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Atlas Platform Services
Secure the front door. Email is where most attacks arrive — Atlas Platform Services is the data services on top of Atlas — Stream Processing (real-time, GA 2024), Data Federation (query Atlas + S3 in one query), Charts & Edge Server. Stream, federate and visualise — on the same Atlas data, without separate systems.
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How it’s rated
Full scoreboard ↓Quick answer
The MongoDB platform family
This page covers Atlas Platform Services — streaming, federation & data services. The rest of the MongoDB platform:
Quick facts
30-second orientation- Product
- Atlas Platform Services — data services on Atlas
- Vendor
- MongoDB (founded 2007 · New York)
- The category
- Streaming, federation & data services
- What it does
- Stream, federate, visualise — on your Atlas data
- Stream Processing
- GA 2024 — real-time streams via aggregation
- Data Federation
- Query across Atlas + S3/object storage
- Also
- Atlas Charts (viz) · Edge Server (IoT/edge)
- Note
- Realm/Device Sync deprecated (EOL Sep 2025)
- Vs
- Confluent/Kafka+Flink, Kinesis, Trino, Athena, Tableau/Power BI
- In India via
- TechBag — scoping, honest compare, GST
Understand streaming, federation & data services before you buy
Most product pages skip this. We start here — so you buy a capability, not a buzzword.
What is Atlas Platform Services?
The data services on top of Atlas — Stream Processing (real-time, GA 2024), Data Federation (query Atlas + S3 in one query), Charts (dashboards) and Edge Server (edge/IoT) — all on the same Atlas data.
Separate systems vs Atlas Platform Services (on one data) — the honest table
What consolidation actually replaces, dimension by dimension.
| Dimension | Unprotected / signature email | Atlas Platform Services (MongoDB) |
|---|---|---|
| Streaming | Separate Kafka+Flink stack | Stream Processing (aggregation) |
| Skills | New streaming language | The aggregation framework you know |
| Cross-source query | ETL into one place first | Federate Atlas + S3 in one query |
| Cold data | Copied or unqueryable | Online Archive, still queryable |
| Visualisation | Export to a BI tool | Atlas Charts on live data |
| Edge / IoT | Deprecated Device Sync | Edge Server (the newer story) |
| Systems to run | Many, to sync | One platform, same data |
| Best fit | (varies) | Teams on Atlas wanting fewer systems |
Atlas Platform Services is the set of data services on top of Atlas — Stream Processing (real-time streams via the aggregation framework, GA 2024), Data Federation (query across Atlas and S3 in one query), Charts (dashboards) and Edge Server (edge/IoT) — on the same Atlas data. Honest: each competes with a deeper specialist (Confluent/Kafka+Flink or Kinesis for streaming, Trino/Athena for federation, Tableau/Power BI for BI), and Realm/Device Sync mobile sync is deprecated (EOL Sep 2025). TechBag scopes it & adds GST.
The five pieces of the platform
Vendors love diagrams; buyers need to know what they’re actually operating. Here’s the whole platform, demystified.
Atlas Stream Processing
Process real-time event streams (from Kafka topics or Atlas change streams) using the familiar MongoDB aggregation framework — continuously transform, filter and react to data in motion, and materialise results back into collections. GA in 2024. Data in motion, the MongoDB way. Stream without a separate engine.
Data Federation
Run a single MongoDB query ACROSS Atlas clusters and external object storage (like Amazon S3) — without moving or copying the data. Combine hot operational data and cold archived data in one query. Query where the data lives. Federate without a pipeline.
Atlas Charts
Build dashboards and embed charts directly on your Atlas data — no export, no separate BI connector, no sync. Visualise operational data in place and share or embed it. See your data where it lives. Charts, built in.
Edge Server
Extend MongoDB to edge and IoT deployments — a local data tier that syncs with Atlas for intermittently-connected environments. (This is the newer edge story; Realm/Device Sync mobile sync is deprecated.) Data at the edge, synced to Atlas. Run where connectivity is spotty.
One Platform, Same Data
Stream Processing, Data Federation, Charts and Edge Server all run against the SAME Atlas data — so you stream, federate and visualise without standing up separate streaming, query or BI systems to sync. (Honest: each competes with a deeper specialist.) One platform. Fewer systems to run.
