| Tag Databricks resources for cost attribution | https://learn.microsoft.com/en-us/azure/databricks/admin/account-settings/usage-detail-tags |
| Use default Databricks compute policy families | https://learn.microsoft.com/en-us/azure/databricks/admin/clusters/policy-families |
| Implement managed disaster recovery for Azure Databricks | https://learn.microsoft.com/en-us/azure/databricks/admin/managed-disaster-recovery |
| Apply identity best practices in Azure Databricks | https://learn.microsoft.com/en-us/azure/databricks/admin/users-groups/best-practices |
| Apply best practices for serverless workspaces | https://learn.microsoft.com/en-us/azure/databricks/admin/workspace/serverless-workspaces-best-practices |
| Synthetically generate agent evaluation sets | https://learn.microsoft.com/en-us/azure/databricks/agents/agent-evaluation/synthesize-evaluation-set |
| Load test Databricks Apps agents for QPS limits | https://learn.microsoft.com/en-us/azure/databricks/agents/custom-agents/load-test-agent-app |
| Measure RAG performance with retrieval and response metrics | https://learn.microsoft.com/en-us/azure/databricks/agents/tutorials/ai-cookbook/evaluate-assess-performance |
| Define RAG application quality with evaluation sets | https://learn.microsoft.com/en-us/azure/databricks/agents/tutorials/ai-cookbook/evaluate-define-quality |
| Evaluate and monitor RAG applications for quality, cost, latency | https://learn.microsoft.com/en-us/azure/databricks/agents/tutorials/ai-cookbook/fundamentals-evaluation-monitoring-rag |
| Design and optimize RAG inference chains on Databricks | https://learn.microsoft.com/en-us/azure/databricks/agents/tutorials/ai-cookbook/fundamentals-inference-chain-rag |
| Build and tune unstructured RAG data pipelines | https://learn.microsoft.com/en-us/azure/databricks/agents/tutorials/ai-cookbook/quality-data-pipeline-rag |
| Improve RAG application quality via key tuning knobs | https://learn.microsoft.com/en-us/azure/databricks/agents/tutorials/ai-cookbook/quality-overview |
| Optimize RAG chain components for better responses | https://learn.microsoft.com/en-us/azure/databricks/agents/tutorials/ai-cookbook/quality-rag-chain |
| Apply performance best practices for AI Search | https://learn.microsoft.com/en-us/azure/databricks/ai-search/best-practices |
| Load test Databricks AI Search endpoints | https://learn.microsoft.com/en-us/azure/databricks/ai-search/endpoint-load-test |
| Improve Databricks AI Search retrieval quality | https://learn.microsoft.com/en-us/azure/databricks/ai-search/retrieval-quality |
| Migrate Databricks library installs from init scripts | https://learn.microsoft.com/en-us/azure/databricks/archive/compute/libraries-init-scripts |
| Apply best practices for Databricks compute policies | https://learn.microsoft.com/en-us/azure/databricks/archive/compute/policies-best-practices |
| Use DBIO for transactional writes to cloud storage in Databricks | https://learn.microsoft.com/en-us/azure/databricks/archive/legacy/dbio-commit |
| Optimize skewed joins in Databricks using skew hints | https://learn.microsoft.com/en-us/azure/databricks/archive/legacy/skew-join |
| Migrate from Databricks Deep Learning Pipelines | https://learn.microsoft.com/en-us/azure/databricks/archive/spark-3.x-migration/deep-learning-pipelines |
| Apply Azure Databricks platform administration best practices | https://learn.microsoft.com/en-us/azure/databricks/cheat-sheet/administration |
| Optimize BI serving performance on Azure Databricks | https://learn.microsoft.com/en-us/azure/databricks/cheat-sheet/bi-serving |
| Prepare and model data for high-performance BI on Databricks | https://learn.microsoft.com/en-us/azure/databricks/cheat-sheet/bi-serving-data-prep |
| Optimize Azure Databricks SQL warehouses for BI | https://learn.microsoft.com/en-us/azure/databricks/cheat-sheet/bi-serving-sql-serving |
| Apply Azure Databricks compute creation best practices | https://learn.microsoft.com/en-us/azure/databricks/cheat-sheet/compute |
| Implement Azure Databricks production job scheduling best practices | https://learn.microsoft.com/en-us/azure/databricks/cheat-sheet/jobs |
| Apply Databricks-specific Power BI performance best practices | https://learn.microsoft.com/en-us/azure/databricks/cheat-sheet/power-bi |
| Use AI-generated comments for Unity Catalog documentation | https://learn.microsoft.com/en-us/azure/databricks/comments/ai-comments |
| Apply best practices for Databricks classic compute | https://learn.microsoft.com/en-us/azure/databricks/compute/cluster-config-best-practices |
| Use flexible node types for reliable Databricks compute | https://learn.microsoft.com/en-us/azure/databricks/compute/flexible-node-types |
