| 为 Databricks 资源添加标记以进行成本归因 | https://learn.microsoft.com/en-us/azure/databricks/admin/account-settings/usage-detail-tags |
| 使用默认的 Databricks 计算策略系列 | https://learn.microsoft.com/en-us/azure/databricks/admin/clusters/policy-families |
| 在 Azure Databricks 中应用身份最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/admin/users-groups/best-practices |
| 应用 Databricks 无服务器工作区最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/admin/workspace/serverless-workspaces-best-practices |
| 合成生成 Agent 评估集 | https://learn.microsoft.com/en-us/azure/databricks/agents/agent-evaluation/synthesize-evaluation-set |
| 对 Databricks Apps Agent 进行负载测试以评估 QPS 容量 | https://learn.microsoft.com/en-us/azure/databricks/agents/custom-agents/load-test-agent-app |
| 使用检索和响应指标衡量 RAG 性能 | https://learn.microsoft.com/en-us/azure/databricks/agents/tutorials/ai-cookbook/evaluate-assess-performance |
| 使用评估集定义 RAG 应用质量 | https://learn.microsoft.com/en-us/azure/databricks/agents/tutorials/ai-cookbook/evaluate-define-quality |
| 评估和监控 RAG 应用的质量、成本和延迟 | https://learn.microsoft.com/en-us/azure/databricks/agents/tutorials/ai-cookbook/fundamentals-evaluation-monitoring-rag |
| 在 Databricks 上设计和优化 RAG 推理链 | https://learn.microsoft.com/en-us/azure/databricks/agents/tutorials/ai-cookbook/fundamentals-inference-chain-rag |
| 构建 Databricks 非结构化 RAG 数据管道 | https://learn.microsoft.com/en-us/azure/databricks/agents/tutorials/ai-cookbook/quality-data-pipeline-rag |
| 通过关键调优参数提升 RAG 应用质量 | https://learn.microsoft.com/en-us/azure/databricks/agents/tutorials/ai-cookbook/quality-overview |
| 优化 RAG 链组件以改进响应 | https://learn.microsoft.com/en-us/azure/databricks/agents/tutorials/ai-cookbook/quality-rag-chain |
| 对 Databricks 模型服务应用服务策略护栏 | https://learn.microsoft.com/en-us/azure/databricks/ai-gateway/moderate-tutorial |
| 调优 Databricks AI Search 性能和扩展能力 | https://learn.microsoft.com/en-us/azure/databricks/ai-search/best-practices |
| 提升 Databricks AI Search 检索质量 | https://learn.microsoft.com/en-us/azure/databricks/ai-search/retrieval-quality |
| 将 Databricks 库安装从 init 脚本迁移出去 | https://learn.microsoft.com/en-us/azure/databricks/archive/compute/libraries-init-scripts |
| 应用 Databricks 计算策略最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/archive/compute/policies-best-practices |
| 在 Databricks 中使用 DBIO 向云存储进行事务性写入 | https://learn.microsoft.com/en-us/azure/databricks/archive/legacy/dbio-commit |
| 使用倾斜提示优化 Databricks 中的倾斜联接 | https://learn.microsoft.com/en-us/azure/databricks/archive/legacy/skew-join |
| 从 Databricks Deep Learning Pipelines 迁移 | https://learn.microsoft.com/en-us/azure/databricks/archive/spark-3.x-migration/deep-learning-pipelines |
| 应用 Azure Databricks 平台管理最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/cheat-sheet/administration |
| 优化 Azure Databricks 上的 BI 服务性能 | https://learn.microsoft.com/en-us/azure/databricks/cheat-sheet/bi-serving |
| 应用 Databricks BI 数据准备最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/cheat-sheet/bi-serving-data-prep |
| 为 BI 优化 Azure Databricks SQL 仓库 | https://learn.microsoft.com/en-us/azure/databricks/cheat-sheet/bi-serving-sql-serving |
| 应用 Azure Databricks 计算资源创建最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/cheat-sheet/compute |
| 实施 Azure Databricks 生产作业调度最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/cheat-sheet/jobs |
| 应用 Databricks 专属的 Power BI 性能最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/cheat-sheet/power-bi |
| 使用 AI 生成的注释编写 Unity Catalog 文档 | https://learn.microsoft.com/en-us/azure/databricks/comments/ai-comments |
| 应用 Databricks 经典计算最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/compute/cluster-config-best-practices |
| 使用灵活节点类型提高 Databricks 计算可靠性 | https://learn.microsoft.com/en-us/azure/databricks/compute/flexible-node-types |
| 应用 Databricks 池最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/compute/pool-best-practices |
| 应用 Databricks 无服务器计算最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/compute/serverless/best-practices |
| 在无服务器计算上使用 DataFrame 缓存 | https://learn.microsoft.com/en-us/azure/databricks/compute/serverless/dataframe-cache |