One agent on every machine, one console over all of them — modules attach without a second operational world.
Twelve capabilities. Stream, federate, visualise.
Atlas Platform Services lets teams stream, federate and visualise on the same data — the data services of portfolio, and paired with the human firewall.
Real-Time Stream Processing
Continuously process event streams from Kafka or Atlas change streams — transform, filter, window and react to data in motion. GA 2024. React to data as it arrives. Streams, not just batch.
The Aggregation Framework
Write stream processing with the SAME MongoDB aggregation framework your team already knows — no new streaming language to learn. Familiar pipeline syntax on streams. One skill set.
Kafka & Change-Stream Sources
Ingest from Apache Kafka topics or Atlas change streams, and materialise results back into collections — connecting your streams to your operational data. Bridge streams and your database. Sources in, results out.
Continuous Processing
Long-running processors handle event streams continuously — windowing, joins and transformations on data in motion, materialised for downstream use. Always-on processing. From batch to continuous.
Cross-Source Federated Query
Query across Atlas clusters and external object storage (Amazon S3) in ONE MongoDB query — no ETL, no copying data into the database first. Query many sources as one. No pipeline to build.
Query Object Storage In Place
Analyse cold, archived or bulk data sitting in S3-style object storage directly — combine it with hot operational Atlas data without moving either. Reach the cold data. Hot + cold, one query.
Online Archive & Data Tiering
Tier ageing data out of hot clusters to lower-cost storage while keeping it queryable via federation — control cost without losing access. Archive without losing the data. Tier to cut cost.
Data Lake Pipelines & Export
Move and land Atlas data into object storage on a schedule for analytics and archival — the platform bridges operational and analytical stores. Land data for analytics. Bridge to the lake.
Charts & Dashboards
Build interactive dashboards on your Atlas data with Atlas Charts — no export, no separate BI connector, no sync pipeline to maintain. Dashboards on live data. Built in, not bolted on.
Embedded & Shared Charts
Embed charts into your own apps and share dashboards — put operational insight in front of users where they work. Insight in your app. Share or embed.
Edge Server (IoT / Edge)
Extend MongoDB to edge and IoT with a local data tier that syncs to Atlas for intermittently-connected sites. (The newer edge story — Realm/Device Sync is deprecated, EOL Sept 2025.) Data at the edge. Sync when connected.
All-In-One — But Specialists Go Deeper
For teams on Atlas, these services cut systems and glue code — but each competes with a deeper specialist: Confluent/Kafka+Flink or Kinesis for streaming, Trino/Athena for federated query, Tableau/Power BI for BI. Simplicity, not out-depth. Pick per need.
Watch Atlas Platform Services in action
The overview, getting started, and protecting M365 email.
Atlas Stream Processing — Demo
Real-time streams with the aggregation framework.
Introducing Atlas Stream Processing
Process data in motion, on Atlas.
MongoDB .local — Keynote
The platform vision, including the data services.
Want a live, India-context walkthrough on your own fleet?
Book a guided demo →The endpoint catches what arrives. Email stops it arriving.
Here’s what genuinely sets Atlas Platform Services apart (and where a specialist fits better).
Stream, federate and visualise — on the same data, no separate systems
The single biggest reason teams choose Atlas Platform Services is that it lets them stream, federate and visualise data ON THE SAME Atlas platform — against the same data — without standing up and syncing separate streaming, query and BI systems. The problem it solves: a modern data workload often needs more than an operational database — you want to process events in real time, query across your operational data AND cold data in object storage, and put dashboards in front of users. Doing that traditionally means stitching together a streaming engine, a federated-query layer and a BI tool — each a separate system to run, secure, pay for and keep in sync. That glue code and sprawl is a real, ongoing tax. What Platform Services provides: Atlas Stream Processing (real-time streams via the aggregation framework), Data Federation (query across Atlas and S3-style storage in one query), Atlas Charts (dashboards on your data) and Edge Server (the edge/IoT tier) — all running against your Atlas data, in one platform. Why it matters: data locality and consolidation cut the sync pipelines, the number of systems you operate, and the glue code — a genuine simplification, especially for a team already standardised on Atlas. The value: Atlas Platform Services unifies streaming, federated query and visualisation on your Atlas data — fewer systems, less glue. For a consolidated data platform, this matters. TechBag scopes which services you need. TechBag helps you run fewer systems.