| Apply best practices for Databricks pools | https://learn.microsoft.com/en-us/azure/databricks/compute/pool-best-practices |
| Follow best practices for Databricks serverless compute | https://learn.microsoft.com/en-us/azure/databricks/compute/serverless/best-practices |
| Use Lakehouse Replay to validate runtime upgrades | https://learn.microsoft.com/en-us/azure/databricks/compute/serverless/lakehouse-replay |
| Tune SQL warehouse settings for BI workloads | https://learn.microsoft.com/en-us/azure/databricks/compute/sql-warehouse/bi-workload-settings |
| Control large interactive queries with Query Watchdog | https://learn.microsoft.com/en-us/azure/databricks/compute/troubleshooting/query-watchdog |
| Optimize Databricks dashboard performance with caching | https://learn.microsoft.com/en-us/azure/databricks/dashboards/caching |
| Implement observability for Databricks streaming workloads | https://learn.microsoft.com/en-us/azure/databricks/data-engineering/observability-best-practices |
| Handle schema evolution in Azure Databricks pipelines | https://learn.microsoft.com/en-us/azure/databricks/data-engineering/schema-evolution |
| Apply best practices for Unity Catalog ABAC policies | https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/abac/best-practices |
| Use common ABAC row filtering and masking patterns | https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/abac/common-patterns |
| Optimize performance of ABAC row filters and masks | https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/abac/performance |
| Apply Unity Catalog governance best practices | https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/best-practices |
| Manage Unity Catalog object storage lifecycle and recovery | https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/object-storage-lifecycle |
| Author Unity Catalog service policies with examples | https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/service-policies/policy-examples |
| Work with legacy Hive metastore objects in Databricks | https://learn.microsoft.com/en-us/azure/databricks/database-objects/hive-metastore |
| Safely use and migrate away from DBFS root | https://learn.microsoft.com/en-us/azure/databricks/dbfs/dbfs-root |
| Apply best practices for DBFS and Unity Catalog | https://learn.microsoft.com/en-us/azure/databricks/dbfs/unity-catalog |
| Apply Delta Lake best practices on Databricks | https://learn.microsoft.com/en-us/azure/databricks/delta/best-practices |
| Handle Delta Lake limitations on S3 safely | https://learn.microsoft.com/en-us/azure/databricks/delta/s3-limitations |
| Use selective overwrite options with Delta Lake on Databricks | https://learn.microsoft.com/en-us/azure/databricks/delta/selective-overwrite |
| Apply recommended CI/CD workflows on Databricks | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/ci-cd/flows |
| View Databricks policy families via CLI | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/cli/reference/policy-families-commands |
| Develop Databricks Apps using supported frameworks and patterns | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/databricks-apps/app-development |
| Apply best practices for Databricks Apps | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/databricks-apps/best-practices |
| Advanced configuration and usage of Databricks Connect | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/databricks-connect/advanced |
| Test Databricks Connect Python code with pytest | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/databricks-connect/python/testing |
| Handle asynchronous queries and interruptions in Databricks Connect | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/databricks-connect/queries |
| Test Databricks Connect Scala code with ScalaTest | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/databricks-connect/scala/testing |
| Call Databricks REST API with performance best practices | https://learn.microsoft.com/en-us/azure/databricks/dev-tools/rest-api |
| Apply Databricks developer and CI/CD best practices | https://learn.microsoft.com/en-us/azure/databricks/developers/best-practices |
| Choose between Unity Catalog volumes and workspace files | https://learn.microsoft.com/en-us/azure/databricks/files/files-recommendations |
| Store and reference Databricks init scripts in workspace files | https://learn.microsoft.com/en-us/azure/databricks/files/workspace-init-scripts |
| Curate effective Genie Agents for accurate answers | https://learn.microsoft.com/en-us/azure/databricks/genie-agents/best-practices |
| Apply prompt and context best practices for Genie Code | https://learn.microsoft.com/en-us/azure/databricks/genie-code/tips |
