Database
MongoDB database services
MongoDB stores each record as a document, with the details that belong together kept together. That suits data that varies in shape, or changes as a product evolves. It also demands good modeling, or flexibility turns into confusion. We help get that right.
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- Building since 2010
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- 50+ technologies
At a glance
What it is
MongoDB is a document database that stores each record as a flexible, self-contained document, which suits data whose shape varies or changes often.
Problems it solves
Data that varies from record to record is awkward to force into fixed tables, but unstructured storage without design becomes a mess.
Who it suits
Teams building products with varied or fast-changing data, and owners of MongoDB systems that have become slow or inconsistent.
How to choose
Choose MongoDB when records are naturally self-contained and vary in shape. Choose a relational database when data is highly connected and rules matter most.
What it means
Records as documents
MongoDB is a document database. Instead of rows in tables, it stores documents, similar to JSON, which can contain nested details and lists. Documents in the same collection can have different fields.
That fits data like product catalogs with different attributes per category, content with varied structure, or event records. A whole record can be read or written in one step.
The flexibility has a price. Relationships between records are looser than in a relational database, so consistency has to be designed for deliberately.
Choosing the right approach
Flexible is not the same as unplanned
The most common problem with MongoDB is a collection where every document has drifted into a slightly different shape, and no code can rely on any field. The flexibility was used as a reason to skip design.
We agree the shape of each kind of document, validate it in the database, and plan how it changes over time, so flexibility stays a feature.
Alternatives
MongoDB compared with other databases
MongoDB
This pageSuits
Varied documents, fast-changing structure, and read-whole-record patterns.
- Trade-off
- Looser relationships, so consistency needs design.
Suits
Connected business data with strict rules, plus flexible columns when needed.
- Trade-off
- Less natural for very large, constantly changing documents.
Firestore
Suits
Apps needing live updates with little server work.
- Trade-off
- Tied to Google's platform, with its own limits and costs.
DynamoDB
Suits
Very high scale with simple access patterns on AWS.
- Trade-off
- Needs access patterns known in advance.
Where it fits
Is MongoDB the right database?
The question is about the shape of the data and what matters most.
What describes your data best?
Choose this when
Catalogs, content, profiles, or events where records differ and are mostly read as a whole.
- What you get
- MongoDB fits naturally, and development is quick.
- Keep in mind
- Model for how the data is read, and keep documents to a sensible size.
Choose this when
Orders, payments, and records that must always agree.
- What you get
- A relational database such as PostgreSQL, which enforces relationships and rules.
- Keep in mind
- Using a document database here shifts the burden of consistency to your code.
Choose this when
Core business records plus some flexible content.
- What you get
- Often one relational database with document columns, or two databases with clear roles.
- Keep in mind
- Two databases mean two things to run. Keep the split simple.
Typical uses
Systems that suit MongoDB
A catalog with varied product types
Each category carries its own attributes, and a product page needs one read to show everything.
Content and profile stores
Articles, user profiles, and settings that differ from one record to the next.
Event and activity records
Logs of what happened, written quickly and read in bulk later.
A product in early development
Data shapes still changing week by week, where rigid tables would slow the team down.
What DECHY can do
Working with MongoDB
Model
- Document designs built around how data is read.
- Rules for what goes inside a document and what stays outside.
Tune
- Indexes and query reviews.
- Fixes for oversized documents and slow aggregations.
Run
- Managed clusters and self-run set-ups.
- Backups, restore tests, and monitoring.
Move
- Migrations to and from relational databases.
- Careful cleaning of inconsistent data.
Common questions
Questions about MongoDB
Is MongoDB faster than a relational database?
For reading whole documents it can be, and for some workloads it scales well. For connected data with rules, a relational database is often as fast and safer.
Does MongoDB support transactions?
Yes, across multiple documents. They exist, but heavy use of them is a sign that a relational database may suit the data better.
Can we move from MongoDB to PostgreSQL or the reverse?
Yes. We plan the mapping, test with real data, and cut over with a way back.
Is our data safe?
Safety depends on configuration. We set up authentication, network limits, encryption, and backups, and test recovery.
Have a different question?
Tell us what you are weighing up and we will answer it plainly.
Ask a questionAlready a client? Email support@dechytech.com
Technical depth
How we build with MongoDB
The detail a technical reviewer will ask about: modeling, performance, and operations.
We embed data that is read together and reference data that is shared or grows without bound, keep documents well under size limits, and add schema validation rules.
- Indexes chosen from real query patterns, including compound and partial indexes.
- Aggregation pipelines reviewed for memory use.
- Replica sets for availability, and sharding only when data volume demands it.
- A managed service in most cases, with encryption, access control, and tested point-in-time recovery.
- Change tracking and migrations for evolving document shapes.
Describe your records
Describe the records and how the application reads them. We will say whether MongoDB suits them and how to model it.