Stream processing with the aggregation framework your team already knows
A defining strength of Atlas Stream Processing (GA in 2024) is that it uses the SAME MongoDB aggregation framework your developers already know to process data in MOTION — not a new streaming language or engine to learn. The problem it solves: real-time stream processing traditionally means adopting a specialist stack (Kafka plus Flink, say) with its own languages, operational model and skills — a steep investment, especially for a team whose expertise is in MongoDB. What Atlas Stream Processing provides: continuous processing of event streams (from Kafka topics or Atlas change streams) written as aggregation pipelines — windowing, filtering, joins and transformations — with results materialised back into collections. Your team writes streams the way they write queries. Why it matters: reusing the aggregation framework collapses the learning curve and the operational overhead of a separate streaming platform for teams already on MongoDB — they can react to data in motion without hiring streaming specialists or running another cluster. (Honest note: for serious, high-throughput streaming, Confluent/Kafka+Flink and Kinesis go deeper — see the honest scope.) The value: Atlas Stream Processing brings real-time streams into the MongoDB aggregation framework — no new engine or language for teams already on Atlas. For accessible streaming, this matters. TechBag scopes it vs the streaming specialists. TechBag helps you stream without a separate stack.
Federated query — combine hot operational data and cold archive in one query
A distinctive strength is Data Federation — the ability to run a SINGLE MongoDB query across Atlas clusters AND external object storage (like Amazon S3), without moving or copying the data first. The problem it solves: valuable data ends up spread across systems — hot operational data in Atlas, cold or archived or bulk data in object storage — and combining them traditionally means building ETL pipelines to copy everything into one place before you can query it. That’s slow, costly and duplicative. What Data Federation provides: query hot Atlas data and cold S3-style data TOGETHER in one query, in place, with no ETL — plus Online Archive to tier ageing data out to cheaper storage while keeping it queryable, and data-lake pipelines to land Atlas data for analytics. Why it matters: querying data where it lives cuts the pipelines, the data duplication and the storage cost — you archive to control cost without losing access, and you analyse across hot and cold data without a warehouse migration. (Honest note: for large-scale federated analytics, Trino/Presto and Amazon Athena go deeper — see the honest scope.) The value: Data Federation queries across Atlas and object storage in one query — no ETL, hot and cold data together, cost tiered without losing access. For querying data in place, this matters. TechBag scopes it vs the query specialists. TechBag helps you query without moving data.
Visualisation and the edge — dashboards in place, data where connectivity is spotty
A practical strength of Platform Services is that it rounds out the platform with visualisation (Atlas Charts) and an edge tier (Edge Server) — so you can put dashboards on your live data and run MongoDB where connectivity is intermittent. The problem it solves: to visualise operational data you traditionally export it to a separate BI tool (another connector, another sync); and to run at the edge or in IoT you need a local data tier that reconciles with the cloud when connectivity returns. What Platform Services provides: Atlas Charts builds and embeds dashboards directly on your Atlas data — no export, no separate BI sync — and Edge Server extends MongoDB to edge/IoT with a local tier that syncs to Atlas for intermittently-connected sites. (Important and honest: MongoDB’s older mobile-sync product — Realm / Atlas App Services Device Sync — is DEPRECATED, with end-of-life in September 2025, so this is NOT a mobile-sync story; Edge Server is the newer edge approach.) Why it matters: in-place dashboards cut the BI-sync tax for operational insight, and Edge Server addresses genuine edge/IoT needs — both without leaving the Atlas platform. (Honest note: for rich enterprise BI, Tableau/Power BI go deeper.) The value: Atlas Charts visualises your data in place and Edge Server extends MongoDB to the edge — without exports or a separate BI stack. For in-place insight and the edge, this matters. TechBag scopes what you need. TechBag helps you visualise and reach the edge.
