Azure Data Factory, Synapse, pipelines
The complete DP-700 study guide with every exam objective mapped to 34 DriveDataScience Fabric posts. Three domains: Implement and Manage, Ingest and Transform, Monitor and Optimize. 8-week study plan, quick reference cards, exam-day tips, and 5 practice questions.
Complete Fabric Data Factory expression language guide. Where expressions work (Copy, Notebook, If Condition, ForEach, Web activities). Pipeline functions (@pipeline, @activity, @variables, @item). String functions (concat, replace, split, trim). Date functions (utcNow, formatDateTime, addDays, startOfMonth). Logical functions (if, equals, coalesce, and, or). Eight real-world patterns: yesterday incremental load, dynamic file paths with date partitioning, conditional full vs incremental, dynamic SQL, ForEach table list, notebook parameters, error handling, dynamic email subjects. ADF vs Fabric comparison (identical syntax).
The complete KQL reference guide. Pipe-based syntax explained with SQL comparison. Filtering with where (comparison, string operators, has vs contains performance). Selecting with project and extend. Aggregation with summarize (count, dcount, percentile, arg_max, make_list). Time-based analysis with bin and ago. All join types including lookup optimization. Complete function reference: string, date/time, numeric, dynamic/JSON. Let statements for variables and subqueries. Render for inline visualization. Advanced: make-series for time series, series_decompose_anomalies for anomaly detection, materialized views, stored functions. Eight real-world query patterns (latest per device, sessions, error rate, top N, funnel, spike detection, week-over-week, distributed tracing). Full KQL vs SQL comparison table.
The complete M language reference for Dataflow Gen2. The let-in structure explained. Every essential function: text (20+ functions including split, combine, replace, extract), numbers (round, math, conversion), dates (30+ functions including arithmetic, extraction, start/end of periods, formatting), logical (if-then-else, null coalescing), lists (aggregate, filter, transform), and tables (filter rows, add columns, join with all 6 join kinds, group by, pivot, unpivot, buffer). Error handling with try-otherwise. Custom functions. Query folding explained. Five real-world patterns.
Complete Fabric connections and gateways guide. Connection types for Azure, on-premises, cross-cloud (AWS S3, GCS), and SaaS sources with authentication options. Connection vs ADF Linked Service migration. On-premises data gateway installation, architecture (outbound HTTPS only, no inbound ports), and high-availability clustering. VNet data gateway for private Azure resources. Creating, sharing, and reusing connections. Security best practices (Service Principal, Key Vault). Troubleshooting connection errors. Three real-world scenarios.
The complete Delta Lake Table Properties reference. Every TBLPROPERTIES setting explained: deletedFileRetentionDuration and logRetentionDuration with VACUUM interaction, Change Data Feed (CDF) for MLVs and streaming, column mapping for rename and drop columns, autoOptimize.optimizeWrite and autoCompact, targetFileSize, protocol versions, schema.autoMerge, and data skipping. Five real-world configurations (Gold SCD dimension, Bronze staging, Silver standard, streaming target, large fact table). Fabric vs Databricks property comparison.
The consolidated Fabric optimization reference. Lakehouse: OPTIMIZE, VACUUM, Z-ORDER, V-Order, file sizing, partitioning. Pipelines: parallel execution, scheduling strategy, Copy Activity tuning. Warehouse: statistics, result set caching, query patterns. Spark: shuffle partitions, AQE, broadcast joins, caching. Eventstream: retention, caching policies, materialized views. Complete optimization checklist across all workloads.
Deep dive into Fabric scheduling and orchestration. Schedule triggers with cron syntax and time zones. Event-based triggers for file arrival and table changes. Tumbling window triggers for historical backfill. Notebook scheduling directly vs via pipeline. Five advanced orchestration patterns: master-child, conditional execution, retry with backoff, fan-out fan-in, cross-pipeline dependency chains. Dynamic scheduling expressions.
Complete Fabric monitoring and troubleshooting manual. Monitoring Hub for all item types. Pipeline, notebook, Dataflow Gen2, semantic model, and Eventstream monitoring. Fabric audit logs for compliance. Error resolution tables for every item type: pipelines, notebooks, Dataflow Gen2, Eventstream, Eventhouse, OneLake shortcuts, T-SQL. Deployment pipeline errors with what can and cannot be deployed. Setting up proactive monitoring.
Complete Materialized Lake Views guide. What MLVs are and how they differ from regular views and tables. When to use MLVs vs notebooks. Creating MLVs with aggregations and joins. Automatic refresh with Change Data Feed (CDF). Building Bronze-Silver-Gold layers with MLVs. Data quality monitoring MLV. Scheduling and debugging MLVs. MLV limitations.
