AWS NoSQL Database
Amazon DynamoDBSRE & Consulting Services
Amazon DynamoDB is a fully managed serverless NoSQL database engine optimized for single-digit-millisecond key-value and document workloads at unlimited scale. JusDB delivers specialized DynamoDB DBRE including single-table data modeling, hot partition mitigation, DAX cache acceleration, on-demand versus provisioned capacity FinOps governance, and low-downtime migrations. AWS's DynamoDB SLA covers the managed service; JusDB's coverage hours and response targets are defined in your service agreement.
Expert management and optimization of Amazon DynamoDB - the fully managed NoSQL database delivering single-digit millisecond performance at any scale. Plus migration services to help you escape vendor lock-in.
DynamoDB At Scale
Response Time
Scalability
AWS global tables SLA
Requests/Second
What is Amazon DynamoDB?
Amazon DynamoDB is a fully managed, serverless, key-value and document NoSQL database designed to run high-performance applications at any scale. It offers built-in security, continuous backups, and automated multi-region replication.
Single-Digit Millisecond Latency
Consistent, fast performance at any scale with SSD storage.
Global Tables
Multi-region, multi-active replication for globally distributed applications.
On-Demand & Provisioned
Flexible capacity modes to match your workload patterns.
DynamoDB Accelerator (DAX)
In-memory caching for microsecond response times.
DynamoDB Streams
Capture item-level changes for real-time processing.
Point-in-Time Recovery
Continuous backups with restore to any second in the last 35 days.
JusDB DynamoDB SRE Services
Comprehensive management and optimization services to maximize your DynamoDB investment
Performance Optimization
Partition design, access pattern optimization, and DAX configuration.
- Partition key design and optimization
- Global secondary index (GSI) strategy
- Hot partition mitigation
- DAX cluster configuration
Capacity Planning
Right-size provisioned capacity or optimize on-demand costs.
- On-demand vs provisioned analysis
- Auto-scaling configuration
- Reserved capacity planning
- Burst capacity management
Monitoring & Alerting
CloudWatch metrics, Contributor Insights, and custom dashboards.
- CloudWatch metrics and alarms
- Contributor Insights analysis
- Throttling detection and alerting
- Capacity utilization tracking
Security Hardening
IAM policies, encryption, VPC endpoints, and compliance.
- Fine-grained access control
- Encryption at rest (KMS)
- VPC endpoint configuration
- Audit logging with CloudTrail
Backup & Recovery
PITR setup, on-demand backups, and disaster recovery planning.
- Point-in-time recovery setup
- On-demand backup automation
- Cross-region backup replication
- Recovery testing and validation
Global Tables Setup
Multi-region deployment for global applications.
- Global tables architecture design
- Conflict resolution strategies
- Cross-region monitoring
- Failover procedures
DynamoDB Migration Services
Considering migrating away from DynamoDB? We help you escape vendor lock-in and move to open-source alternatives with better economics.
Common Reasons to Migrate from DynamoDB
Cost at Scale
DynamoDB costs grow exponentially with traffic
Vendor Lock-In
Proprietary APIs create AWS dependency
Query Limitations
Limited query flexibility vs other NoSQL options
Multi-Cloud Strategy
Need database portability across clouds
DynamoDB → Apache Cassandra
Open-source, no vendor lock-in, similar wide-column data model
Key Benefits:
- No licensing costs
- Multi-cloud / on-premise flexibility
- Larger community and ecosystem
- Better price/performance at scale
DynamoDB → ScyllaDB
Cassandra-compatible; ScyllaDB cites up to 10x Cassandra throughput
Key Benefits:
- Drop-in Cassandra replacement
- Significantly lower latency
- Better hardware utilization
- Predictable performance
DynamoDB → Aerospike
Ultra-low latency with hybrid memory architecture
Key Benefits:
- Sub-millisecond latency
- Exceptional price/performance
- Strong consistency option
- Proven at massive scale
DynamoDB → MongoDB
Flexible document model with rich query capabilities
Key Benefits:
- Rich query language
- Flexible schema design
- Atlas or self-managed options
- Large developer ecosystem
Our DynamoDB Migration Process
Assessment
Analyze data model, access patterns, and workload characteristics
Schema Design
Map DynamoDB tables to target database schema
Data Migration
Export via DynamoDB Streams or S3 export, transform and load
Cutover
Parallel run, validation, and zero-downtime switchover
DynamoDB Use Cases
Ideal workloads for Amazon DynamoDB
High-Traffic Web Apps
Session management, user profiles, and real-time data.
