Use notebook-connector's itde_manager to run a local Docker-based Exasol database. Covers bring_itde_up, get_itde_status, restart_itde, take_itde_down, and how ITDE populates notebook-connector configuration automatically.
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安装前可以查看交给 AI 代理的指令内容。
Trigger when the user mentions ITDE, docker-db, local Exasol Docker database, bring_itde_up, restart_itde, take_itde_down, or local notebook-connector database setup.
Start local Docker DB
bring_itde_up, start local exasol, docker-dbreferences/itde-lifecycle.mdscripts/Check status / restart / tear down
get_itde_status, restart_itde, take_itde_down, container statusreferences/itde-lifecycle.mdscripts/Config not present yet
Use the lifecycle scripts to validate the stored ITDE setup:
scripts/check_itde_status.py after setup changesbring_itde_up, expect ItdeContainerStatus.READYstorage_backend=onprem first because bring_itde_up(...) does not populate that key itselfdb_schema yourself before handing off to schema-dependent workflows such as SQLAlchemy, Ibis, TE, or TXAIE validationABSENT or a clean no-container stateExpected failure mode:
scripts/restart_itde.py should raise a runtime error if the Docker-DB container does not exist yetstorage_backend or db_schema even when ITDE itself is healthy, because those keys are not written by bring_itde_up(...)bring_itde_up populates the secure config store with the generated DB and BucketFS connection details automatically.bring_itde_up does not populate storage_backend or db_schema.还没有评价。欢迎分享你的使用感受。
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Top-level router for Exasol work. Use for any Exasol database, exapump, SQL, BucketFS, extension, integration, UDF, Script Language Container, or Exasol Personal setup task, then route to the narrowest specialized Exasol skill.
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Exasol BucketFS file system management via the `exapump bucketfs` CLI. Covers listing, uploading, downloading, and deleting files and directories in BucketFS, the `bfsdefault` service and other bucket services, bucket structure, `bfs_*` profile settings, the `/buckets/<service>/<bucket>/<path>` UDF path, and staging JARs, models, and Script Language Containers for UDFs.
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Use Exasol Cloud Storage Extension workflows for moving structured object-storage files through extension UDFs. Covers setup prerequisites, the fixed `CLOUD_STORAGE_EXTENSION.IMPORT_PATH` and `CLOUD_STORAGE_EXTENSION.EXPORT_PATH` entrypoints used with `FROM SCRIPT` and `INTO SCRIPT`, Parquet, Avro, ORC, and Delta files on S3, Google Cloud Storage, Azure Blob Storage, Azure Data Lake, HDFS, and Alluxio, connection-object credential patterns, and routing away from native IMPORT or EXPORT when the extension path is the right fit.
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Exasol database interaction via the exapump CLI and Exasol SQL. Covers SQL queries and DML/DDL such as `SELECT`, `INSERT`, `UPDATE`, `DELETE`, `MERGE`, `CREATE TABLE`, `ALTER TABLE`, and `DROP TABLE`, `CREATE CONNECTION` connection objects without import or export intent, schema inspection, table design, `exapump sql` and `exapump profile`, query profiling, analytics, and Exasol-specific SQL behavior outside exasol-import and exasol-export.
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Distributed machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining (Apriori, FP-Growth, association rules, market-basket analysis), Lua execute script orchestration for iterative algorithms (k-means, SGD, gradient descent), scikit-learn model training, parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).
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Use Exasol document-file virtual schemas for federated read-only access to object and file storage — S3, Google Cloud Storage, Azure Blob Storage, and Azure Data Lake Storage Gen2 document files. Covers document-file adapter family selection, the EDML `MAPPING` definition, `CREATE VIRTUAL SCHEMA` usage, refresh workflows, and query-side troubleshooting.
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