DuckDB or PostgreSQL: when to run analytics in each
Run heavy reporting in DuckDB when the queries are large analytical scans, the data changes in bulk, and one process or read-only consumers are the only users. Keep analytics in PostgreSQL when many writers must change data at the same time, when several teams need shared live access, or when reports depend on tooling your operations group already runs for the primary database. DuckDB targets analytical (OLAP) workloads, complex long-running queries over large portions of the data, using a columnar-vectorized engine that expects large-scale changes such as bulk appends. PostgreSQL is a row-oriented multi-user server built for many concurrent transactions.
DuckDB fits when the reporting job is a batch that reads a large slice of history, when data arrives as files or periodic loads, and when the output is a snapshot file or a process that only reads. DuckDB runs embedded in a host process, so there is no database server to install, patch or back up as a separate service. Its postgres extension can read PostgreSQL tables directly, which lets a nightly job copy or scan operational data without moving it into a new system first. Confirm the extension's support tier in the DuckDB core extensions documentation before you make it a dependency, since most core extensions carry a best-effort tier.
PostgreSQL stays the right home when analytics must be served to many users at once, when results feed live applications, or when the workload writes continuously alongside transactions. In DuckDB's read-write mode, one process can write a database file, and several processes can only read it in READ_ONLY mode. Writing from several processes needs the Quack remote protocol, which was in beta as of DuckDB v1.5.2, or DuckLake with PostgreSQL as the catalog. Check the current status of those options before you design around them. The decision turns on three questions: how many writers and readers need concurrent access, how much data each query scans and how fast it grows, and where the results must be served. If the answers are one or few writers, large scans, and results delivered as files or to a single consumer, DuckDB is a strong candidate. If they are many users, shared dashboards and live writes, extend PostgreSQL or add a dedicated analytical engine instead.
Which platform fits which reporting scenario
This table maps common reporting scenarios to the platform that fits each one and the condition that decides it. Read each row against your own write pattern, concurrency needs and where the data already lives. The main takeaway is that DuckDB favors large analytical reads and bulk loads from one writing process, while PostgreSQL favors shared, live, concurrent access.
| Scenario | Better fit | Condition that drives the choice |
|---|---|---|
| Nightly aggregation over large Parquet or CSV files | DuckDB | DuckDB reads these files in place through the FROM clause and runs complex queries over large portions of the data with its columnar-vectorized engine. The job should run as a single process with no need for live multi-user writes. |
| Ad hoc analyst exploration | DuckDB | Analysts can run a local CLI or Python session with no server to install or patch. This works best when each analyst works on their own copy or a read-only file, since one writer per database file is the constraint. |
| Dashboards served to many concurrent users | PostgreSQL | A server process handles many simultaneous client connections, while DuckDB is embedded in a host process. If dashboard traffic comes from many users at once, a shared database server is the simpler architecture. |
| Operational reporting on live transactional tables | PostgreSQL | Reports that must reflect the latest committed rows from the application belong where those writes happen. Querying through the postgres extension is possible, but confirm freshness, load on the source and the extension's support tier for your version. |
| Read-only analytics copy | Depends on refresh and access | A read-only DuckDB file can be read by several processes, but none can write. A PostgreSQL hot standby also serves read-only queries and fits teams that already run replication. Confirm which refresh cadence and access pattern you need. |
| Frequent small updates from applications | PostgreSQL | DuckDB targets large-scale changes such as bulk appends, not many small transactions. Row-level transactional workloads belong in the operational database. |
| Multi-writer loads from several services | PostgreSQL | DuckDB's multi-writer support applies within one process. Writes from several processes need the Quack remote protocol (beta as of v1.5.2) or DuckLake with PostgreSQL as the catalog, so confirm maturity before depending on either. |
How each engine is built and deployed
DuckDB and PostgreSQL differ first in where the engine runs and how it stores data, and those choices carry through to patching, backups and availability. The cards below cover the decisions that shape day-to-day operation, so you can check each one against your own environment.