Gaming Applications
Player data, leaderboards, and game state management.
IoT Applications
High-volume sensor data ingestion and retrieval.
E-commerce
Shopping carts, product catalogs, and order history.
Mobile Backends
Serverless backends with AppSync and Lambda.
Ad Tech
Real-time bidding and user targeting data.
Why Choose JusDB for DynamoDB?
Our team brings deep expertise in DynamoDB architecture, optimization, and migration to help you make the most of your NoSQL investment.
Expert Services Include
Amazon DynamoDB: Critical Failure Modes
Operating DynamoDB at enterprise scale introduces subtle partition throttling, secondary index backpressure, and scan latency failure modes. Here are the 3 critical edge cases our AWS DBRE team mitigates:
Unbounded Partition Key Skew & 400 ProvisionedThroughputExceeded Cascades
Uneven access distributions or low-cardinality partition keys (e.g. status codes, country codes, current date) funnel traffic into single 1,000 WCU / 3,000 RCU partition slices. Even when overall table capacity is underutilized, hot partition throttling triggers client SDK retry storms that saturate connection pools and crash upstream microservices.
JusDB rearchitects data models using composite partition keys with synthetic sharding (hash salts), activates CloudWatch Contributor Insights for real-time key heat mapping, configures adaptive capacity safeguards, and introduces DAX read caching for high-concurrency lookup keys.
GSI Backpressure Throttling Stalling Base Table Writes
When a Global Secondary Index (GSI) has insufficient provisioned write capacity or uneven partition keys compared to its base table, DynamoDB applies backpressure throttling to the base table. All transactional writes to the primary table fail, even if the primary table has ample headroom.
We conduct schema reviews to convert bloated GSIs into sparse indexes projecting only necessary attributes, implement automated GSI auto-scaling policies synchronized with base table workload limits, and migrate batch write jobs to asynchronous SQS buffer queues.
Scan Operations Scurrying Over Multi-Gigabyte Partitions & Cost Spikes
Applications running unindexed filter expressions inadvertently trigger DynamoDB Scan operations rather than targeted Query requests. Scans evaluate every item across all storage partitions before applying filters, consuming entire table read capacity within seconds and generating exponential billing spikes.
JusDB implements single-table design query paths with inverted index lookups, deploys IAM service-control policies blocking unfiltered Scans on production tables, and architects real-time CDC projection pipelines to OpenSearch for ad-hoc analytical queries.
Our AWS DBREs execute non-blocking CLI queries to diagnose partition hotspots, throttling events, and capacity bottlenecks without impacting production workloads:
# Audit consumed vs throttled write events on primary production table
aws cloudwatch get-metric-data \
--metric-data-queries '[
{"Id":"m1","MetricStat":{"Metric":{"Namespace":"AWS/DynamoDB","MetricName":"ThrottledRequests","Dimensions":[{"Name":"TableName","Value":"production-core-table"},{"Name":"Operation","Value":"PutItem"}]},"Period":60,"Stat":"Sum"}},
{"Id":"m2","MetricStat":{"Metric":{"Namespace":"AWS/DynamoDB","MetricName":"ConsumedWriteCapacityUnits","Dimensions":[{"Name":"TableName","Value":"production-core-table"}]},"Period":60,"Stat":"Sum"}}
]' \
--start-time $(date -u -v-1H +%Y-%m-%dT%H:%M:%SZ) \
--end-time $(date -u +%Y-%m-%dT%H:%M:%SZ) \
--output table# Describe table GSI status, billing mode, and continuous backup posture
aws dynamodb describe-table \
--table-name production-core-table \
--query "Table.{Billing:BillingModeSummary.BillingMode,Status:TableStatus,GSIs:GlobalSecondaryIndexes[*].{Index:IndexName,Status:IndexStatus,WriteThrottle:ProvisionedThroughput.NumberOfDecreasesToday}}" \
--output json
# Check Contributor Insights status for pinpointing hot partition keys
aws dynamodb describe-contributor-insights \
--table-name production-core-tableHow JusDB DynamoDB DBRE compares to alternative options.
