The Streaming Data Platform is a unified solution that enables real-time data analytics and serves as a foundational service for AI solutions. Provectus’ streaming-first architecture powers and provides governance for a data lake ecosystem. It consolidates data pipelines and improves scalability in the cloud for real-time analysis. These well-architected solutions accelerate time-to-market and mitigate technology risks.
Streaming Data Platform is delivered as an AWS CloudFormation template quick start. Resources in the stack depend on the region. Use a table below to launch the stack in your region:
| Region | Launch stack |
|---|---|
| us-east-1 |  |
| us-east-2 | |
| us-west-2 | |
| eu-west-1 | |
| eu-central-1 | |
The template is designed for the following initiatives:
- Plug-n-play solution for processing and storing data streams in the AWS cloud
- Migration of Hadoop based on-prem platform to AWS native streaming services
- Migration of legacy Enterprise Service Bus or Data Integration architectures to AWS native platform
- Rearchitecture of Data Warehouse workloads to handle growing data volume and velocity, and to provide capabilities for real-time analytics
- Rearchitecture of slow, inconsistent and always-out-of-date data marts in existing Data Lake or Data Warehouse
- Rearchitecture of duplicated and disjointed real-time and batch pipelines
SDP provides API Gateway endpoints for ingestion of real time-messages at high throughput.
All events are pushed to Kinesis Data Streams and are processed in the Blueprint Kinesis Analytics processor, which serves as a point of customization to filter, enrich, and aggregate incoming data.
Data lands in S3 buckets in a columnar format, partitioned by date time. Smart partitioning system eventually merges them into bigger files to provide sub-second SQL queries on top of Athena.
Aggregated messages are sorted and stored in DynamoDB. They can be accessed by reporting endpoints in API Gateway. Each type of message is registered in AWS Glue with associated metadata for catalogizing and self-description purposes. AWS Athena acts as an interactive ad-hoc SQL interface on top of those tables.
Streaming Data Platform provides a blueprint machine learning path, with feature collection and continuous model retraining algorithm on top of SageMaker. As a blueprint, it implements prediction of the probability, winning the bid by the specified price.
All services which were used in the stack are available only in the following AWS regions:
- Northern Virginia (
us-east-1) - Ohio (
us-east-2) - Oregon (
us-west-2) - Ireland (
eu-west-1) - Frankfurt (
eu-central-1)
mvn clean package
Upload build artifacts to S3
aws cloudformation package
--template-file fds-template.yaml
--s3-bucket <s3-bucket-name>
--output-template-file fds.yaml
Deploy a cloudformation stack:
aws cloudformation deploy
--template-file fds.yaml
--capabilities CAPABILITY_AUTO_EXPAND CAPABILITY_IAM
[ --parameter-overrides S3Bucket=<some-value> AggregationPeriod=10 S3ResourceBucket=<some-value>]
--stack-name <stack-name>
Stack outputs:
UrlForAPI- URL for injection requestsUrlForReports- URL for retrieving reportsUrlForPredictions- URL for retrieving predictions
The integration test is launched in the us-west-2 region by default.
mvn -fn verify -DresourceBucket=<temporaryBucket>
Where temporaryBucket is a bucket for resources required to create the stack. You may remove it once stack creation is finished.
Test report is stored in ./fds-it/target/surefire-reports/
Gatling is used for performance testing
docker run -it --rm -e JAVA_OPTS="-Dduration=60 -DbaseUrl=<UrlForAPI> -Dgatling.core.simulationClass=basic.ApiPerformanceTest" -v `pwd`/gatling:/opt/gatling/user-files -v `pwd`/gatling/results:/opt/gatling/results -v `pwd`/gatling/conf:/opt/gatling/conf denvazh/gatling
Test report would be stored in ./gatling/results/
An architecture is generic enough to support any business use case. For demonstration purposes, a canonical Adtech use case is implemented.
The following data streams are available:
Ingesting Bids:
curl --request POST --header "Content-type: application/json" --data '{"tx_id":"44db4cf3-c372-4f7c-8443-9d2a1e725473","domain":"www.google.com","app_uid":"e582f2a0-3e2b-4066-a2a3-dc5867953d0d","campaign_item_id":1463517,"creative_id":"b72897cb-3f88-423b-84aa-9b7710d2416d","creative_category":"testCreativeCategory"}' '<UrlForAPI>/bid'
Bid Schema
title: Bid
type: object
properties:
app_uid:
required: true
type: string
campaign_item_id:
required: true
type: integer
creative_category:
type: string
creative_id:
type: string
tx_id:
required: true
type: string
domain:
type: string
win_price:
type: integer
type:
type: string
default: bid
Ingesting Clicks:
curl --request POST --header "Content-type: application/json" --data '{"tx_id":"44db4cf3-c372-4f7c-8443-9d2a1e725473"}' '<UrlForAPI>/click'
Click Schema
title: Click
type: object
properties:
tx_id:
required: true
type: string
type:
type: string
default: click
Ingesting Impressions:
curl --request POST --header "Content-type: application/json" --data '{"tx_id":"44db4cf3-c372-4f7c-8443-9d2a1e725473","win_price":1}' '<UrlForAPI>/impression'
Impression schema:
title: Impression
type: object
properties:
tx_id:
required: true
type: string
win_price:
required: true
type: integer
type:
type: string
default: imp
Reports are calculated in Amazon Kinesis and stored in Aggregatio Stream.
Aggregated stream is exposed to realtime consumers,
as well as to offline queries.
Aggregation Schema:
title: Aggregation
type: object
properties:
campaign_item_id:
required: true
type: integer
period:
type: integer
clicks:
type: integer
impressions:
type: integer
bids:
type: integer
Realtime reporting for particular as Ad Campaign is available via API Gateway:
curl -o bid-report.json '<UrlForReports>/reports/campaigns/<campaign_item_id>'
Kinesis Streams are snapshotted and compacted in Amazon S3 for a Data Lake type of workloads. Each table stores a data streams (Bids, Clicks, Impressions, and Aggregations) data type in json or parquet.
The following tables are available for Athena queries:
- parquet_aggregates and raw_aggregates
- parquet_bcns and raw_bcns
- parquet_clicks and raw_clicks
- parquet_impressions and raw_impressions
Streaming Data Platform has a feature called Continuous Model Training. It's mean that the learning model will retrain as soon as new data is arrived by the Streaming Data Platform.
You can learn more about SageMaker response format here
curl -d \
'{"campaignItemId":"realCompaignItemId","domain":"realDomain","creativeId":"realCreativeId","creativeCategory":"realCreativeCategory","winPrice":prefferedPrice}' -H "Content-Type: application/json" \
-X POST ${UrlForPredictions}And the result of this statement may be like this:
{"predictions": [{"score": 0.9999716281890869, "predicted_label": 1.0}]}