| Deep clone managed Iceberg tables in Unity Catalog | https://learn.microsoft.com/en-us/azure/databricks/iceberg/clone |
| Apply Azure Databricks Auto Loader best practices | https://learn.microsoft.com/en-us/azure/databricks/ingestion/cloud-object-storage/auto-loader/best-practices |
| Configure Azure Databricks Auto Loader for production | https://learn.microsoft.com/en-us/azure/databricks/ingestion/cloud-object-storage/auto-loader/production |
| Apply common COPY INTO data loading patterns | https://learn.microsoft.com/en-us/azure/databricks/ingestion/cloud-object-storage/copy-into/examples |
| Apply common patterns to Lakeflow ingestion pipelines | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/common-patterns |
| Apply Confluence connector behaviors and FAQs | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/confluence-faq |
| Apply Dynamics 365 connector FAQs and behaviors | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/d365-faq |
| Safely fully refresh Lakeflow target tables | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/full-refresh |
| Apply Glean connector FAQs and usage guidance | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/glean-faq |
| Use Gmail connector FAQs and behavior guidance | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/gmail-faq |
| Apply MySQL connector usage best practices | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/mysql-faq |
| Use Oracle integrated CDC connector FAQs and tips | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/oracle-faq |
| Apply PostgreSQL connector FAQs and guidance | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/postgresql-faq |
| Maintain PostgreSQL ingestion pipelines in production | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/postgresql-maintenance |
| Optimize incremental ingestion of Salesforce formula fields | https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/salesforce-formula-fields |
| Use Zerobus acknowledgment callbacks effectively | https://learn.microsoft.com/en-us/azure/databricks/ingestion/zerobus-callbacks |
| Choose Zerobus blocking methods for durability | https://learn.microsoft.com/en-us/azure/databricks/ingestion/zerobus-message-blocking |
| Implement resilient Zerobus recovery patterns | https://learn.microsoft.com/en-us/azure/databricks/ingestion/zerobus-recovery |
| Design Zerobus schemas for evolving data | https://learn.microsoft.com/en-us/azure/databricks/ingestion/zerobus-schema-management |
| Use init scripts to configure Databricks clusters | https://learn.microsoft.com/en-us/azure/databricks/init-scripts/ |
| Reference external files in Databricks init scripts | https://learn.microsoft.com/en-us/azure/databricks/init-scripts/referencing-files |
| Test applications using the legacy Simba JDBC Driver | https://learn.microsoft.com/en-us/azure/databricks/integrations/jdbc/testing |
| Test Databricks ODBC driver connections in code | https://learn.microsoft.com/en-us/azure/databricks/integrations/odbc/testing |
| Diagnose and optimize Lakeflow Jobs performance | https://learn.microsoft.com/en-us/azure/databricks/jobs/diagnose-job-performance |
| Schedule recurring SQL queries with backfill in Jobs | https://learn.microsoft.com/en-us/azure/databricks/jobs/how-to/create-recurring-job |
| Configure classic compute for Databricks Lakeflow Jobs | https://learn.microsoft.com/en-us/azure/databricks/jobs/run-classic-jobs |
| Apply Databricks cost optimization best practices | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/cost-optimization/best-practices |
| Implement best practices for Databricks data and AI governance | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/data-governance/best-practices |
| Design observability and monitoring strategy for Databricks | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/deployment-guide/observability |
| Apply interoperability and usability best practices on Databricks | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/interoperability-and-usability/best-practices |
| Implement operational excellence best practices for Databricks | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/operational-excellence/best-practices |
| Implement performance efficiency best practices for Databricks | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/performance-efficiency/best-practices |
| Implement reliability best practices for Databricks workloads | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/reliability/best-practices |
| Implement Databricks security, compliance, and privacy best practices | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/security-compliance-and-privacy/best-practices |
