When the job crosses models.
Consider 8DB when structured records are one part of a wider application. For a SQL-heavy warehouse, compare exact operators, integrations and query coverage, not just one fast count.
Explore the connected workflow ↗Explore the models / 8DB
Tables & records
Familiar rows. More context within reach.
Work with structured records and selected fields alongside documents, relationships and time-based observations.
Filter · sort · project
Model / TablesConceptual diagram
The decision / Specialist context
Keep the row. Bring the rest of the story.
Business records become more useful when a calculation can reach the documents, relationships and observations behind them.
ClickHouse
SQL analytics, compression and real-time data products.
Read the source ↗DuckDB
Embedded analytical queries with a practical data-access ecosystem.
Read the source ↗WHAT THE WORK REQUIRES
Apache Arrow gives analytics a typed interchange model. Uber’s engineering account also shows why schema evolution and consistency become application-level concerns.
Start with the operations
your question needs.
- Typed rows and record identity
- Filtering, sorting and projections
- Point and range reads on supported paths
Documented product scope, reviewed 17 September 2026. The underlying build and deployment must be qualified for your application.
A relational representation is not a claim of full SQL or PostgreSQL compatibility. Check joins, transactions, aggregations, null handling and connectors against the exact supported query surface.
Connected native models
Keep the next question within reach.
The opportunity is to carry row identity into the rest of a mixed-data question. A table can remain a useful projection while the application follows richer context beyond its columns.
Follow the relationship to a better answer.
Connect · traverse · follow
Explore model ↗Keep the source behind the answer.
Structure · search · trace
Explore model ↗Keep the meaning in every observation.
Observe · window · analyze
Explore model ↗These connections show the models involved in the application design. Compare the supported combination and full workflow with a realistic alternative.
Input → usable value / interactive design example
Build an evidence-backed operations report
Ingest operational records and stable identifiers
Rows share a structure. A query selects the fields and records relevant to the question.
Next: Select rows
Conceptual application workflow. Qualify the supported operations, external tools, protection and persistence for the complete path. These diagrams are explanatory, not measured results.
Read the complete workflow and comparison scope
- Ingest operational records and stable identifiers
- Filter the relevant assets and inspection dates
- Resolve relationships and source documents
- Inspect associated readings or spatial context
- Produce a report whose rows lead back to evidence
Measure preparation, indexing, query and result assembly. A fast selected SQL-like operation does not establish faster analytics across every workload.
Compare a realistic alternative.
DuckDB is an embedded analytics alternative with an extension ecosystem. Compare required expressions, formats and deployment alongside the value of connected native representations.
DuckDB extensionsCompare equivalent outputs and required guarantees, including the simplest single-product alternative where one exists. A specialist may remain the better fit when its operators, ecosystem or operational maturity are decisive. Combining native models becomes valuable when your actual question crosses them.
Modality performance / Evidence reviewed 18 September 2026
Analytical work on approximately 100 million rows.
milliseconds · filtered count Lower is better · linear scale from zero
8DB18
May 2026 · specialized server handler
ClickHouse1
September 2026 published reference
DuckDB41
May 2026 published reference
Apache Doris50
May 2026 published reference
StarRocks59
September 2026 published reference
c6a.4xlarge machine class; dates, settings and timing boundaries differ. This selected count is not the full ClickBench suite or evidence of SQL compatibility. ClickHouse is faster in the shown reference.
Read the results, comparison and methods →01Correct query outputs
02Scan / aggregation latency
03Ingest & index readiness
Include memory, storage, energy and hardware writes
Record build-time and serving memory separately, original and derived stored bytes, measured joules per completed job, and physical writes including indexing, compaction and recovery. A smaller representation does not by itself prove lower energy or longer device life.
Match correctness, freshness, durability and protection across alternatives. Encryption, authorization, adapters, network setup and output delivery belong in the complete-job comparison wherever required. No universal zero-overhead claim follows from these results.
Evaluate the result you need
Turn operational records into a report whose exceptions link to evidence.
Illustrative evaluation workflow · qualify the required operations and deployment
- 01Load records
- 02Filter and aggregate
- 03Follow exceptions
- 04Explain the result
Time to a correct report
Include loading, joins, aggregation and delivery.
Freshness under updates
How soon does a committed correction reach the result?
Total working memory
Include scan, join and intermediate buffers.
Bring the workload that matters to you.
Discuss an evaluation ↗We’ll use its output, quality and operating requirements to define a useful comparison.
Comparison scope, sources and next evidence
Extend selected-count evidence to a declared operator suite and complete report; audit import/export, nulls and type semantics.
Match the required correctness, freshness, durability and protection. Include ingestion, transformation, indexing and delivery in a complete-job result. Measure memory, stored bytes, energy and physical writes separately. Vendor descriptions establish offers, not independent rankings or 8DB compatibility.
- ClickHouse Vendor offer
- DuckDB Project documentation
- Apache Arrow columnar format Interchange specification
- Uber Docstore User-organization engineering report
Market review: 20 September 2026 · Reference revision 3.0. Linked benchmark results retain their own dates and scope. Standards are evaluation context, not an 8DB certification.
Choose the path that fits your deployment.
Verify the supported API/SDK, data format, persistence and indexing behaviour for the release you plan to use. Match the operation-by-modality protection profile and device qualification to the workload.
Related articles
Take the next step into the thinking.
8DB analytics | How we compare, and what else you get
Explore the measured path, comparison and conditions.
8DB | Native Multimodal Data: Connect the Dots Across Graphs, Maps and Time Series
Explore the ideas and implementation context behind this capability.
One Sealed Store for Every Shape of Data, and It Works With the Network Gone
Explore the ideas and implementation context behind this capability.
Reference / Reviewed 17 September 2026
Readable, shareable and traceable.
This reference explains 8DB’s public modality description with an explicit workload and selection criteria. It is a documentary review, not fresh execution of every operator or platform.
Public sources: 8DB modality scope; DuckDB extensions; the related articles above. Internal source leads and outstanding release checks remain in the editorial record.
Download this referenceEvaluate the question
your application needs to answer.
Bring the data shapes, input state, expected output and the systems you use today. Define a comparison around that complete job.
Discuss your workload