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.

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.

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.

Migrates data from MongoDB, PostgreSQL, MySQL, Neo4j, Kafka, and JSONL into SurrealDB with full and incremental CDC synchronization.

Search the web, extract URL content, enrich datasets with web-sourced fields, and run deep research reports, prioritizing academic and scientific sources.

Apache Airflow, dbt, Prefect, Dagster, and modern data orchestration for production data pipelines

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

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.

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