| 使用 Lakehouse Replay 验证运行时升级 | https://learn.microsoft.com/en-us/azure/databricks/compute/serverless/lakehouse-replay |
| 为 BI 工作负载调优 SQL 仓库设置 | https://learn.microsoft.com/en-us/azure/databricks/compute/sql-warehouse/bi-workload-settings |
| 使用 Query Watchdog 控制大型交互式查询 | https://learn.microsoft.com/en-us/azure/databricks/compute/troubleshooting/query-watchdog |
| 为 Databricks 仪表板配置数据集物化 | https://learn.microsoft.com/en-us/azure/databricks/dashboards/materialization |
| 应用 Azure Databricks 数据工程最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/data-engineering/best-practices |
| 为 Databricks 流式处理工作负载实施可观测性 | https://learn.microsoft.com/en-us/azure/databricks/data-engineering/observability-best-practices |
| Unity Catalog ABAC 策略最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/abac/best-practices |
| 常见的 ABAC 行筛选和屏蔽模式 | https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/abac/common-patterns |
| 优化 ABAC 行筛选和屏蔽性能 | https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/abac/performance |
| 应用 Unity Catalog 治理最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/best-practices |
| 编写自定义 Unity Catalog 服务策略示例 | https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/service-policies/policy-examples |
| 在 Databricks 中使用旧版 Hive 元存储对象 | https://learn.microsoft.com/en-us/azure/databricks/database-objects/hive-metastore |
| 安全使用 DBFS 根目录并逐步迁移 | https://learn.microsoft.com/en-us/azure/databricks/dbfs/dbfs-root |
| 应用 DBFS 和 Unity Catalog 最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/dbfs/unity-catalog |
| 在 Databricks 上应用 Delta Lake 最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/delta/best-practices |
| 安全处理 S3 上的 Delta Lake 限制 | https://learn.microsoft.com/en-us/azure/databricks/delta/s3-limitations |
| 在 Databricks 上对 Delta Lake 使用选择性覆写选项 | https://learn.microsoft.com/en-us/azure/databricks/delta/selective-overwrite |
| 在 Databricks 上应用推荐的 CI/CD 工作流 | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/ci-cd/flows |
| 通过 CLI 查看 Databricks 策略系列 | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/cli/reference/policy-families-commands |
| 使用受支持的框架和模式开发 Databricks Apps | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/databricks-apps/app-development |
| 应用 Databricks Apps 最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/databricks-apps/best-practices |
| 在 Databricks Apps 中添加和使用模型服务终结点 | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/databricks-apps/model-serving |
| Databricks Connect 的高级配置和用法 | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/databricks-connect/advanced |
| 使用 pytest 测试 Databricks Connect Python 代码 | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/databricks-connect/python/testing |
| 使用 Databricks Connect 处理异步查询和中断 | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/databricks-connect/queries |
| 使用 ScalaTest 测试 Databricks Connect Scala 代码 | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/databricks-connect/scala/testing |
| 按照性能最佳实践调用 Databricks REST API | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/rest-api |
| 应用 Databricks 开发人员和 CI/CD 最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/developers/best-practices |
| 在 Unity Catalog 卷和工作区文件之间进行选择 | https://learn.microsoft.com/en-us/azure/databricks/files/files-recommendations |
| 在工作区文件中存储和引用 Databricks init 脚本 | https://learn.microsoft.com/en-us/azure/databricks/files/workspace-init-scripts |
| 精心设计高效的 Genie Agents 以获得准确答案 | https://learn.microsoft.com/en-us/azure/databricks/genie-agents/best-practices |
| 应用 Genie Code 的提示词和上下文最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/genie-code/tips |
| 在 Unity Catalog 中深度克隆托管的 Iceberg 表 | https://learn.microsoft.com/en-us/azure/databricks/iceberg/clone |
| 应用 Auto Loader 最佳实践以实现可靠的数据引入 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/cloud-object-storage/auto-loader/best-practices |
| 为生产环境配置 Azure Databricks Auto Loader | https://learn.microsoft.com/en-us/azure/databricks/ingestion/cloud-object-storage/auto-loader/production |