Embedded execution with no server
DuckDB runs inside the host process rather than as a separate service, so there is no database server to install, update or keep running. The database is a file that the application, notebook or script opens directly. That removes a component from the stack, but it also means the operational controls you would normally apply to a server (network listeners, connection limits, central audit of sessions) must come from the host process instead. Check who can open the file and where it is stored.
Client-server multi-user architecture
PostgreSQL runs as a server process that accepts connections from many clients, with authentication, roles and per-connection management built in. Your team already has runbooks for this model. The cost is that the server is a shared dependency that must be patched, monitored and sized for its concurrent load. Confirm which authentication methods your organization requires, since PostgreSQL documents password, GSSAPI, SSPI, LDAP, RADIUS, certificate, PAM and OAuth options.
Columnar-vectorized versus row storage
DuckDB uses a columnar-vectorized engine built for complex queries that scan large portions of the data, and it expects large changes such as bulk appends. PostgreSQL stores and reads rows, which suits transactional work that touches a few records at a time. For reporting, this means an aggregation over one column can avoid reading unrelated columns in DuckDB. Test your heaviest reports on representative data rather than assuming the faster path for every query.
Read-write versus READ_ONLY process model
In read-write mode one process can read and write a DuckDB file, and multiple writer threads inside that process can update separate tables or separate subsets of a table. In READ_ONLY mode several processes can read the file, but none can write. Writing from several processes requires the Quack remote protocol, which was in beta as of DuckDB v1.5.2, or DuckLake with PostgreSQL as the catalog. Plan the write path before you adopt a shared file.
Patching, backups and minor upgrades
Patching a DuckDB deployment means updating the library in each host application and testing that application, which fits the release cadence of your code rather than a database calendar. PostgreSQL minor releases do not change the storage format, so updating means replacing executables and restarting. Backup designs differ too: a DuckDB file is backed up with its host, while PostgreSQL offers continuous archiving and point-in-time recovery. Confirm your recovery objectives before choosing.
High availability and automatic failover
PostgreSQL supports hot standby servers that answer read-only queries, along with synchronous replication, where a commit waits for standbys, and asynchronous replication. It does not detect a primary failure or promote a standby on its own, so automatic failover requires external tooling that you must select, test and operate. DuckDB has no equivalent because it is not a network service. If an analytical workload must survive a host failure, plan that recovery at the application or storage layer.
Capabilities that decide the choice
This table compares the capabilities that most often settle the decision between DuckDB and PostgreSQL for analytics. Read each row as a question about your workload: if the answer favors the column you need, that capability is a strong signal, and the rows on writers and authentication often matter most for shared reporting.
| Capability | DuckDB | PostgreSQL |
|---|---|---|
| Execution model | Columnar-vectorized engine built for analytical queries over large portions of the data, with bulk appends as the expected write pattern | Row-oriented storage suited to transactional access; analytical scans run on the same server that serves applications |
| Deployment and server | Embedded in a host process, so there is no database server to install, update or maintain | Separate multi-user server process that you install, patch, back up and operate |
| Concurrency and writers | In read-write mode one process writes a database file; multiple writer threads work within one process, and conflicts arise only when two threads change the same row. Multi-process writes need the Quack remote protocol (beta as of v1.5.2) or DuckLake with PostgreSQL as the catalog | Multiple client connections read and write concurrently through the server, which is its native model for shared access |
| File and table formats | Queries CSV and Parquet by naming files in the FROM clause; iceberg, delta and ducklake table format extensions are available, mostly at best-effort support | Queries its own tables; reading external files requires an extension or an export/load step, which you should confirm for your data |
| Client languages | Standalone CLI plus clients for Python, R, Java, Wasm and other languages; DuckDB-Wasm runs in browsers | Wide client library support through standard drivers; confirm the drivers your reporting tools need |
| postgres, mysql and sqlite extensions | Read and write those databases from DuckDB; the postgres extension is a core extension at the secondary (best-effort) support tier, so confirm fit before production use | Not applicable as an extension to query other engines; cross-database reads depend on foreign data wrappers or external tools you would need to evaluate |
| Authentication methods | Not applicable; access control comes from the host process and the file system around the database | Documents password, GSSAPI, SSPI, LDAP, RADIUS, certificate, PAM and OAuth; SCRAM is the password default after MD5 was deprecated in PostgreSQL 18 |
| Replication and standbys | Not applicable as a server feature; durability and copies are handled by your file and backup practices | Hot standbys serve read-only queries, with synchronous or asynchronous replication; automatic failover requires external tools |
| License | MIT License, with intellectual property held by the DuckDB Foundation | PostgreSQL License, a liberal license similar to BSD or MIT |
License terms and what each platform costs to license
DuckDB is released under the MIT License, a permissive open-source license. The project's intellectual property is held by the DuckDB Foundation, so the license terms and the stewarding organization are both public. The facts supplied for this comparison do not name a paid edition of DuckDB, so you pay no license fee to use the core engine in production. Your license review should focus on the extensions you plan to load, because not every extension is maintained by the core project.
