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Database ⇄ Data warehouse

Amazon DynamoDB to Apache Kylin integration — real-time data sync

Keep Amazon DynamoDB and Apache Kylin in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Amazon DynamoDB and Apache Kylin

Flow Apache Kylin data into Amazon DynamoDB in real time — no exports, no schedulers, no custom scripts.

Apache Kylin is a read-only source: Stacksync reads its data in real time and delivers it into Amazon DynamoDB, so Amazon DynamoDB always reflects the current state of Apache Kylin — without exports, scripts, or schedulers.

Operational databases and analytical warehouses want the same data at different moments. Analysts want Amazon DynamoDB's rows in Apache Kylin, current and joinable, without a change-data-capture pipeline to maintain. Engineers want the outputs of warehouse work, such as aggregates, features, and segments, available in Amazon DynamoDB where the services that read from it get them at normal query latency.

Common use cases

  • 01 Read pre-aggregated metrics from Kylin and sync them into CRM fields or planning spreadsheets on a schedule.
  • 02 Expose Kylin query results to operational dashboards without granting access to the underlying Hadoop data.
  • 03 Use DynamoDB Streams as a change-data-capture source to push item INSERT, MODIFY, and REMOVE events into a CRM, search index, or operational database in near-real-time.
  • 04 Backfill or migrate records into DynamoDB from another database using BatchWriteItem, then keep the two stores in continuous sync.

Common sync patterns

Fresh analytics without loading windows

Because changes stream continuously, analysts query current data instead of waiting for last night's load.

Offload heavy reads

Point analytical queries at the synced copy in Apache Kylin and keep Amazon DynamoDB focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from Amazon DynamoDB land in Apache Kylin as they change, replacing hand-built CDC and batch extract jobs.

What you can sync between Amazon DynamoDB and Apache Kylin

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

Amazon DynamoDB objects Apache Kylin objects How this pairing syncs
Local secondary indexes (LSIs) Extra sort keys within the same partition key, defined at table creation; queried like the base table for alternate access patterns. Build Jobs Batch jobs that compute or refresh segments, monitored via the REST API. Local secondary indexes (LSIs) is specific to Amazon DynamoDB and Build Jobs to Apache Kylin — each maps to any object or custom field on the other side.
DynamoDB Streams Ordered item-level change records (INSERT, MODIFY, REMOVE) with old/new image views and 24-hour retention; the native change-data-capture source Stacksync reads for near-real-time sync. Projects Top-level workspaces that group models, tables, and jobs. DynamoDB Streams is specific to Amazon DynamoDB and Projects to Apache Kylin — each maps to any object or custom field on the other side.
Global Tables Multi-region, active-active replicas of a table kept in sync by DynamoDB; each region is read and written locally with last-writer-wins conflict resolution. Models Star-schema definitions over source tables that determine what can be queried. Global Tables is specific to Amazon DynamoDB and Models to Apache Kylin — each maps to any object or custom field on the other side.
Time to Live (TTL) Per-item expiry timestamps; DynamoDB deletes expired items in the background and emits a Streams REMOVE record for each deletion. Cubes / Indexes Pre-computed aggregate structures that answer queries at low latency. Time to Live (TTL) is specific to Amazon DynamoDB and Cubes / Indexes to Apache Kylin — each maps to any object or custom field on the other side.
Tables Top-level containers, each with a partition key and optional sort key; Stacksync syncs a table as a stream of items with full read and write via PutItem, UpdateItem, and DeleteItem. Source Tables Hive or other upstream tables that builds read from. Tables is specific to Amazon DynamoDB and Source Tables to Apache Kylin — each maps to any object or custom field on the other side.
Items Individual schemaless records (attributes up to 400 KB each); read with GetItem, Query, and Scan and written with PutItem or BatchWriteItem, so write is supported here. Segments Time-ranged build units that partition pre-computed data. Items is specific to Amazon DynamoDB and Segments to Apache Kylin — each maps to any object or custom field on the other side.