From the category leader — and TechBag adds honest comparison and India support
Atlas Platform Services comes from the document-database category leader, and for Indian teams TechBag adds the scoping, honest comparison and INR/GST support that make adopting it straightforward. MongoDB the company: founded in 2007 as 10gen, renamed MongoDB in 2013, public on NASDAQ (MDB) since 2017; ~$2.5B revenue growing ~23% YoY (FY2026), with CJ Desai (ex-Cloudflare) as CEO since November 2025; Atlas is ~75% of revenue, and MongoDB is source-available under the SSPL. These data services extend that flagship platform. India relevance: MongoDB runs one of its LARGEST global hubs in Gurugram (DLF Cyber City) — product, engineering and sales — plus Bengaluru, with a large trained India developer community and MongoDB.local events — genuine India engineering depth. Where TechBag adds value: because each Platform Service competes with a deeper specialist, TechBag scopes which services you actually need, compares them honestly (vs Confluent/Kafka+Flink and Kinesis for streaming, Trino/Athena for federation, Tableau/Power BI for BI), and adds INR/GST (18%) invoicing and local support. The value: Atlas Platform Services extends the category leader’s platform, with major India R&D — and TechBag adds honest comparison, scoping and INR/GST. TechBag supplies it with local support. TechBag provides the platform services, made local for India.
The honest scope
Atlas Platform Services is the set of data services on top of Atlas — Atlas Stream Processing (real-time streams via the aggregation framework, GA 2024), Data Federation (query across Atlas and object storage in one query), Atlas Charts (dashboards on your data) and Edge Server (the edge/IoT tier) — all against the same Atlas data. It’s an attractive all-in-one for teams already on Atlas: fewer systems, less glue code. The honest framing — strengths, and where a specialist fits better: its strengths are data locality (services on the same data), simplicity (reuse the aggregation framework and one platform), and consolidation (fewer systems to run). But honest caveats matter, because EACH piece competes with a deeper best-of-breed specialist. (1) Streaming: for serious, high-throughput, complex stream processing, Confluent/Kafka + Flink is the deep specialist, and AWS Kinesis is the AWS-native option — Atlas Stream Processing is accessible and convenient for Atlas teams, not the deepest streaming engine. (2) Federated query: for large-scale federated analytics across many sources, Trino/Presto and Amazon Athena go deeper than Data Federation. (3) BI: for rich enterprise business intelligence, Tableau and Power BI go far deeper than Atlas Charts. (4) The edge: note the older Realm / Atlas App Services Device Sync mobile-sync product is DEPRECATED (EOL September 2025) — Edge Server is the newer edge story, so don’t plan around mobile sync. So the honest positioning: for a team already standardised on Atlas that wants to stream, federate and visualise WITHOUT running separate systems, Platform Services is a genuinely convenient, simplifying choice; for the deepest capability in any one of those areas, the specialist wins. TechBag scopes it honestly — comparing vs the streaming, query and BI specialists — and licenses and supports it locally with GST.
The numbers behind the platform
What your Atlas Platform Services journey looks like
Scoping (& which services)
Your workload (real-time streams, cross-source query, dashboards, edge/IoT) and whether you’re already on Atlas — the sweet spot. TechBag scopes which services you need and compares honestly vs the streaming, query and BI specialists.
Turn on the services you need
Enable Atlas Stream Processing (streams via the aggregation framework), Data Federation (query Atlas + S3 in one query), Atlas Charts (dashboards) and/or Edge Server — all against your Atlas data. TechBag sizes it.
Stream, federate, visualise
Process event streams from Kafka/change streams, query across hot and cold data with no ETL, tier ageing data with Online Archive, and build dashboards in place — fewer systems, less glue. One platform.
Right-size cost & compare
These services bill as Atlas usage — TechBag scopes exactly what you need, flags where a specialist (Confluent/Kinesis, Trino/Athena, Tableau/Power BI) is worth it, and supports you locally (GST).
Trusted across regulated industries in 100+ countries
The review scoreboard
Modelled on Gartner Peer Insights structure. *Counts and breakdowns are illustrative pending verified review collection.
Quick poll — what’s driving your evaluation?
Talk to an advisor“We already ran everything on Atlas, so processing our event streams with the aggregation framework — no separate Kafka+Flink stack to operate — was a big simplification. Streams in the language we already knew.”