- MIT terms: You may use, modify and redistribute the engine under the MIT License, subject to its notice requirement.
- Foundation stewardship: The DuckDB Foundation holds the intellectual property, which is relevant if your procurement policy asks who owns the project.
- No named paid edition: The supplied facts name no commercial tier, so there is no edition to buy for the engine itself.
- Third-party extensions: The motherduck extension is maintained by a third party that connects DuckDB to the MotherDuck service, so its terms and support come from that party.
Confirm the license of each extension and any hosted service you add to DuckDB directly with its maintainer, and record the result in your third-party software inventory.
PostgreSQL is released under the PostgreSQL License, a liberal open-source license similar to the BSD or MIT licenses. It places few restrictions on use, modification or redistribution, including in commercial products. There is no paid PostgreSQL edition named in the supplied facts, so the server itself carries no license cost. Commercial value appears in the services around it, such as managed hosting or vendor support, which are separate contracts with separate prices.
- Permissive terms: The license resembles BSD or MIT, so redistribution and embedding carry few obligations.
- Server carries no fee: Running PostgreSQL in your own infrastructure requires no license purchase.
- Extensions have their own terms: Add-on extensions are licensed separately, so check each one before it reaches production.
- Support is contracted separately: Any vendor support or managed service is a service purchase, not a license.
Ask each support or hosting provider to state in writing what is included, what is excluded and how fees change with size and region.
Because neither platform charges a license fee, the cost comparison depends on the work each one creates. DuckDB runs inside a host process, so there is no database server to install, update or maintain as a separate service. PostgreSQL is a server that your team installs, patches, backs up, replicates and upgrades, and the supplied facts describe several of those tasks, such as major upgrades and failover tooling. The recurring cost of PostgreSQL is largely staff time and infrastructure, while DuckDB shifts cost toward application code and data file management.
- One-time costs: Design, schema work, migration and initial integration, which are paid to staff or a services firm.
- Recurring costs: Patching, backup verification, monitoring, on-call coverage and infrastructure.
- Cost drivers: Number of concurrent writers, data volume, uptime requirements and the number of teams sharing the store.
- Quote request: Ask for the scope of operations, the named deliverables, and the response expectations in writing.
When you request a quote, ask the provider to separate one-time implementation from recurring operations so you can compare the two platforms on equal terms.
The skills each platform needs differ, and skills are where most of the cost hides. A team that already operates PostgreSQL can absorb routine tasks at lower marginal effort, while a team new to analytical engines will need time to learn columnar execution, file layout and extension management. Support contracts are optional for both platforms, so their presence should follow from the risk you cannot absorb internally, such as an outage during month-end reporting.
- Existing PostgreSQL skills: If your database team already runs PostgreSQL, the operating load of a second server may be small, but confirm it against your own environment.
- DuckDB learning curve: Application developers and analysts will need to understand the embedded model and its single-writer rules.
- Optional vendor support: Purchase support only for components where an outage or security gap would be costly to resolve internally.