| Apply Databricks well-architected best practices across pillars | https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/well-architected |
| Classify documents with large label taxonomies | https://learn.microsoft.com/en-us/azure/databricks/large-language-models/classify-documents-labels-tutorial |
| Optimize pipeline clusters with enhanced autoscaling | https://learn.microsoft.com/en-us/azure/databricks/ldp/auto-scaling |
| Apply Lakeflow pipeline design best practices | https://learn.microsoft.com/en-us/azure/databricks/ldp/best-practices/ |
| Design Lakeflow pipelines for safe retries | https://learn.microsoft.com/en-us/azure/databricks/ldp/best-practices/processing-guarantees |
| Apply production readiness checks to Lakeflow pipelines | https://learn.microsoft.com/en-us/azure/databricks/ldp/best-practices/production-readiness |
| Use REPLACE WHERE flows for targeted recomputes | https://learn.microsoft.com/en-us/azure/databricks/ldp/dbsql/flows-replace-where |
| Apply data quality expectations in pipelines | https://learn.microsoft.com/en-us/azure/databricks/ldp/developer/ldp-python-ref-expectations |
| Apply advanced expectation patterns across datasets | https://learn.microsoft.com/en-us/azure/databricks/ldp/expectation-patterns |
| Perform full refreshes of streaming tables safely | https://learn.microsoft.com/en-us/azure/databricks/ldp/full-refresh-st |
| Optimize stateful streaming with watermarks in pipelines | https://learn.microsoft.com/en-us/azure/databricks/ldp/stateful-processing |
| Unit test Lakeflow pipeline transformations with mocks | https://learn.microsoft.com/en-us/azure/databricks/ldp/unit-testing |
| Optimize AI Runtime training performance and resiliency | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/ai-runtime/guides/performance-and-resiliency |
| Apply Hyperopt best practices and troubleshooting on Databricks | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/automl-hyperparam-tuning/hyperopt-best-practices |
| Improve Databricks AutoML forecasting with covariates | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/automl/automl-covariate-forecast |
| Follow Databricks machine learning lifecycle practices | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/concepts/ml-lifecycle |
| Optimize Databricks Feature Store costs | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/feature-store/cost-management |
| Implement point-in-time correct feature joins | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/feature-store/time-series |
| Benchmark Databricks LLM endpoints for latency and throughput | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/foundation-model-apis/prov-throughput-run-benchmark |
| Prepare large datasets for distributed training on Databricks | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/load-data/ddl-data |
| Apply recommended LLMOps workflows on Databricks | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/mlops/llmops |
| Configure load tests for custom model serving endpoints | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/model-serving/configure-load-test |
| Validate models before Databricks Model Serving deployment | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/model-serving/model-serving-pre-deployment-validation |
| Monitor Databricks model quality and endpoint health | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/model-serving/monitor-diagnose-endpoints |
| Optimize Databricks Model Serving endpoints for production | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/model-serving/production-optimization |
| Plan and execute load testing for Databricks serving endpoints | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/model-serving/what-is-load-test |
| Tune and scale Ray clusters on Databricks | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/ray/scale-ray |
| Apply deep learning best practices on Azure Databricks | https://learn.microsoft.com/en-us/azure/databricks/machine-learning/train-model/dl-best-practices |
| Adapt existing Apache Spark workloads to Databricks | https://learn.microsoft.com/en-us/azure/databricks/migration/spark |
| Evaluate and improve agents with MLflow scorers | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/ |
| Align MLflow LLM judges with human feedback | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/align-judges |
| Create guidelines-based LLM judges in MLflow | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/concepts/judges/guidelines |
| Developer workflow for MLflow code-based scorers | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/custom-scorer-dev-workflow |
| Tutorial: Evaluate and improve MLflow GenAI agents | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/evaluate-app |