“Data Federation let us query hot Atlas data and cold S3 archive in one query, with no ETL pipeline. We tiered ageing data out to cut cost without losing access.”
“Atlas Charts gave us dashboards directly on our operational data — no export, no BI connector to sync. For internal operational insight it was enough and saved us a system.”
“Edge Server addressed our intermittently-connected IoT sites — a local tier syncing to Atlas. We confirmed with TechBag that the old Realm mobile sync was deprecated, so we planned on Edge Server instead.”
“Honest: for our highest-throughput streaming we still use Confluent — TechBag was candid that Atlas Stream Processing is convenient for Atlas teams but not the deepest engine. We use each where it fits.”
“That MongoDB runs a huge hub in Gurugram gave us confidence — and TechBag scoped which services we needed, compared them honestly vs the specialists, and added INR/GST. Convenient platform, honestly scoped.”
“Reusing aggregation pipelines for stream processing collapsed the learning curve — our MongoDB developers were productive on streams fast, no streaming specialists to hire.”
“These services are billed as Atlas usage and can add up — TechBag scoped exactly what we needed, flagged where a specialist was worth it, and added INR/GST. Consolidation with cost under control.”
Where everyone sits — the grids
Analyst firms bury this view behind paywalls, and G2 retired its Grid. So here’s TechBag’s synthesis of the data-services market — tap any vendor to see why it sits where it does.
TechBag Email-Security Grid
Execution strength vs product vision — the classic market map, minus the paywall.
Data services on Atlas (all-in-one). This page.
Detection × Portfolio Integration
The grid nobody publishes — how strong the email detection is vs how integrated with the wider security portfolio.
Convenience + same-data locality.
Positions are TechBag’s illustrative synthesis of public review-platform data and vendor documentation — not a reproduction of any analyst graphic. Verify before relying on it.
Atlas Platform Services vs the specialists
Confluent/Kafka+Flink, Kinesis, Trino, Athena and Tableau/Power BI — honest lanes; the edge is data locality & simplicity for teams already on Atlas, not out-depthing each specialist. Serious streaming? Confluent. Large federation? Trino/Athena. Rich BI? Tableau/Power BI. We say so.
| Dimension | MongoDB Atlas | Confluent/Kafka+Flink | AWS Kinesis | Trino/Presto | Athena | Tableau/Power BI |
|---|---|---|---|---|---|---|
| Position | Data services on Atlas (all-in-one) | Deep streaming platform | AWS-native streaming | Federated query engine | Serverless query on S3 (AWS) | Enterprise BI / visualisation |
| Streaming depth | Aggregation framework (GA 2024) | Deepest (Kafka + Flink) | Solid, AWS-native | Not streaming | Not streaming | Not streaming |
| Federated / cross-source query | Atlas + object storage | Not its job | Not its job | Deep, many sources | Serverless on S3 | Not its job |
| Visualisation / BI | Atlas Charts (on Atlas data) | Not its job | Not its job | Not its job | Not its job | Deepest enterprise BI |
| Convenience for Atlas teams | Same data, same skills, one platform | Separate stack to run | Separate AWS service | Separate engine to run | AWS-tied | Separate BI tool + sync |
| Best fit | Teams on Atlas wanting fewer systems | Serious, high-throughput streaming | AWS-native streaming | Large-scale federated analytics | Serverless query on S3 (AWS) | Rich enterprise BI |
Which email-security approach fits you?
Honest fit signals — because the fastest way to lose your trust is to pretend one product wins every scenario.
Choose Atlas Platform Services if…
- ✓You’re already on Atlas and want to STREAM, FEDERATE and VISUALISE on the same data — without separate systems
- ✓You want real-time stream processing in the MongoDB AGGREGATION FRAMEWORK your team already knows (GA 2024)
- ✓You want to query across Atlas and S3-style storage in ONE query (Data Federation) and dashboards in place (Charts)
- ✓You want fewer systems and less glue code — with TechBag scoping which services you need & adding GST
Confluent / Kafka + Flink if…
- ✓You need SERIOUS, high-throughput, complex stream processing — the deepest streaming platform
AWS Kinesis if…
- ✓You want AWS-NATIVE streaming, tightly integrated with the rest of your AWS stack
Trino/Presto or Athena if…
- ✓You need large-scale FEDERATED analytics across many sources (Trino/Presto), or serverless query on S3 (Athena)
Tableau / Power BI if…
- ✓You need rich, enterprise BUSINESS INTELLIGENCE and visualisation beyond in-place operational dashboards
What do email threats cost you?
