Insights, tutorials, and best practices for DynamoDB and serverless development
DynamoDB scales to millions of requests with no DBA, but the 400KB item cap and rigid access patterns bite. Ten upsides, ten tradeoffs, and when to walk.
DynamoDB scales to millions of requests with no DBA, but the 400KB item cap and rigid access patterns bite. Ten upsides, ten tradeoffs, and when to walk.
The scalar, document, set and binary types DynamoDB supports, how each counts against the 400KB item limit, and the empty-value rules that break writes.
DAX is a write-through cache that drops DynamoDB reads from milliseconds to microseconds. How the item and query caches work, what it costs, when to skip.
Items cap at 400KB, transactions are ACID, and there is no open-source build. Short answers on limits, consistency, indexes, scaling and DynamoDB billing.
A GSI gives your table a second partition key so you stop scanning. Projections, eventual consistency, capacity planning, and sparse and inverted indexes.
Thirty questions interviewers actually ask, answered: partition keys, single-table design, capacity units, adaptive capacity, Streams, DAX and GSIs.
The partition key decides where an item lives, the sort key decides its order within. How to choose both, and the hot-partition mistakes to avoid.
Streams records a before and after image of every write, kept for 24 hours. How shards and Lambda triggers work, plus replication and aggregation patterns.
Build a DynamoDB table from scratch in Node.js: keys, CRUD, Query versus Scan, and a GSI. Runs locally or on the free tier, with no prior NoSQL experience.
PITR restores a table to any second in the last 35 days. How to switch it on, how a restore really runs, what it costs, and where on-demand backups win.
Cosmos DB sells multi-model APIs and tunable consistency. DynamoDB sells simplicity and AWS-native scale. Compared on pricing, APIs and latency.
Where a key-value store beats a relational one and where it loses: data modelling, joins, scaling limits, and what each really costs at your volume.
A relational schema models relationships; DynamoDB models access patterns you must know up front. How queries, joins, transactions and pricing differ.
DynamoDB is AWS's serverless key-value store: single-digit millisecond reads at any scale. How partitions and keys work, what it costs, when to avoid it.
Ten rules that decide whether DynamoDB runs fast and cheap or slow and expensive: access-pattern design, key cardinality, index restraint, TTL.
An LSI shares the partition key and must exist from table creation. A GSI can be added later, at its own cost. Which to reach for, and what to project.
Work out what a table will actually cost. On-demand versus provisioned compared, an interactive calculator, and where the bill usually leaks.
PutItem replaces the entire item. UpdateItem changes only the attributes you name. When each is correct, and the silent data loss PutItem can cause.
Working Query examples for the AWS CLI, Boto3 and the Node SDK: sort key conditions, GSI queries, pagination, projections and fixes for empty result sets.
Get DynamoDB Local running with one docker command, keep data across restarts, and connect from the CLI, Boto3, Node or Java. Plus what Local cannot do.
Streams captures table changes for 24 hours at no extra charge; Kinesis takes any source and holds it up to a year. Retention, scaling and cost compared.
These solve different problems. DynamoDB serves single-item reads in milliseconds, BigQuery scans terabytes for analytics. When you need each, or both.
The real choice here is operational, not technical. AWS runs DynamoDB for you, you run Cassandra yourself. What that costs in modelling and in staff.
DynamoDB is serverless with a proprietary API. DocumentDB is cluster-based and MongoDB-compatible. Which one your queries and your budget point to.
Firestore gives you client-facing real-time listeners and offline sync. DynamoDB gives you unlimited scale and 400KB items. Which trade-off fits you.
Query flexibility against operational simplicity. How the two data models differ, what each costs to scale, and which workloads clearly favour one.
Redis answers in microseconds from memory, DynamoDB in milliseconds from durable SSD. Where each fits, what each costs, and why many teams run both.
NoSQL Workbench is free but frustrating. Here's why I paid for Dynomate and never looked back.
25GB of storage plus 25 read and 25 write capacity units, free every month. The exact limits, the charges that catch people out, and how to stay inside.
A practical comparison of DynamoDB GUI tools, based on real AWS work. Skip the marketing and see which ones genuinely improve your workflow.
DynamoDB serves single-item lookups in milliseconds; Redshift scans petabytes with SQL. Pick by workload. Architecture, queries and cost compared.
S3 holds unstructured objects at around 100ms; DynamoDB serves 400KB items in single-digit milliseconds. When to use each, and the pattern using both.
Dynobase has not been updated since 2022. Here are the DynamoDB GUI tools that actually work, tested by developers who use them every day.
