DynamoDB vs Google Cloud Bigtable

DynamoDB and Google Cloud Bigtable are both distributed NoSQL databases that partition data by key, but they target different shapes of workload. DynamoDB is a serverless AWS key-value and document store built for operational (OLTP-style) access. Bigtable is a GCP wide-column store designed to scale into the petabyte range for analytical, time-series, and high-throughput ingest workloads.

Should you use DynamoDB or Bigtable?

Choose DynamoDB if you are on AWS and want a serverless, pay-per-request key-value/document database for operational access patterns with no cluster to run. Choose Google Cloud Bigtable if you are on GCP with very high-throughput or petabyte-scale time-series, IoT, or analytical data, want HBase/Cassandra API compatibility, and can provision cluster nodes. Cloud and workload shape usually decide it.

DynamoDB vs Bigtable at a glance

Characteristic	DynamoDB	Google Cloud Bigtable
Data model	NoSQL key-value and document; typed items up to 400 KB in tables	Wide-column store; a sparse, sorted map keyed by row key, with column families and cells (can be very wide)
Query language / API	Native API (`GetItem`, `Query`, `Scan`) plus PartiQL, a SQL-compatible language	Bigtable API (row-key reads and scans); HBase and Cassandra API compatibility; SQL support for queries
Keys / access	Partition key with optional sort key; access designed around known patterns	Single row key that determines sort order; range scans over the sorted key space
Secondary indexes	Global secondary indexes (GSI) and local secondary indexes (LSI)	No secondary indexes; you design the row key (and can maintain your own index tables)
Consistency	Eventually consistent by default; strongly consistent reads available per request	Strong consistency on a single-cluster instance; multi-cluster instances default to eventual (configurable)
Scaling model	Automatic partitioning; serverless on-demand or provisioned capacity	Provisioned cluster nodes (with autoscaling); scales to petabytes across many machines
Transactions	ACID transactions across multiple items within a Region	Single-row atomic read-modify-write; no general multi-row ACID transactions
Pricing / ops model	Pay-per-request (on-demand) or provisioned capacity plus storage; serverless, AWS-only	Billed by provisioned node-hours plus SSD/HDD storage (and network); node-based, GCP-only
Best-fit workloads	Operational apps with predictable key access needing consistent low latency	Petabyte-scale time-series, IoT, analytics, and high-throughput ingest with range scans

When DynamoDB is the better choice

You are on AWS and want zero operations. DynamoDB is serverless — no cluster nodes to size or scale. On-demand capacity bills per request and scales to traffic automatically.
Your access is operational and key-based. Get-by-id, query-a-partition, and filter-within-a-partition map naturally to DynamoDB, and secondary indexes let you query on alternate keys without maintaining separate tables.
You want per-request strong consistency. DynamoDB offers strongly consistent reads on demand without configuring cluster topology.
You need native AWS integration. IAM, Lambda, and Streams reduce glue code.

When Bigtable is the better choice

You are on GCP with petabyte-scale data. Bigtable is built to scale across hundreds or thousands of machines into the petabyte range, adding nodes for more throughput.
Your workload is time-series, IoT, or analytical. A single sorted row key plus wide rows suits time-ordered data and large range scans, and it feeds the Hadoop/Spark/Beam ecosystem.
You want HBase or Cassandra API compatibility. Bigtable supports the open HBase API standard and a Cassandra API, easing migration from those systems.
You need very high sustained write throughput with predictable, provisioned node capacity.

Working with DynamoDB

If DynamoDB fits your operational workload, DynoTable is a native desktop client for it on macOS, Windows, and Linux. It reads your standard AWS credential chain, so your data stays in DynamoDB with nothing to migrate. It browses and inline-edits items, builds key conditions and filters visually, and adds a SQL Workbench that expresses relational-shaped queries within DynamoDB's access-pattern rules, plus an AI assistant on your own AWS Bedrock credentials.

The free DynamoDB Expression Builder generates key-condition, filter, and update expressions in SDK, CLI, and PartiQL form without an install. DynoTable is a closed-source commercial app; this page describes what it does, not how it is built.

FAQ

Is DynamoDB based on Bigtable?

No. They are independent products from AWS and Google. Bigtable's 2006 design influenced the wider NoSQL field (including Cassandra), and DynamoDB traces to Amazon's 2007 Dynamo work, but they share design lineage in the space, not a codebase.

Does Bigtable have secondary indexes like DynamoDB?

No. Bigtable has a single row key and no secondary indexes — you design the row key for your read pattern and, if needed, maintain your own index tables. DynamoDB provides global and local secondary indexes that let you query on an alternate key directly.

DynamoDB or Bigtable for time-series data?

Bigtable is purpose-built for large-scale time-series: a sorted row key over time ranges plus wide rows suits range scans and petabyte volumes. DynamoDB can handle time-series too — commonly with a composite sort key and time-bucketed partitions — and is the simpler choice on AWS at moderate scale, but Bigtable is designed for the very high-volume analytical end.

Learn how DynamoDB partition keys work and sort key strategies for time-ordered data.
Understand read modes in the DynamoDB consistency guide and when to use DynamoDB.
Build queries fast with the free DynamoDB Expression Builder.
Download DynoTable to browse, query, and edit your DynamoDB tables.

Last verified 2026-07-12 against the official AWS DynamoDB Developer Guide and Google Cloud Bigtable documentation. Google Cloud Bigtable is a trademark of Google LLC; referenced here for identification only.