- Staff continuity: Consider how many people can operate each platform if one leaves.
Before signing any support agreement, confirm response times, covered versions and the process for security fixes.
Licensing terms rarely decide this comparison, since both platforms are free to license. Your checks should focus on the components you will actually deploy and the contracts you will sign around them. Confirm the terms of every extension and hosted service in the design, record the owner of each license obligation, and make sure the budget reflects operations rather than license lines.
- Extension inventory: List every DuckDB and PostgreSQL extension you plan to use and confirm its license with its maintainer.
- Third-party services: Confirm the terms of the motherduck extension and any MotherDuck service with that third party before relying on them.
- Support scope: Ask any provider which versions, components and hours are covered.
- Operations estimate: Ask your team or your implementation partner to estimate staff time for patching, backups and upgrades in your own environment.
Record these answers in your architecture decision record so the licensing and operating assumptions remain visible at the next review.
How DuckDB's postgres extension queries PostgreSQL tables directly
The postgres extension lets a DuckDB session attach a PostgreSQL database, read its tables and, if you allow it, write to them. The practical gain is that an analyst can join a live operational table with a Parquet or CSV file in one query, without first exporting the table. That matters most for occasional, heavy reads, such as a month-end reconciliation that scans a large orders table once. Skipping the export also removes a copy of the data to govern and a scheduled job to maintain. Check the DuckDB core extensions documentation before you commit, because the postgres extension sits in the secondary, best-effort support tier. Test it as a convenience for specific reports, and keep a documented export path as the fallback.
The first risk is load on the production database. A wide scan from DuckDB competes with transactions for CPU, I/O and connections, and a long read can hold back vacuum cleanup and add replication lag. Before you attach analysts to a primary, ask your DBA how the extension sends work to the server and what they will monitor during a long query. Where the data allows, point the attach at a hot standby, which PostgreSQL supports for read-only queries, and confirm that the standby's lag is acceptable for the report. Also confirm the network path: keep traffic on private links, enforce TLS on the connection, and limit which hosts, including analyst laptops, can reach the database port.
Credentials need the same care as any service account. Create a dedicated read-only PostgreSQL role for DuckDB, grant access only to the schemas and tables the report needs, and keep its password out of notebooks, scripts and shared project files. Source it from your secrets manager and rotate it under your existing policy. Then test performance against the source using a representative query in a production-like window, and compare the result with the full export you are replacing. Record the query, the window and the server-side load your DBA observed. The PostgreSQL authentication methods page lists what your server accepts, so confirm the method with your DBA before you write the connection details.
What drives running cost for each engine
Running cost for DuckDB and PostgreSQL comes from different places. DuckDB shifts spend toward compute sizing and file and pipeline management, while PostgreSQL shifts it toward availability, upgrades and the people who run them. The cards below separate those components so you can ask vendors, partners and your own platform team for the right numbers.
Compute and hosting footprint
DuckDB runs inside a host process, so its main infrastructure cost is one large server or cloud VM sized for the heaviest query and the memory it needs. A PostgreSQL deployment for the same reporting load usually means a primary, one or more replicas for read-only queries, and storage for each. Ask for the node count, instance sizes and storage per option in any quote, and confirm whether replicas are needed for your reporting concurrency.
Skills and on-call coverage
A PostgreSQL cluster needs staff who can tune replication, backups and failover, and who can answer a page at night. DuckDB has no server to patch or monitor, so its operating load falls mostly on the team that writes queries and pipelines. Count the hours your own engineers spend on each option, and ask any implementation partner to state who carries on-call responsibility after handover.
PostgreSQL support windows
PostgreSQL 14 is supported until November 12, 2026, PostgreSQL 15 until November 11, 2027, PostgreSQL 16 until November 9, 2028, PostgreSQL 17 until November 8, 2029, and PostgreSQL 18 until November 14, 2030. A cluster on a version near its end date forces an upgrade within your budget cycle. Plan for that cost now, and confirm which major version your existing cluster runs.