| Monitor MLflow GenAI agents in production | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/monitor-in-production |
| Collect human feedback and build evaluation datasets | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/human-feedback/ |
| Label MLflow traces during agent development | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/human-feedback/dev-annotations |
| Enable experts to label MLflow traces with Review App | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/human-feedback/expert-feedback/label-existing-traces |
| Evaluate and compare MLflow prompt versions | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/prompt-version-mgmt/prompt-registry/evaluate-prompts |
| Detect issues across MLflow GenAI traces | https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/tracing/observe-with-traces/analyze-traces |
| Apply software engineering practices to Databricks notebooks | https://learn.microsoft.com/en-us/azure/databricks/notebooks/best-practices |
| Run Databricks notebooks safely and efficiently | https://learn.microsoft.com/en-us/azure/databricks/notebooks/run-notebook |
| Test Databricks notebooks with built-in tools | https://learn.microsoft.com/en-us/azure/databricks/notebooks/test-notebooks |
| Monitor active queries in Lakebase Postgres | https://learn.microsoft.com/en-us/azure/databricks/oltp/projects/active-queries |
| Analyze Lakebase query performance history | https://learn.microsoft.com/en-us/azure/databricks/oltp/projects/query-performance |
| Apply performance optimization recommendations on Databricks | https://learn.microsoft.com/en-us/azure/databricks/optimizations/ |
| Use adaptive query execution on Databricks | https://learn.microsoft.com/en-us/azure/databricks/optimizations/aqe |
| Decommission deprecated Bloom filter indexes | https://learn.microsoft.com/en-us/azure/databricks/optimizations/bloom-filters |
| Optimize Spark SQL queries with Databricks CBO | https://learn.microsoft.com/en-us/azure/databricks/optimizations/cbo |
| Improve read performance with Databricks disk cache | https://learn.microsoft.com/en-us/azure/databricks/optimizations/disk-cache |
| Use dynamic file pruning for Delta queries | https://learn.microsoft.com/en-us/azure/databricks/optimizations/dynamic-file-pruning |
| Reduce write conflicts with row-level concurrency | https://learn.microsoft.com/en-us/azure/databricks/optimizations/isolation/row-level-concurrency |
| Optimize Delta MERGE with low shuffle merge | https://learn.microsoft.com/en-us/azure/databricks/optimizations/low-shuffle-merge |
| Use predictive I/O optimizations on Databricks | https://learn.microsoft.com/en-us/azure/databricks/optimizations/predictive-io |
| Use predictive optimization for managed tables | https://learn.microsoft.com/en-us/azure/databricks/optimizations/predictive-optimization |
| Tune range join optimization on Databricks | https://learn.microsoft.com/en-us/azure/databricks/optimizations/range-join |
| Diagnose Databricks Spark cost and performance in UI | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/ |
| Handle Databricks spot instance losses effectively | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/losing-spot-instances |
| Resolve long Spark stages with a single task | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/one-spark-task |
| Optimize many small Spark jobs in Databricks | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/small-spark-jobs |
| Mitigate overloaded Spark driver on Databricks | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/spark-driver-overloaded |
| Detect unnecessary data rewriting in Databricks Spark writes | https://learn.microsoft.com/en-us/azure/databricks/optimizations/spark-ui-guide/spark-rewriting-data |
| Apply best practices for Partner Connect setup | https://learn.microsoft.com/en-us/azure/databricks/partner-connect/best-practice |
| Configure networking for Lakehouse Federation data sources | https://learn.microsoft.com/en-us/azure/databricks/query-federation/networking |
| Optimize performance of Lakehouse Federation queries | https://learn.microsoft.com/en-us/azure/databricks/query-federation/performance-recommendations |
| Encrypt inter-node traffic for Databricks clusters | https://learn.microsoft.com/en-us/azure/databricks/security/keys/encrypt-otw |
| Use SIGNAL and RESIGNAL in Databricks SQL handlers | https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/control-flow/signal-stmt |
| Optimize Delta Lake tables with OPTIMIZE in Databricks | https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-optimize |
| Estimate distinct counts with approx_count_distinct | https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/functions/approx_count_distinct |