Drag the sliders (data services needed; systems/pipelines to sync; ops hours per month; hour cost as loaded rate). Estimates contrast running separate streaming, federated-query and BI systems (each provisioned, secured, paid for and kept in sync) vs Atlas Platform Services (stream, federate and visualise on the same Atlas data — fewer systems, less glue code) — the wins are systems eliminated, glue-code avoided and ops time saved. Illustrative — remember each service competes with a deeper specialist, so TechBag models the full picture for your workload.
Loaded cost = salary + overheads per productive hour. Illustrative only — your TechBag quote models actual device counts and modules.
Three ways to consume it
Atlas Platform Services bill as Atlas usage — Stream Processing, Data Federation, Charts and Edge Server are metered by use on top of your Atlas clusters. Buy direct or via the AWS/Azure/GCP marketplace to draw down committed cloud spend. Honest: each competes with a deeper specialist, so scope what you need. TechBag scopes it and handles INR/GST (18%) — quote current figures.
Atlas Platform Services (usage-based)
Best for teams already on Atlas
- Stream Processing — real-time streams via the aggregation framework (GA 2024)
- Data Federation — query across Atlas & S3 in one query, no ETL
- Charts (dashboards) & Edge Server (edge/IoT) on the same data
+ Platform add-ons
Best for a broader rollout
- Scoped to your estate
- Add-on modules as needed
- Phased, right-sized deployment
+ scoping & local support
Best value with TechBag
- Scoping + honest Confluent/Kinesis, Trino/Athena, Tableau/Power BI comparison
- Deprecation guidance (Realm/Device Sync EOL Sep 2025 — Edge Server is newer); Gurugram R&D
- TechBag adds INR/GST (18%) invoicing & local support
Buy it for less — TechBag pricing beats list
Whatever the list prices above, TechBag negotiates a significantly better deal — with GST-compliant INR invoicing and local support. Ask us for your discounted quote.
Get an India-ready quote
Tell us your device counts and current tools — we’ll model it against what you spend today.
The 8 questions to ask every vendor
Take this into your next vendor call — including ours.
These services run against your Atlas data — if you’re already standardised on Atlas, they cut systems and glue code. That’s the sweet spot.
Need real-time streams? Atlas Stream Processing (GA 2024) uses the aggregation framework you know. Serious throughput? Confluent/Kafka+Flink or Kinesis go deeper — TechBag advises.
Need to query across Atlas and S3? Data Federation does it in one query, no ETL. Large-scale federation? Trino/Presto or Athena go deeper.
Need dashboards? Atlas Charts builds them in place on your data. Rich enterprise BI? Tableau/Power BI go deeper. TechBag says which fits.
Need the edge? Edge Server is the newer story — note Realm/Device Sync mobile sync is deprecated (EOL Sept 2025), so don’t plan around it.
Each service competes with a deeper specialist — the trade is convenience/locality for depth. TechBag scopes honestly per need.
MongoDB runs one of its largest global hubs in Gurugram (DLF Cyber City) + Bengaluru — genuine India relevance. TechBag supports it locally.
These bill as Atlas usage — TechBag scopes what you need, handles marketplace draw-down, and adds INR/GST (18%) invoicing.
Questions buyers ask
Ready to stream, federate and visualise on Atlas?
Scope Atlas Platform Services (the data services on Atlas — Stream Processing, Data Federation, Charts and Edge Server — stream, federate and visualise on the same data) — and let a TechBag advisor scope which services you need, compare honestly vs Confluent/Kinesis, Trino/Athena and Tableau/Power BI, flag the Realm/Device Sync deprecation, and add INR/GST and local support.
Stats, ratings, review counts and pricing are illustrative and sourced from public materials; verify before purchase.