| 在 Databricks Auto Loader 中使用自动类型扩展 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/cloud-object-storage/auto-loader/type-widening |
| 应用常见的 COPY INTO 数据加载模式 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/cloud-object-storage/copy-into/examples |
| Celigo 连接器常见问题和使用指南 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/celigo-faq |
| 将常见模式应用于 Lakeflow 数据引入管道 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/common-patterns |
| Confluence 连接器行为和常见问题 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/confluence-faq |
| CrowdStrike Falcon Event Stream 连接器常见问题 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/crowdstrike-falcon-event-stream-faq |
| Glean 连接器常见问题和使用指南 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/glean-faq |
| 在 Lakeflow Connect 中有效使用 Marketo 连接器 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/marketo-faq |
| Meta Ads 连接器使用指南和常见问题 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/meta-ads-faq |
| MySQL Lakeflow 连接器常见问题和使用指南 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/mysql-faq |
| Oracle Eloqua 连接器使用常见问题 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/oracle-eloqua-faq |
| Oracle 集成 CDC 连接器常见问题和提示 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/oracle-faq |
| PostgreSQL 连接器常见问题和指南 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/postgresql-faq |
| 维护和运维 PostgreSQL Lakeflow 管道 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/postgresql-maintenance |
| 使用 Productboard 连接器功能和行为 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/productboard-faq |
| Reddit Ads 连接器使用常见问题和行为 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/reddit-ads-faq |
| 配置 Salesforce 公式字段的增量数据引入 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/salesforce-formula-fields |
| 有效使用 Zerobus 确认回调 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/zerobus-callbacks |
| 选择 Zerobus 阻塞方法以确保持久性 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/zerobus-message-blocking |
| 实施可靠的 Zerobus 恢复模式 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/zerobus-recovery |
| 设计支持数据演变的 Zerobus 架构 | https://learn.microsoft.com/en-us/azure/databricks/ingestion/zerobus-schema-management |
| 使用和配置 Databricks 集群 init 脚本 | https://learn.microsoft.com/en-us/azure/databricks/init-scripts/ |
| 在 Databricks init 脚本中引用外部文件 | https://learn.microsoft.com/en-us/azure/databricks/init-scripts/referencing-files |
| 使用旧版 Simba JDBC Driver 测试应用 | https://learn.microsoft.com/en-us/azure/databricks/integrations/jdbc/testing |
| 在代码中测试 Databricks ODBC driver 连接 | https://learn.microsoft.com/en-us/azure/databricks/integrations/odbc/testing |
| 在 Jobs 中通过回填安排定期 SQL 查询 | https://learn.microsoft.com/en-us/azure/databricks/jobs/how-to/create-recurring-job |
| 应用 Jobs 的经典计算最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/jobs/run-classic-jobs |
| 应用 Databricks 成本优化最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/cost-optimization/best-practices |
| 实施 Databricks 数据和 AI 治理最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/data-governance/best-practices |
| 为 Databricks 设计可观测性和监控策略 | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/deployment-guide/observability |
| 在 Databricks 上应用互操作性和易用性最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/interoperability-and-usability/best-practices |
| 实施 Databricks 卓越运营最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/operational-excellence/best-practices |
| 实施 Databricks 性能效率最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/performance-efficiency/best-practices |
| 为 Databricks 工作负载实施可靠性最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/reliability/best-practices |
| 在 Azure Databricks 中应用安全与合规最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/security-compliance-and-privacy/best-practices |
| 在各个支柱领域应用 Databricks 良好架构最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/well-architected |
| 确定 Lakewatch 检测仓库的规模和配置 | https://learn.microsoft.com/en-us/azure/databricks/lakewatch/admin/detection-warehouse |
| 设计高效的 Lakewatch 检测规则 SQL | https://learn.microsoft.com/en-us/azure/databricks/lakewatch/detections/write-detection-rules |
