Results for “pipeline”

Query, filter, transform, and aggregate JSON data using jq in shell pipelines and scripts.

Query, filter, transform, and aggregate JSON data using jq, with practical patterns for shell pipelines and CLI integration.

Guides database design, implementation, optimization, migration, pipeline development, and operations across SQL and NoSQL platforms.

Guides database design, implementation, optimization, migration, pipeline development, quality, and operations across SQL and NoSQL platforms.

Guides database design, implementation, optimization, migrations, data pipelines, and operations across SQL and NoSQL platforms.

Query, filter, and transform JSON with expert jq patterns, including aggregation, string formatting, and shell pipeline integration.

Query, filter, transform, and aggregate JSON data using jq, with practical patterns for shell pipelines and CLI integration.

Guides database design, implementation, optimization, migrations, data pipelines, and operations for SQL, NoSQL, and modern data platforms.

Orchestrates Python data pipelines with Prefect flows, tasks, retries, caching, parallel execution, and deployments to work pools.

Query, filter, transform, and aggregate JSON data using jq, with practical patterns for shell pipelines and integration with CLI tools.

Guides database design, implementation, query optimization, migrations, data pipeline development, and operations across SQL and NoSQL platforms.

Operate the VSS alert pipeline for real-time monitoring, Alert-Bridge subscriptions, Slack notifications, incident queries, and camera onboarding.

Analyzes sales pipeline health, revenue forecasting accuracy, and go-to-market efficiency metrics for SaaS revenue optimization.

Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking, creates orchestration DAGs, builds feature store schemas, deploys model registries, and automates retraining and validation workflows.

Convert daily market observations into structured research tickets and export validated candidate specs for a trading strategy pipeline.

Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.

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

Cleans datasets, detects anomalies, generates reports, and creates visualizations using pandas, scikit-learn, and plotting libraries to turn raw data into client-ready deliverables.

Build clinical deep-learning pipelines with PyHealth: load EHR, signal, and imaging datasets, define prediction tasks, instantiate models, train with the PyHealth Trainer, and compute clinical metrics.

Diagnoses and validates Redpanda Connect pipelines using linting, dry-run connection tests, logging, metrics, tracing, and health endpoints, including enterprise feature troubleshooting.

Define el enfoque, las herramientas y el pipeline de análisis antes de escribir código, eligiendo entre SQL, Python o un enfoque híbrido según la pregunta y los datos.

Guides the choice between DuckDB and Polars for each stage of an analytical pipeline, covering Arrow transfer, lazy versus eager execution, registration lifetime, schema conversion, and result ownership.

Designs and implements big data architectures, processes large-scale datasets with distributed systems, and optimizes data pipelines for throughput using Hadoop, Spark, and cloud platforms.

Load pre-trained models from Hugging Face Hub, run pipeline inference, generate text, and fine-tune models on NLP, vision, audio, and multimodal tasks using the Transformers library.

Monitors and manages Coveo enterprise search platform: sources, indexes, query pipelines, ML models, usage analytics, and security providers, with health checks and license utilization.

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

Guides the setup and use of dbt for extract-transform-load workflows.

Build ETL pipelines with data ingestion, cleaning, and validation steps. Use when ingesting sources, transforming formats, validating data, or scheduling loads.

Build synthetic datasets and data generation pipelines using the Data Designer library.

Build synthetic datasets and data generation pipelines using the Data Designer library.