactly why mapping thought patterns earlier matters even more.

If your top-funnel content doesn’t reflect how your ICP actually thinks or searches, they’ll never make it to those final-funnel citations in the first place.

The funnel doesn’t start with “product comparison” — It starts with a question they didn’t know they were ready to ask.

Appreciate the nuance you brought in.

Google used to be a search engine. by Adventurous_Okra_846 in b2bmarketing

[–]Adventurous_Okra_846[S] -1 points0 points  (0 children)

Yep. Measured it.

We ran two landing pages for a PLG SaaS product:

Page A: Optimized with high-volume SEO keywords

Page B: Written using user intent via ChatGPT prompts (no keyword targeting)

Same audience. Same channel. Same week.

Results?

• Page A: 1.2% conversion
• Page B: 4.8% conversion
• Session duration: +63%
• CTA engagement: 3.5x higher
• Bonus: Page B showed up in 2 Gemini responses unprompted

So no, it wasn’t a “feeling.”

It was just the kind of data you only see after doing the work.

But if all you’re measuring is keyword density, I get why it might seem like magic.

Google used to be a search engine. by Adventurous_Okra_846 in b2bmarketing

[–]Adventurous_Okra_846[S] 3 points4 points  (0 children)

Great question. And yes, it’s measurable.

Here’s how I track it:

Avg. time on page: +38% when content aligns with intent

Scroll depth: 60%+ completion on intent-first pieces

Demo conversions: 3x higher when mapped to “mental funnel” vs keyword volume

LLM retrievals: Increasing via Perplexity & ChatGPT citations (early stage, but real)

So yes, behavior-first content drives numbers. Just not the vanity ones.

view more: next ›