How changes propagate between Amazon DynamoDB and Apache Kylin

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

Amazon DynamoDB Apache Kylin Sub-second propagation

DetectionChanges in Amazon DynamoDB are captured at the source via change data capture — no polling loop against its API. DynamoDB Streams emit ordered item-level change records (INSERT, MODIFY, REMOVE) with KEYS_ONLY, NEW_IMAGE, OLD_IMAGE, or NEW_AND_OLD_IMAGES views.

DeliveryApache Kylin does not accept inbound record writes, so this direction carries requests rather than records: Apache Kylin's output flows back as field updates on the originating Amazon DynamoDB records.

Apache Kylin Amazon DynamoDB Interval-based propagation

DetectionStacksync polls Apache Kylin for changes on an incremental schedule, reading only records changed since the previous pass. Data freshness follows segment build and refresh jobs, so integrations poll query results.

DeliveryEach detected change is applied to Amazon DynamoDB as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Amazon DynamoDB: Throughput is metered in read/write capacity units (provisioned or on-demand): 1 WCU = one 1 KB write per second, 1 RCU = one strongly-consistent 4 KB read per second. Exceeding capacity or the ~3,000 RCU / 1,000 WCU per-partition ceiling returns ProvisionedThroughputExceededException with throttling.
  • Apache Kylin: No fixed API quotas; query capacity depends on the deployment and pre-computed index coverage.
What ships with Amazon DynamoDB ⇄ Apache Kylin

Connect Amazon DynamoDB and Apache Kylin for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon DynamoDB–Apache Kylin connection.

Real-time

Real-time sync

Changes in Amazon DynamoDB or Apache Kylin instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon DynamoDB or Apache Kylin data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single Amazon DynamoDB or Apache Kylin record.

Observability

Monitoring

Track your Amazon DynamoDB ⇄ Apache Kylin sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Amazon DynamoDB and Apache Kylin.

How the Amazon DynamoDB and Apache Kylin connectors work

Amazon DynamoDB

Integration surface
AWS SDK / low-level HTTPS JSON API at dynamodb.<region>.amazonaws.com (PutItem, GetItem, UpdateItem, DeleteItem, Query, Scan, BatchWriteItem, TransactWriteItems), plus PartiQL (ExecuteStatement) for SQL-style access and DynamoDB Streams for change capture.
Authentication
AWS Signature Version 4 (SigV4) signed requests using an IAM access key ID and secret key, or temporary STS credentials from an assumed IAM role; IAM policies scope access down to table and item level.
Change detection
DynamoDB Streams emit ordered item-level change records (INSERT, MODIFY, REMOVE) with KEYS_ONLY, NEW_IMAGE, OLD_IMAGE, or NEW_AND_OLD_IMAGES views and 24-hour retention, read via shard iterators (or Kinesis Data Streams for longer retention). No native HTTP webhooks.
Capabilities
read · write · CDC
Rate limits
Throughput is metered in read/write capacity units (provisioned or on-demand): 1 WCU = one 1 KB write per second, 1 RCU = one strongly-consistent 4 KB read per second. Exceeding capacity or the ~3,000 RCU / 1,000 WCU per-partition ceiling returns ProvisionedThroughputExceededException with throttling.

Apache Kylin

Integration surface
SQL over JDBC/ODBC plus a REST API for queries and administration
Authentication
Username/password (HTTP basic authentication on the REST API)
Change detection
Not applicable for row-level capture; data freshness follows segment build and refresh jobs, so integrations poll query results
Capabilities
read
Rate limits
No fixed API quotas; query capacity depends on the deployment and pre-computed index coverage
How it works

How to connect Amazon DynamoDB to Apache Kylin — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate Amazon DynamoDB and Apache Kylin with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Amazon DynamoDB connected
    Apache Kylin connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Amazon DynamoDB and Apache Kylin objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Amazon DynamoDB ⇄ Apache Kylin
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Amazon DynamoDB Apache Kylin
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Amazon DynamoDB and Apache Kylin integration FAQ

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Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 424 integrations available for Amazon DynamoDB and Apache Kylin.

Popular · 5 of 424
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