SaaS ideas your next customers are already looking for
100 SaaS startup ideas for the people who said they'd pay, from agency owners to dentists. Micro SaaS ideas, AI SaaS ideas and B2B SaaS ideas: find yours, build it, sign them up.
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Pain points in business: what people struggle with
Search the problems to solve, as people described them on Reddit and other platforms, in their own words.
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Unreliable Data Pipelines
Data pipelines silently fail, go stale, or time out, causing downstream breakage and wasted compute.
In their words
in NZST, this effectively means that "Today" doesn't align with the user's actual day until around midday.
Anything in a 15-workflow n8n setup that will bite me as clients grow?
we would have to check whether it's accurate and whether it's been replicated properly
Got tired of getting billed every time a Zapier polling trigger ran just to find zero new rows
Paging thru a long list of runs is a really painful experience.
it seems to cost about $8 a day just for these to run
We have a small startup, ~60 people. The data team is 1 data engineer and 2 data analysts. They have struggled to establish a consistent triaging process.
We find ourselves spinning up spark sessions, and waiting 5 minutes, and executing a quick job.
How do you detect rows entering a predicate without doing work proportional to the size of the set?
it may mean replacing 216 other dataflows that still use query staging to avoid future problems
Why do I need to download market replay data 1 day at a time?
We are running into a bottle neck on the write operations.
Cronjob runs, deleting hundreds of millions of rows quickly. Next day, replica lag's high and MySQL's transaction history is very high.
Doing a BC breaking schema change in a chain of materialized view and repopulating can take a week or more for a big dataset.
Data engineers
Where they talk
- r/MicrosoftFabric
- r/databricks
- r/dataengineering
- r/apachekafka
- r/AI_Agents
- r/snowflake
- r/n8n
- r/Rag
Tools they use today
- Microsoft Fabric
- Kafka
- Databricks
- PostgreSQL
- Debezium
- n8n
- ClickHouse
- Lakehouse
Why those fall short
- Workarounds need manual rebuilding per visual and DST patching
- No way to prove which tables truly need real-time freshness
- replication adds moving parts and accuracy checks; upstream filtering changes force full reloads
- no CDC or soft deletes upstream, so deletes and changes are untracked
- Term is vague; no shared definition of required latency
Map of ideas
Drawing the map…