Upgrade effort and minor releases
Minor PostgreSQL releases do not change the storage format, so applying them means replacing executables and restarting, which is routine work. Major upgrades use pg_upgrade, a dump and restore, or logical replication, and each needs rehearsal against a copy of production. Logical replication can reduce downtime to several seconds, but it adds monitoring and cutover steps. Ask for the upgrade method and rehearsal time included in any scope.
Failover and connection pooling tools
PostgreSQL does not detect a failed primary or promote a standby on its own, so automatic failover needs an external tool that you must license or build, install, test and monitor. Connection pooling with PgBouncer is a separate component when many reporting clients connect at once. DuckDB avoids both components for single-team analytics, but it is not a replacement for a high-availability transactional database. Ask whether failover tooling and pooling are in scope and who tests them.
DuckDB pipeline code and file management
The DuckDB cost sits in the code and files around it: extract jobs that land CSV or Parquet files, naming and retention rules, schema changes, and checks that a database file or concurrent writer set is not corrupted. Concurrent writes from several processes need the Quack remote protocol (beta as of DuckDB v1.5.2) or DuckLake with PostgreSQL as the catalog, which adds components back. Ask what file layout, write pattern and backup method your quote assumes.
Moving analytics workloads from PostgreSQL to DuckDB
This sequence moves reporting reads off the PostgreSQL primary and into DuckDB, with each step producing a check that the numbers still match. Teams finish with analytics running in a separate process, and the operational database serving only the work it was built for.
Inventory reports and the tables they read
The data or platform owner lists every report, dashboard, scheduled export and ad hoc query that touches the PostgreSQL primary, along with the tables, views and columns each one reads and how often it runs. Include the BI tool connections and any service accounts behind them, since these often hide reads nobody remembers. The output is a written inventory that marks each workload as a candidate for DuckDB, a candidate to stay in PostgreSQL, or a candidate for redesign. Confirm the list with report owners before moving anything.
Choose how data reaches DuckDB
For each candidate workload, the engineer decides between two routes. One exports tables to Parquet files that DuckDB reads by name in the FROM clause, which suits bulk snapshots and reproducible extracts. The other uses DuckDB's postgres extension to query PostgreSQL tables directly, which suits ad hoc work where a copy would go stale. The decision depends on freshness needs, data volume and whether the export job would itself load the primary. The output is a documented route per table, approved by the platform owner, with the extension version pinned in the environment.
Load the data and set up the DuckDB environment
The engineer builds the export pipeline or configures the postgres extension, creates the DuckDB database file or runs the process in the host application that will own it, and confirms that the process runs on the target operating system and CPU. Because DuckDB is embedded, there is no server to install, so the work sits in the host process, its file location, its backups and its permissions. Note that concurrent writers need the Quack remote protocol or DuckLake, so keep a single writing process unless one of those is in scope. The output is a loaded environment with row counts matching the source for each exported table.
Rebuild queries and dashboards against DuckDB
Analysts or BI developers port each query in the inventory. Standard SQL usually carries over, but functions, type handling, date logic and vendor-specific syntax should be reviewed line by line rather than assumed to match. Dashboards need new data source definitions, and any row-level or column-level permissions must be re-expressed in the new layer, because they do not move automatically. Confirm which BI tool supports DuckDB as a connection before committing to a rebuild. The output is a ported query set in version control, with a list of anything that could not be ported and why.
Validate results against PostgreSQL
The engineer runs each ported report against both engines over the same data snapshot and compares totals, row counts, distinct counts, null handling and rounding on sample and full data. Differences in ordering, timezone handling and numeric precision are the usual causes of mismatch, so check them explicitly. Report owners sign off on the figures they rely on. The output is a validation log showing which reports match, which differ and the reason for each difference, and no report goes live until its owner has accepted the result.
