Opportunities › Unified data warehouse platform for organizations › Proposed app
Tidepool
One analytics warehouse that pulls in your files and databases, with no stack to stitch
- Score
- 46
- confidence 38
- Evidence
- 3
- items on the page
- Rivals
- 3
- named in sources
- First version
- 70 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 1996
- Mature market · AI estimate
ScreensConcept
Desktop app: Data teams query warehouses, manage connections, write SQL, and monitor operations in extended analytical sessions requiring multiple views and keyboard work. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Sources
All connected6 sources
About this appAI proposal
Tidepool is a hosted analytics warehouse for mid-sized teams that today run an OLAP store, a relational database and a query federation engine side by side. It brings fast analytical queries and the data connections into one service, so one team can run it.
Point it at a cloud storage bucket and it imports new files on a schedule. Connect a relational database and query it directly, or copy tables in. Dashboards and SQL work across all of it without a separate federation layer.
It is built for publishers and data-driven companies with a small data team. You get one place to monitor, one bill and one set of permissions, instead of three systems to patch and keep in step.
- Tagline
- Your data, gathered in one place.
- Voice
- PlainCalmPrecise
- Why this name
- A tidepool collects water from many channels into one still, clear place, as the product gathers many sources into one warehouse.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Automatic import from cloud storage
Watch a bucket or folder and load new files into tables on a schedule, with schema detection and a clear log of every load.
No member yet
Direct relational database connections
Connect a Postgres-style database read-only. Query it live or sync chosen tables into the warehouse.
No member yet
One SQL endpoint for all sources
Join imported files, synced tables and live connections in a single query. No separate federation engine to run.
No member yet
Fast analytical tables
Columnar storage for big event and content tables, built for group-by and time-range questions.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#0F3D4CDeep Tide · primary#F2F7F5Sea Foam · surface#13262DInk · text#E8704FCoral · accent#2E8B6EKelp · data#E6D9BFSand · secondary surface
Typography
Aa
Tidepool
PricingAI proposal
The material gives no competitor prices. It only suggests pricing by data volume, query throughput or storage. Tiers are a starting point to test; dollar amounts are left open until cost per terabyte is known.
Trial
Free
- Up to 20 GB stored
- 2 sources
- 1 user
- Community support
Team
Contact us
- Priced on storage and query volume
- Unlimited sources
- Up to 15 users
- Email alerts
- Email support
Business
Contact us
- Higher volume limits
- Roles and audit log
- Migration help
- Priority support
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Shared outputs. Analysts share saved queries and result links with colleagues, editors and executives, who view them in a read-only page that carries the product name.
- Do they become customers?
- Viewers are rarely the buyers of a warehouse, so exposure alone will bring few customers. Test whether viewers who are data leads ask to try it, and track how many shared links lead to sign-ups.
- First customers
- Mid-sized digital publishers whose small data team runs an OLAP store, Postgres and a federation engine, and wants to cut the operations load.
- Where to reach them
- Hacker News and data forums: Post migration write-ups and honest comparisons where teams already discuss consolidating their stacks.
- Direct outreach to data leads: Message heads of data at publishers and offer a free side-by-side migration of one dataset.
- Data engineering communities: Join Slack and meetup groups, share a connector guide, and invite members to a pilot.
- First milestone
- Three publishers each run one real dataset in a pilot, and at least two agree to a paid plan within 60 days.
Why people already pay for this
- Public claimMedium-sized organizations with data warehouse needs currently use multiple tools (OLAP, relational, and query federation) that require significant operational effort.
- Public claimOrganizations experience performance and maintenance benefits from consolidating to a unified platform with built-in data connectors.
- Public claimThe platform handles automatic data import from cloud storage and direct connections to relational databases.
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Analytical engineOpen-source columnar OLAP engine, self-hostedFast group-by queries on large tables are the core product and Postgres alone will not give that.
- This app · Object storageS3-compatible bucket storageHolds imported files and columnar data cheaply, and reads customers' buckets.
RisksAI inference
- TechnicalRunning a multi-tenant analytical engine with live federation across customer databases is hard to make fast, isolated and cheap. A weak first version could miss the performance buyers expect.
- CommercialTeams that already run an OLAP store, Postgres and a federation engine may find switching costly. Evidence is only public claims from one forum, so demand is unproven.
- DataCustomers must share database and bucket credentials and data. Security reviews, residency questions and trust could slow every early sale.
- PlatformLarge cloud warehouse vendors can bundle similar import and query features at low marginal cost, squeezing a small team on price.
- TechnicalLive queries on customer production databases can slow those systems. Read replicas, limits and sync defaults are needed from day one.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.