Results for “ohlcv-data”

Diagnose and observe an Oxla distributed analytical database using system catalog tables, Prometheus metrics, runtime log-level changes, and troubleshooting workflows for slow queries, node health, and memory/OOM pressure. Also covers debugging Oxla's external data sources, including the Redpanda/Kafka ingestion path.

Configures and operates an Oxla cluster: YAML config and OXLA__ environment-variable overrides, node ports, roles, and leader election; storage backends (local/S3/GCS/Azure); memory limits; access control; and TLS/OIDC, plus the HTTP-based ConnectRPC admin service and Prometheus metrics endpoint. Also covers Oxla's.

Guides users through a structured, conversational process to generate a production-ready Terraform stack and OCI Resource Manager schema for OKE clusters on Oracle Cloud Infrastructure.

Deploy and configure Generic VNIC Attachment (GVA) for OCI Kubernetes Engine (OKE), including node pool creation with secondary VNIC profiles and workload mapping.

Diagnose and root-cause issues with OCI Kubernetes Engine clusters and workloads through evidence-driven investigation.

Data Version Control for ML projects. Track large datasets and models alongside Git, build reproducible ML pipelines, and run experiments with metric comparison. Works with any storage backend including S3, GCS, Azure, and local filesystems.

Patterns for H5AD-backed Shiny apps with Excel fallback, groovy data in .uns, scVI QC validation, Leiden clustering merge, per-donor tissue extraction

Coordinate multi-stage edge research pipelines from candidate detection through strategy design, review, revision, and export.

Streams change data capture from Oracle Database into Redpanda or Kafka using the oracledb_cdc input in Redpanda Connect, which reads redo logs via LogMiner. Covers configuration, Oracle setup, checkpointing, and enterprise features.

Deploy Flutter backends to OVHCloud using Managed Kubernetes, Object Storage, and Terraform, with CLI and infrastructure-as-code examples.

Domine SQL moderno com bancos de dados cloud-native, otimização OLTP/OLAP e técnicas avançadas de query. Especialista em tuning de performance, modelagem de dados e sistemas analíticos híbridos.

Build, slice, concatenate, read, and write AnnData annotated data matrices (obs, var, X, layers, obsm, uns) — the scverse data STRUCTURE, not an analysis pipeline. Use when creating or wrangling .h5ad/zarr files, managing cell and gene annotations, concatenating batches, or handling layers/obsm/backed-mode; for the QC, normalization, clustering, UMAP, and differential-expression analysis pipeline prefer alterlab-scanpy instead, and for RNA velocity from spliced/unspliced layers prefer alterlab-scvelo instead. Part of the AlterLab Academic Skills suite.

Query external data from Oxla — Kafka topics via catalogs, Apache Iceberg tables, and S3/GCS/Azure parquet/ORC files — alongside native Oxla tables. Use when querying Kafka topics with CREATE KAFKA CATALOG or CREATE REDPANDA CATALOG, reading Apache Iceberg tables with the catalog=>path.table syntax, loading or.

Run Comet's Opik — open-source LLM observability, evaluation, and optimization — from one routing-first skill: install the Python/TypeScript SDK, stand up a server (Comet.com cloud, Docker Compose via `./opik.sh`, or Kubernetes/Helm), wire tracing through `@opik.track` or one of 50+ framework integrations (OpenAI, Anthropic, LangChain, LangGraph, LlamaIndex, CrewAI, DSPy, Ollama, Bedrock, Vercel AI SDK, …), score outputs with LLM-as-a-judge metrics (Hallucination, Moderation, Answer Relevance, Context Precision), and run Datasets/Experiments evaluations including PyTest CI gates. Use when the user wants LLM tracing, prompt evaluation, production LLM monitoring, agent optimization, or guardrails with Opik. Triggers on: opik, comet opik, opik configure, opik.sh, llm observability, llm tracing, llm as a judge, hallucination metric, prompt evaluation, opik dashboard, opik guardrails, agent optimizer.