| Use session_user instead of deprecated current_user | https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/functions/current_user |
| Define liquid clustering with CLUSTER BY in Databricks | https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-ddl-cluster-by |
| Use OFFSET and LIMIT for pagination in Databricks SQL | https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-qry-select-offset |
| Author effective SQL patterns for Databricks alerts | https://learn.microsoft.com/en-us/azure/databricks/sql/user/alerts/query-patterns |
| Apply Databricks SQL performance insights and recommendations | https://learn.microsoft.com/en-us/azure/databricks/sql/user/queries/performance-insights |
| Optimize Databricks SQL queries using RELY constraints | https://learn.microsoft.com/en-us/azure/databricks/sql/user/queries/query-optimization-constraints |
| Use asynchronous transformWithState for higher throughput | https://learn.microsoft.com/en-us/azure/databricks/stateful-applications/async |
| Manage Structured Streaming checkpoints correctly | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/checkpoints |
| Run multiple streaming queries per Databricks cluster | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/multiple-streams |
| Run Structured Streaming in production on Databricks | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/production |
| Use real-time mode for ultra-low latency streaming | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/real-time/ |
| Optimize and monitor real-time mode performance | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/real-time/performance |
| Manage and optimize stateful streaming queries | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/stateful-streaming |
| Optimize stateless Structured Streaming queries | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/stateless-streaming |
| Apply watermarks for stateful streaming control | https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/watermarks |
| Use liquid clustering instead of partitioning | https://learn.microsoft.com/en-us/azure/databricks/tables/clustering |
| Leverage data skipping for faster queries | https://learn.microsoft.com/en-us/azure/databricks/tables/data-skipping |
| Optimize partition discovery for external tables | https://learn.microsoft.com/en-us/azure/databricks/tables/external-partition-discovery |
| Use change data feed for Delta and Iceberg v3 | https://learn.microsoft.com/en-us/azure/databricks/tables/features/change-data-feed |
| Optimize VARIANT performance with shredding | https://learn.microsoft.com/en-us/azure/databricks/tables/features/variant-shredding |
| Use table history and time travel safely | https://learn.microsoft.com/en-us/azure/databricks/tables/history |
| Enrich Databricks tables with comments and metadata | https://learn.microsoft.com/en-us/azure/databricks/tables/operations/custom-metadata |
| Safely drop or replace Databricks tables by type | https://learn.microsoft.com/en-us/azure/databricks/tables/operations/drop-table |
| Optimize Delta and Iceberg table file layout | https://learn.microsoft.com/en-us/azure/databricks/tables/operations/optimize |
| Use VACUUM to reclaim storage and ensure compliance | https://learn.microsoft.com/en-us/azure/databricks/tables/operations/vacuum |
| Interpret table size and reclaim storage in Databricks | https://learn.microsoft.com/en-us/azure/databricks/tables/size |
| Control Delta and Iceberg data file sizes | https://learn.microsoft.com/en-us/azure/databricks/tables/tune-file-size |
| Safely evolve Delta and Iceberg table schemas | https://learn.microsoft.com/en-us/azure/databricks/tables/update-schema |
| Aggregate data using batch, materialized views, and streaming | https://learn.microsoft.com/en-us/azure/databricks/transform/aggregation |
| Design Delta Lake data models on Databricks | https://learn.microsoft.com/en-us/azure/databricks/transform/data-modeling |
| Implement joins for batch and streaming in Databricks | https://learn.microsoft.com/en-us/azure/databricks/transform/join |
| Optimize join performance on Azure Databricks | https://learn.microsoft.com/en-us/azure/databricks/transform/optimize-joins |
| Clean and validate data on Azure Databricks | https://learn.microsoft.com/en-us/azure/databricks/transform/validate |
| Implement and use Python UDFs in Azure Databricks | https://learn.microsoft.com/en-us/azure/databricks/udf/python |
| Access task context inside Databricks UDFs | https://learn.microsoft.com/en-us/azure/databricks/udf/udf-task-context |
| Download internet data into Azure Databricks volumes | https://learn.microsoft.com/en-us/azure/databricks/volumes/download-internet-files |
