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Chapter 2 · Design for New Solutions·v2.1.0·Updated 7/16/2026·~14 min

What's changed: Service coverage: added catalog sections (s4 compute/DB/storage/network, s5 analytics/ML/IoT/media/integration)

2.5Analytics, ML, IoT, Media, and Integration Services

Key points

Building blocks for data-driven and intelligent features—analytics (Athena, EMR, OpenSearch Service, Kinesis Data Streams, Data Firehose, MSK, Managed Service for Apache Flink, Lake Formation, QuickSight, Data Exchange, AppFlow), machine learning (SageMaker AI, Rekognition, Comprehend, Textract, Transcribe, Translate, Polly, Kendra, Personalize, Fraud Detector), IoT (IoT Core, Greengrass, SiteWise, etc.), media (Kinesis Video Streams, Elastic Transcoder), application integration (AppSync, MQ), and blockchain/email (Managed Blockchain, SES).

These are best separated by intent (ingest/transform/search/visualize/infer/integrate). Each service is organized by definition, role, and when to choose.

2.5.1Analytics

  • Amazon Athena: serverless analytics to query S3 data directly with standard SQL. Choose it for ad-hoc analysis of logs/data lakes with no ETL.
  • Amazon EMR: managed clusters to run big-data frameworks like Spark/Hadoop. Choose it for large-scale distributed ETL or ML preprocessing.
  • Amazon OpenSearch Service: managed search engine for full-text search, log analytics, and observability. Choose it for log search and dashboards (formerly Elasticsearch/Kibana).
  • Amazon Kinesis Data Streams: high-throughput real-time stream ingestion. Choose it for streaming needing ordering, multiple consumers, and replay.
  • Amazon Data Firehose: fully managed delivery that auto-loads/transforms streams into S3/Redshift/OpenSearch. Choose it to land streams into stores with no operations.
  • Amazon MSK: managed Apache Kafka. Choose it for streaming where you want to keep existing Kafka assets/ecosystem.
  • Amazon Managed Service for Apache Flink: real-time stateful stream processing (aggregation/analytics) with Apache Flink. Choose it to aggregate or detect anomalies on data as it arrives.
  • AWS Lake Formation: builds data lakes and centrally governs access. Choose it to manage column/row-level permissions on an S3 data lake centrally.
  • Amazon QuickSight: serverless BI dashboards/visualization. Choose it to share analytics as charts for business stakeholders.
  • AWS Data Exchange: a marketplace to subscribe to and obtain third-party datasets. Choose it to bring external commercial data into analytics pipelines.
  • Amazon AppFlow: no-code data transfer between SaaS (e.g., Salesforce) and AWS. Choose it to sync SaaS data into S3/Redshift on a schedule.

2.5.2Machine learning

  • Amazon SageMaker AI: fully managed ML platform to build, train, and deploy models end to end. Choose it to train custom models and host inference endpoints.
  • Amazon Rekognition: image/video analysis (objects, faces, moderation). Choose it to add image recognition without training.
  • Amazon Comprehend: NLP (sentiment/entities/key phrases). Choose it to extract meaning from text.
  • Amazon Textract: extracts text/tables/forms from documents (OCR+). Choose it to turn scanned forms into structured data.
  • Amazon Transcribe: speech-to-text (ASR). Choose it to transcribe calls/meetings.
  • Amazon Translate: neural machine translation. Choose it to translate content in real time or batch across languages.
  • Amazon Polly: text-to-speech. Choose it to generate narration or voice responses.
  • Amazon Kendra: ML-powered enterprise search (natural-language queries). Choose it to search internal documents in natural language.
  • Amazon Personalize: ML service for real-time recommendations. Choose it for per-user recommendations/personalization.
  • Amazon Fraud Detector: ML specialized in online fraud detection. Choose it to score payment/sign-up fraud with rules + models.

2.5.3IoT

  • AWS IoT Core: the IoT core to securely connect devices and exchange messages (MQTT, etc.). Choose it as the platform to connect many devices to the cloud.
  • AWS IoT Greengrass: runs local compute/inference/sync at the edge (device side). Choose it for on-site processing where connectivity is intermittent or latency matters.
  • AWS IoT SiteWise: collects, models, and visualizes industrial equipment data. Choose it to aggregate factory OEE/metrics.
  • AWS IoT Device Management: registers, organizes, and bulk-updates (OTA) many devices. Choose it to operate device fleets efficiently.
  • AWS IoT Device Defender: security auditing and anomaly detection for devices. Choose it to monitor configuration drift or suspicious behavior across a fleet.
  • AWS IoT Events: detects state from device events and triggers responses. Choose it to infer conditions (e.g., failure states) from multiple sensors.
  • AWS IoT Things Graph: connects devices and cloud services via workflows (visual orchestration). Note: discontinued in 2024 (out of current scope).
  • AWS IoT 1-Click: triggers Lambda from simple button-type devices. Choose it for one-touch actions from preconfigured devices. Note: discontinued in 2024.

2.5.4Media, integration, and others

  • Amazon Kinesis Video Streams: ingest, store, and play back video/audio streams. Choose it to stream camera feeds for ML analysis or playback.
  • AWS Elemental MediaConvert: transcodes media files (format/resolution). Choose it to convert uploaded video into delivery formats (note: the older Amazon Elastic Transcoder is closed to new use and being retired).
  • AWS AppSync: managed GraphQL API (real-time/offline sync). Choose it to combine multiple data sources behind one endpoint for mobile/web.
  • Amazon MQ: managed message broker (ActiveMQ/RabbitMQ). Choose it when existing apps use standard protocols like JMS/AMQP (use SQS/SNS for new decoupling).
  • Amazon Managed Blockchain: managed blockchain networks (Hyperledger Fabric/Ethereum). Choose it to share a tamper-resistant ledger among multiple parties.
  • Amazon Simple Email Service (SES): send/receive bulk transactional/marketing email. Choose it to send app notification email with high deliverability.
Exam point

Common distinctions: SQL on S3 = Athena; Spark cluster = EMR; full-text/log search = OpenSearch; high-throughput ingest = Kinesis Data Streams; auto-delivery to S3 etc. = Data Firehose; Kafka = MSK; stream aggregation = Flink; lake permissions = Lake Formation; BI = QuickSight. Images = Rekognition; NLP = Comprehend; OCR = Textract; speech-to-text = Transcribe; text-to-speech = Polly; translation = Translate; enterprise search = Kendra; recommendations = Personalize; fraud = Fraud Detector; custom models = SageMaker AI. Edge compute = Greengrass; device connectivity = IoT Core.

Diagram of analytics, ML, IoT/media, and integration.
Pick services by use case

2.5.5Section summary

  • Pick analytics by intent (Athena/EMR/OpenSearch/Kinesis/Firehose/MSK/Flink/Lake Formation/QuickSight)
  • ML is task-specific APIs (Rekognition/Comprehend/Textract/Transcribe/Polly/Translate/Kendra/Personalize/Fraud Detector) plus general-purpose SageMaker AI

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Quick check

(just a quick review)

Q1. You want ad-hoc analysis of logs in S3 using standard SQL, with no ETL or servers. What?

Q2. You want to detect objects and inappropriate content in uploaded images without training a model. What?

Q3. On factory devices with intermittent connectivity, you want local ML inference that syncs with the cloud. What?

Check your understandingPractice questions for Chapter 2: Design for New Solutions