Move scheduled jobs and access controls
Scheduled exports, refresh jobs and reporting batches are moved from the old scheduler or cron entries to the new environment, with logs, failure alerts and retry behavior defined in the new location. Roles and connection credentials are created for the DuckDB-side processes, using least privilege on the PostgreSQL side for any role the postgres extension uses. Reports that previously relied on PostgreSQL roles must have an equivalent access decision recorded. The output is a schedule register, a credential inventory and a test run of each job that completes without manual intervention.
Retire or narrow the analytical load on the primary
Once validation and scheduling are complete, the team removes the migrated reads from the PostgreSQL primary, retires unused replicas or views, and reduces the connection limits or pooling settings that existed only for reporting. Where a standby already serves read-only queries, decide whether it still has a role. Keep the transactional workload on PostgreSQL, and record the rollback path for each retired report for a defined period agreed with the business owner. The output is a before-and-after view of the primary's reporting load and a documented rollback procedure.
What implementing each involves and where projects run long
A DuckDB analytics layer is mostly a file and process design problem, and the first decision is who writes the database file and who reads it. DuckDB runs inside a host process, so the team designs a pipeline that loads data in bulk, a file layout that separates raw inputs from published database files, and an access model for those files. Because one read-write process can hold a database file, the design must state which job writes and which consumers only read. Backup then means copying or snapshotting the database file on a schedule the team has tested with a restore, since no server handles it for you. Confirm the current concurrency options in the DuckDB concurrency documentation before you commit to a multi-writer pattern.
PostgreSQL reporting work is mostly a role, replica and tuning problem on a system that already has owners. The team defines roles that separate reporting reads from application writes, decides whether reports run on a hot standby (which serves read-only queries) or on the primary, and sets connection limits so reporting sessions do not starve the application. Index and query tuning then happens against real report workloads, and a connection pooler such as PgBouncer may be needed to manage many short sessions. Replicas add lag, so the team must agree on how stale a report may be.
Projects usually run long in three places. The first is undefined concurrency needs: if nobody has written down how many writers, readers and report refreshes must run at the same time, a DuckDB design gets reworked after the first collision, and a PostgreSQL design gets re-tuned after the first slow report. The second is unclear data ownership, meaning no one can say who signs off on a table's definition, its retention or its restore point. The third is failover design. PostgreSQL does not detect a primary failure or promote a standby by itself, so automatic failover needs external tooling that someone must choose, configure, test and operate. Ask for each of these to be settled in writing before the scope is fixed.
Questions teams ask before choosing or hiring help
Can DuckDB replace a PostgreSQL reporting replica?
Sometimes, for read-heavy analytics that run as batch jobs or scheduled reports over data you can export or attach. DuckDB is an embedded analytical engine with no server to run, so it fits when reports are generated by one process at a time. It does not replace a replica that many dashboards query concurrently, serve as a failover target, or feed applications with live reads. Check your concurrency pattern, how fresh the data must be, and whether anyone needs failover before you decide. A replica from PostgreSQL remains the stronger choice for those needs, because PostgreSQL supports hot standby servers serving read-only queries and both synchronous and asynchronous replication.
How many writers does DuckDB support in one process and across processes?
Within one process, DuckDB supports multiple writer threads, which can update separate tables or separate subsets of the same table. A write fails with a conflict error only when two threads change the same row at the same time. Across processes, the rule is stricter: in read-write mode, one process can read and write a database file, while READ_ONLY mode lets several processes read but none write. Writing from several processes requires the Quack remote protocol, which was in beta as of DuckDB v1.5.2, or DuckLake with PostgreSQL as the catalog database. Confirm the current status of these options against the DuckDB concurrency documentation before you design around them.
Is the DuckDB postgres extension production-ready?
Treat it as a tool to validate in your own environment rather than assume it is production-ready by default. The postgres extension reads and writes PostgreSQL databases from DuckDB, but DuckDB places most core extensions at a secondary, best-effort support tier. Before relying on it, test the query patterns you need, the data types you use, read performance against your replica, and the credentials and network paths. Confirm the support tier and known limitations for your DuckDB version with the DuckDB core extensions documentation. A pilot against a copy of your data will answer more than a general rating.
What effort does a migration need and what does it require from the client team?