DynamoDB promises limitless scale, but high-throughput workloads risk hot partition throttling, secondary index write freezes, and unpredictable billing. Here is how JusDB specialized DBRE compares against standard AWS defaults and generalist teams.
| Evaluation Vector | JusDB DynamoDB DBRE | AWS Managed Default | In-House Generalist |
|---|---|---|---|
| Architecture & Storage Engine Hardening | Single-table design modeling with composite partition keys (PK/SK), sparse GSIs to prune storage overhead, and automated TTL expiration | Raw table provisioning without data model validation; unoptimized multi-table layouts leading to N+1 scans and unindexed fan-outs | Treating DynamoDB like an RDBMS with relational anti-patterns, missing partition key entropy, and excessive Scan operations over Query APIs |
| Latency, Concurrency & Throughput Profiling | Sub-millisecond read/write profiling with DAX write-through/read-through clustering, client connection pooling, backoff jitter, and adaptive capacity tuning | Standard provisioned or on-demand mode with unmanaged SDK retry loops, default HTTP client settings, and susceptibility to sudden partition throttling | Unmitigated hot partition saturation triggering HTTP 400 ProvisionedThroughputExceededException cascades and application request timeouts |
| High Availability, Failover RTO & Disaster Recovery | Multi-region Global Tables with active-active synchronization, conflict resolution via version vectors, and automated point-in-time recovery (PITR) drill testing | Single-region default table with manual PITR snapshots; no cross-region failover automation or verified RTO/RPO recovery guarantees | Disaster recovery untested; manual JSON table exports using AWS Glue or Data Pipeline during outages with prolonged RTO (>12 hours) |
| FinOps Cost Governance & Billing Predictability | Algorithmic capacity mode switching (On-Demand vs Provisioned with auto-scaling targets), DynamoDB Standard-IA archival, and reserved capacity commitments, with savings measured against a pre-change baseline | Unmonitored On-Demand mode accumulating massive per-request write costs during batch workloads and unthrottled background ETL spikes | Over-provisioned RCU/WCU safety buffers or neglected GSI projections causing uncontrolled AWS billing creep and budget overruns |
| Telemetry, Observability & Response Coverage | CloudWatch Contributor Insights rules isolating top throttled partition keys, custom Grafana alerting on consumed capacity, and agreed initial-response targets (24x7 on-call available) | Generic CloudWatch metrics at table level only; no item-level cardinality visibility or real-time partition heat maps | Relying on application-level error logs after clients fail; inability to trace throttling back to specific hot hash keys |
| Migration, Compatibility & Zero-Downtime Upgrades | Zero-downtime dual-write CDC replication via DynamoDB Streams to Kinesis/Kafka, automated schema transformations to ScyllaDB/Cassandra, and verified cutovers | Basic AWS DMS or S3 export/import tooling without continuous change capture validation or rollback orchestration | Brittle custom migration scripts causing schema mismatches, dropped stream records, and extended maintenance downtime windows |
Ready to Optimize or Migrate Your DynamoDB?
Whether you want to optimize your existing DynamoDB deployment or migrate to an open-source alternative, our experts are here to help.
Other AWS Database Services
Amazon Aurora
MySQL & PostgreSQL compatible; AWS cites up to 5x MySQL throughput
Amazon RDS
Managed MySQL, PostgreSQL, MariaDB, Oracle & SQL Server
Amazon ElastiCache
Redis/Memcached in-memory caching at microsecond latency
Amazon DocumentDB
MongoDB-compatible document database at scale
Amazon Keyspaces
Cassandra-compatible wide-column for high-scale apps
Amazon Neptune
Graph database for billions of connected relationships