| 端到端运行 Lakewatch SIEM 工作流 | https://learn.microsoft.com/en-us/azure/databricks/lakewatch/quickstart-guide |
| 使用增强型自动缩放优化管道集群 | https://learn.microsoft.com/en-us/azure/databricks/ldp/auto-scaling |
| 应用 Lakeflow 管道最佳实践和模式 | https://learn.microsoft.com/en-us/azure/databricks/ldp/best-practices/ |
| 确保 Lakeflow 中的幂等处理和恰好一次处理 | https://learn.microsoft.com/en-us/azure/databricks/ldp/best-practices/processing-guarantees |
| 为生产环境做好 Lakeflow 管道准备 | https://learn.microsoft.com/en-us/azure/databricks/ldp/best-practices/production-readiness |
| 在 Lakeflow 中为 SCD 表实现 AUTO CDC | https://learn.microsoft.com/en-us/azure/databricks/ldp/cdc |
| 处理 Lakeflow 管道中的环境版本兼容性 | https://learn.microsoft.com/en-us/azure/databricks/ldp/developer/environment-version-compatibility |
| 在 Lakeflow 管道中管理外部 Python 依赖项 | https://learn.microsoft.com/en-us/azure/databricks/ldp/developer/external-dependencies |
| 在管道中应用数据质量预期 | https://learn.microsoft.com/en-us/azure/databricks/ldp/developer/ldp-python-ref-expectations |
| 在管道中应用高级预期模式 | https://learn.microsoft.com/en-us/azure/databricks/ldp/expectation-patterns |
| 在 Lakeflow 管道中使用 from_json 架构推断 | https://learn.microsoft.com/en-us/azure/databricks/ldp/from-json-schema-evolution |
| 安全地对流式处理表执行完整刷新 | https://learn.microsoft.com/en-us/azure/databricks/ldp/full-refresh-st |
| 使用流式处理表将数据加载到 Lakeflow 管道 | https://learn.microsoft.com/en-us/azure/databricks/ldp/load |
| 使用水印优化管道中的有状态流式处理 | https://learn.microsoft.com/en-us/azure/databricks/ldp/stateful-processing |
| 在 Lakeflow 管道中定义转换 | https://learn.microsoft.com/en-us/azure/databricks/ldp/transform |
| 使用模拟数据对 Lakeflow 管道转换进行单元测试 | https://learn.microsoft.com/en-us/azure/databricks/ldp/unit-testing |
| 优化 AI Runtime 训练性能和弹性 | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/ai-runtime/guides/performance-and-resiliency |
| 在 Databricks 上应用 Hyperopt 最佳实践并排查问题 | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/automl-hyperparam-tuning/hyperopt-best-practices |
| 使用协变量改进 Databricks AutoML 预测 | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/automl/automl-covariate-forecast |
| 源数据更改后回填 Databricks Feature Views | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/feature-store/backfill-features |
| 实现时间点正确的特征联接 | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/feature-store/time-series |
| 对 Databricks LLM 终结点进行延迟和吞吐量基准测试 | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/foundation-model-apis/prov-throughput-run-benchmark |
| 为 Databricks 分布式训练准备大型数据集 | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/load-data/ddl-data |
| 为自定义模型服务终结点配置负载测试 | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/model-serving/configure-load-test |
| 在部署 Databricks Model Serving 前验证模型 | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/model-serving/model-serving-pre-deployment-validation |
| 优化 Databricks Model Serving 终结点以用于生产环境 | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/model-serving/production-optimization |
| 使用 Locust 对 Databricks 模型服务终结点进行负载测试 | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/model-serving/what-is-load-test |
| 在 Databricks 上调优和扩展 Ray 集群 | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/ray/scale-ray |
| 在 Azure Databricks 上应用深度学习最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/train-model/dl-best-practices |
| 将现有 Apache Spark 工作负载迁移到 Databricks | https://learn.microsoft.com/en-us/azure/databricks/migration/spark |
| 在 Databricks 上实施 Agent 可观测性和质量管理 | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/agent-observability-and-quality |
| 使 MLflow LLM 评判器与人工反馈保持一致 | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/align-judges |
| 在 MLflow 中创建基于准则的 LLM 评判器 | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/concepts/judges/guidelines |
| 收集人工反馈并构建 MLflow 数据集 | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/human-feedback/ |
| 使用 Review App 让专家标注 MLflow 跟踪记录 | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/human-feedback/expert-feedback/label-existing-traces |
| 评估和比较 MLflow 提示词版本 | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/prompt-version-mgmt/prompt-registry/evaluate-prompts |