Effort depends on what moves. Moving a few reporting queries to read from PostgreSQL through DuckDB is a small change. Moving pipelines, scheduling, access control and data lineage is a larger job. The client team usually needs to name the reports in scope, supply sample data and schema definitions, confirm which system is the source of truth, approve access and network changes, and test outputs against current results. Ask a provider to list these inputs in the written scope so you can see what your own staff must supply and when.
Can our team do this migration itself?
Often yes, if you have engineers who already write SQL, manage scheduled jobs and understand the reporting queries. The work is mostly query translation, testing and operational changes rather than new infrastructure, because DuckDB has no server to install. The harder parts are concurrency decisions, access control design and supporting the setup after handover. Assess your team against those tasks honestly. If the team lacks time or experience with backups, upgrades or security review, a fixed-scope engagement can cover those pieces with clear deliverables.
What drives the cost of a fixed-scope engagement?
The written scope and quote set the cost. The main drivers are the number of reports and pipelines in scope, the complexity of the queries and data types, how many source systems and access paths must be connected, the testing and validation required before cutover, and the documentation and handover your team needs. Recurring costs depend on whether you want ongoing support, upgrades or continuous testing, which are available as retainers. Ask for the deliverables, assumptions, client inputs and exclusions listed separately, so you can compare quotes on the same basis.
Which parts of the PostgreSQL side stay the same after adding DuckDB?
PostgreSQL remains responsible for the application database, its authentication, backups, replication and major version upgrades. Those duties do not disappear because analytics moves elsewhere. PostgreSQL 18 is supported until November 14, 2030, and its upgrade options include pg_upgrade and logical replication, with logical replication allowing major upgrades with only several seconds of downtime. Confirm with your own DBA which PostgreSQL version you run and what its support window means for your plan.
How DeployOpen helps choose, implement or migrate
DeployOpen is a Chainscore Labs company that plans, implements, hardens, backs up and documents self-hosted open-source platforms inside your own infrastructure. The team helps you weigh DuckDB and PostgreSQL on the same evidence, and every engagement starts from a written scope with a quote that follows from it.
Workload and concurrency assessment
We map which queries, writers and readers your reporting stack actually has, then test them against DuckDB's single read-write process model and PostgreSQL's multi-user server. The output is a written recommendation covering where analytics should run, who writes to each store and which concurrency patterns need a proof-of-concept before you commit. Ask for the workload inventory to be part of the written scope.
PostgreSQL operations and upgrade planning
If PostgreSQL stays in your stack, we plan the operating work it brings: supported major versions, minor release cadence, upgrade method and replication design. Failover needs external tooling, so we document which components own detection and promotion and how backups and point-in-time recovery are tested. Confirm the target major version and its support end date with your own team before scoping.
Extension and federation design
Where DuckDB reads PostgreSQL tables through its postgres extension, we design the access path, credentials and read patterns, and we verify which core extensions are supported at the tier your policy requires. We also confirm the team's write path so analytical copies do not drift from the operational source. Ask for a test against your real table sizes before the design is approved.
Identity, access and hardening
We configure authentication for the chosen platform, such as SCRAM password policy or certificate and directory-based methods, and we restrict who can attach databases or write to analytical files. For DuckDB, file permissions and host process access become the control points. Each control is recorded in the implementation documentation so your auditors can trace it to an owner.
Backup, recovery and evidence
Each platform recovers differently. PostgreSQL uses base backups, archiving and point-in-time recovery, while a DuckDB database is a file that must be copied consistently while no writer is active. We build and test the restore procedure, then document the recovery objectives agreed with you. DeployOpen prepares this evidence for your assessors, but does not issue audit reports or certifications.
Fixed-scope delivery and retainers
Most engagements are fixed-scope, with deliverables and timelines agreed in writing before work starts. Ongoing support, upgrades and continuous testing run under retainers. Your quote follows from that written scope, so ask each bidder to list the platforms, environments, data volumes and handover documents they will deliver. DeployOpen does not publish a price list.
