Results for “alternate-data-streams”

Build event streaming applications using Azure Event Hubs SDK for JavaScript (@azure/event-hubs). Use when implementing high-throughput event ingestion, real-time analytics, IoT telemetry, or event-driven architectures with partitioned consumers.

Data pipeline patterns — ETL/ELT, batch vs streaming, idempotency, orchestration. Use when designing a data pipeline, choosing between batch and streaming, implementing ingestion or transformation, setting up orchestration, or debugging pipeline failures.

Monitor and react to content distribution lifecycle events using the Sling Distribution API, covering event handling, queue monitoring, and distribution tracking.

Detect anomalies in time-series data using the Azure AI Anomaly Detector SDK for Java, with support for univariate and multivariate analysis, model training, and inference.

Ingest and process high-throughput event streams using Azure Event Hubs SDK for Python, with support for producers, consumers, and checkpointing.

Send and receive high-throughput event streams using Azure Event Hubs with .NET, including batch sending, buffered producers, and checkpoint-based processing.

Migrate AEM Cloud Service resource listeners to the lightweight ResourceChangeListener + JobConsumer pattern, covering migration from legacy APIs, OSGi configuration, troubleshooting, and review checklists.

Migrate AEM replication code from legacy CQ Replicator and Sling Replication Agent APIs to the Sling Distribution API for AEM as a Cloud Service, covering agent selection, async handling, and service-user setup.

Apache Kafka event streaming platform. Covers producers, consumers, topics, partitions, Kafka Streams, and Connect. Use for high-throughput event-driven architectures and real-time data pipelines. USE WHEN: user mentions "kafka", "event streaming", "kafka streams", "consumer groups", "topic partitions", asks about "high throughput messaging", "event sourcing", "log aggregation", "real-time pipelines" DO NOT USE FOR: simple queues - use `rabbitmq` or `activemq`; cloud-native lightweight - use `nats`; AWS-native - use `sqs`; Azure-native - use `azure-service-bus`; GCP-native - use `google-pubsub`

Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls, and potential data exfiltration using statistical baselines.

Applies the same change across multiple repositories in parallel using git worktrees, tests, and pull requests.

End-to-end data pipeline for empirical research: fetch economic data from APIs (FRED, World Bank, IMF, BLS, OECD, Yahoo Finance), clean and transform raw data, construct strategy-specific variables, and validate panel structure. Use when asked to fetch data, download data, clean data, merge datasets, prepare analysis-ready data.

Design asynchronous messaging patterns in Azure using Service Bus, Event Hubs, Event Grid, and Storage Queues. Use this skill when users ask for queueing, pub-sub, event streaming, retries, or decoupled system design. Covers delivery semantics, dead-lettering, idempotency, and monitoring.

Construye pipelines ETL/ELT con Pandas: extracción, transformación y carga de datos con logging, manejo de errores, idempotencia y opciones de orquestación.

Analyze network traffic, logs, and data flows to detect potential data exfiltration via DNS tunneling, cloud storage uploads, encrypted channels, and other indicators of compromise.

Streams change data capture from Google Cloud Spanner into Redpanda or Kafka using Redpanda Connect's gcp_spanner_cdc input, with partition-aware watermarked delivery and support for Redpanda Enterprise features.

Wire ETL, ingestion, cron, edge-function, and queue jobs correctly. Use for "build a pipeline", "sync X into Y", "nightly aggregation", "cron double-counts", "dedupe", "backfill", "the numbers are wrong after a retry". Bakes in idempotency, atomic writes, data contracts, dead-letter, and observability.

Data Pipeline Architect

Data pipeline specialist for ETL design, data quality, CDC patterns, and batch/stream processingUse when "data pipeline, etl, cdc, data quality, batch processing, stream processing, data transformation, data warehouse, data lake, data validation, data-engineering, etl, cdc, batch, streaming, data-quality, dbt, airflow, dagster, data-pipeline, ml-memory" mentioned.

ByteDance's open-source super agent harness — spawns parallel sub-agents, Docker sandbox execution, persistent long-term memory, modular skills (research/report/slides). Built on LangChain + LangGraph. Triggers on: 'deerflow', 'deer-flow', 'bytedance agent', 'super agent harness', 'LangGraph agent orchestration', 'multi-hour agent tasks', 'spawn sub-agents', 'agent sandbox docker', 'persistent agent memory', 'agent skills system', 'AI research orchestrator', 'long-running agent tasks', 'agent with memory', 'langgraph orchestrator', 'IM channel agent integration'.

Build streaming data pipelines with Redpanda Connect using declarative YAML configs, Bloblang mappings, and component discovery. Covers running, linting, and dry-running pipelines.

NATS cloud-native messaging system. Covers Core NATS, JetStream persistence, and request/reply patterns. Use for lightweight, high-performance microservices communication. USE WHEN: user mentions "nats", "jetstream", "cloud-native messaging", "request/reply", "subject wildcards", asks about "lightweight messaging", "microservices communication", "nats streaming" DO NOT USE FOR: complex routing - use `rabbitmq`; AWS-native - use `sqs`; Azure-native - use `azure-service-bus`; JMS compliance - use `activemq`; persistent queues only - use dedicated broker