| 使用标记、上下文和反馈丰富 MLflow 跟踪记录 | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/tracing/enrich-traces |
| 使用 MLflow 跟踪记录观察和调试 Agent | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/tracing/observe-with-traces/ |
| 检测和监控 MLflow 跟踪记录中的问题 | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/tracing/observe-with-traces/analyze-traces |
| 使用 MLflow 优化多个串联提示词 | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/tutorials/examples/multi-prompt-optimization |
| 将软件工程实践应用于 Databricks 笔记本 | https://learn.microsoft.com/en-us/azure/databricks/notebooks/best-practices |
| 安全高效地运行 Databricks 笔记本 | https://learn.microsoft.com/en-us/azure/databricks/notebooks/run-notebook |
| 使用内置工具测试 Databricks 笔记本 | https://learn.microsoft.com/en-us/azure/databricks/notebooks/test-notebooks |
| 监控 Lakebase Postgres 中的活动查询 | https://learn.microsoft.com/en-us/azure/databricks/oltp/projects/active-queries |
| 分析 Lakebase 查询性能历史记录 | https://learn.microsoft.com/en-us/azure/databricks/oltp/projects/query-performance |
| 应用 Azure Databricks 性能优化建议 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/ |
| 从 Databricks 中已弃用的 Bloom filter 索引迁移 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/bloom-filters |
| 使用磁盘缓存优化 Azure Databricks 性能 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/disk-cache |
| 使用动态文件剪枝优化 Delta 联接 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/dynamic-file-pruning |
| 管理 Delta 表的隔离级别和写入冲突 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/isolation/ |
| 使用行级并发减少 Delta 写入冲突 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/isolation/row-level-concurrency |
| 使用低 Shuffle 合并优化 Delta MERGE 操作 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/low-shuffle-merge |
| 利用 Photon 的预测性 I/O 优化 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/predictive-io |
| 对托管表使用预测性优化 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/predictive-optimization |
| 在 Azure Databricks 上优化区间联接和范围联接 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/range-join |
| 使用 Spark UI 优化 Databricks 成本和性能 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/ |
| 分析 Spark 作业时间线以优化 Databricks 性能 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/jobs-timeline |
| 使用 Databricks UI 诊断耗时较长的 Spark 作业 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/long-spark-stage |
| 优化 Databricks 中 I/O 密集型 Spark 阶段 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/long-spark-stage-io |
| 诊断 Databricks Spark 阶段中的数据倾斜和溢出 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/long-spark-stage-page |
| 修复 Databricks 中只有一个任务的耗时 Spark 阶段 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/one-spark-task |
| 调试 Databricks 中低 I/O 的缓慢 Spark 阶段 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/slow-spark-stage-low-io |
| 优化 Databricks 上大量的小型 Spark 作业 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/small-spark-jobs |
| 识别 Databricks SQL DAG 中开销高昂的读取操作 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/spark-dag-expensive-read |
| 解决 Databricks 上 Spark 驱动程序过载问题 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/spark-driver-overloaded |
| 修复 Databricks 中 Spark 作业之间的间隔 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/spark-job-gaps |
| 检测 Databricks Spark 中过多的 Delta 重写操作 | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/spark-rewriting-data |
| 应用 Partner Connect 设置最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/partner-connect/best-practice |
| 配置 Databricks Lakehouse Federation 的网络 | https://learn.microsoft.com/en-us/azure/databricks/query-federation/networking |
| 优化 Azure Databricks Lakehouse Federation 性能 | https://learn.microsoft.com/en-us/azure/databricks/query-federation/performance-recommendations |
| 加密 Databricks 集群的节点间流量 | https://learn.microsoft.com/en-us/azure/databricks/security/keys/encrypt-otw |
| 应用 Databricks VNet 的自定义 DNS 最佳实践 | https://learn.microsoft.com/en-us/azure/databricks/security/network/classic/custom-dns |
| 在 Databricks SQL 处理程序中使用 SIGNAL 和 RESIGNAL | https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/control-flow/signal-stmt |
| 使用 approx_count_distinct 估算不同值计数 | https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/functions/approx_count_distinct |
| 使用 session_user 替代已弃用的 current_user | https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/functions/current_user |
| 在 Databricks 中使用 CLUSTER BY 定义液态聚类 | https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-ddl-cluster-by |
| 在 Databricks SQL 中使用 OFFSET 和 LIMIT 分页 | https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-qry-select-offset |
| 使用 REPLACE WHERE 工作流进行定向重新计算 | https://learn.microsoft.com/en-us/azure/databricks/sql/mv-st/flows-replace-where |
| 为 Databricks 警报编写高效的 SQL 模式 | https://learn.microsoft.com/en-us/azure/databricks/sql/user/alerts/query-patterns |
| 应用 Databricks SQL 查询性能洞察 | https://learn.microsoft.com/en-us/azure/databricks/sql/user/queries/performance-insights |
| 使用 RELY 约束优化 Databricks SQL | https://learn.microsoft.com/en-us/azure/databricks/sql/user/queries/query-optimization-constraints |
| 正确管理 Structured Streaming 检查点 | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/checkpoints |
| 在每个集群上运行多个 Structured Streaming 查询 | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/multiple-streams |
| 在生产环境中运行 Structured Streaming 工作负载 | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/production |
| 使用实时模式实现超低延迟流式处理 | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/real-time/ |
| 优化 Databricks 实时流式处理查询性能 | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/real-time/performance |
| 在 Databricks 中使用滚动窗口进行实时聚合 | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/rolling-window |
| 优化 Databricks 上的有状态 Structured Streaming 查询 | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/stateful-streaming |
| 优化无状态 Structured Streaming 查询 | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/stateless-streaming |
| 有效监控 Databricks Structured Streaming 查询 | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/stream-monitoring |
| 应用水印控制有状态流式处理 | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/watermarks |
| 对 Databricks Unity Catalog 表使用自动升级 | https://learn.microsoft.com/en-us/azure/databricks/tables/automatic-upgrades |
| 使用数据跳过统计信息加快 Databricks 查询 | https://learn.microsoft.com/en-us/azure/databricks/tables/data-skipping |
| 优化外部表的分区发现 | https://learn.microsoft.com/en-us/azure/databricks/tables/external-partition-discovery |
| 使用分解优化 VARIANT 性能 | https://learn.microsoft.com/en-us/azure/databricks/tables/features/variant-shredding |
| 安全使用表历史记录和时间旅行 | https://learn.microsoft.com/en-us/azure/databricks/tables/history |
| 使用注释和元数据丰富 Databricks 表 | https://learn.microsoft.com/en-us/azure/databricks/tables/operations/custom-metadata |
| 优化 Delta 和 Iceberg 表文件布局 | https://learn.microsoft.com/en-us/azure/databricks/tables/operations/optimize |
| 使用 VACUUM 回收存储空间并确保合规性 | https://learn.microsoft.com/en-us/azure/databricks/tables/operations/vacuum |
| 解读表大小并在 Databricks 中回收存储空间 | https://learn.microsoft.com/en-us/azure/databricks/tables/size |
| 控制 Delta 和 Iceberg 数据文件大小 | https://learn.microsoft.com/en-us/azure/databricks/tables/tune-file-size |
| 安全演变 Delta 和 Iceberg 表架构 | https://learn.microsoft.com/en-us/azure/databricks/tables/update-schema |
| 使用批处理、物化视图和流式处理聚合数据 | https://learn.microsoft.com/en-us/azure/databricks/transform/aggregation |
| 在 Databricks 上设计 Delta Lake 数据模型 | https://learn.microsoft.com/en-us/azure/databricks/transform/data-modeling |
| 在 Databricks 中实现批处理和流式处理联接 | https://learn.microsoft.com/en-us/azure/databricks/transform/join |
| 优化 Azure Databricks 上的联接性能 | https://learn.microsoft.com/en-us/azure/databricks/transform/optimize-joins |
| 在 Azure Databricks 上清理和验证数据 | https://learn.microsoft.com/en-us/azure/databricks/transform/validate |
| 实现会话范围的 Scala 和 Java UDF | https://learn.microsoft.com/en-us/azure/databricks/udf/scala |
| 在 Databricks UDF 中访问任务上下文 | https://learn.microsoft.com/en-us/azure/databricks/udf/udf-task-context |
| 将互联网数据下载到 Azure Databricks 卷 | https://learn.microsoft.com/en-us/azure/databricks/volumes/download-internet-files |
