In this article, we’ll have a look at the topics that you need to study for Microsoft’s DP-700 “Implementing Data Engineering Solutions Using Microsoft Fabric” exam.
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Configure Microsoft Fabric workspace settings
- Configure Spark workspace settings (additional link about Starter pools). Learn how to set up Spark environments, including defining Starter pools for cost-effective and scalable data processing.
- Configure domain workspace settings. Understand how to configure domain-specific workspaces for managing data access, governance, and collaboration across teams.
- Configure OneLake workspace settings. Master the configuration of OneLake, a unified storage solution in Microsoft Fabric, to optimize data ingestion and storage workflows.
- Configure data workflow workspace settings. These are nowadays known as Apache Airflow Job workspace settings.
Implement lifecycle management in Fabric
- Configure version control. This is covered in the DP-600 exam. Set up and manage version control systems to track and manage changes in Microsoft Fabric projects
- Implement database projects. Understand how to design, develop, and deploy database projects using tools like SQL scripts and database templates within Fabric.
- Create and configure deployment pipelines. Gain expertise in setting up deployment pipelines to automate the release of data workflows, ensuring consistency across development, testing, and production environments.
Configure security and governance
- Implement workspace-level access controls. Configure permissions at the workspace level to manage access for users and groups effectively across Fabric environments.
- Implement item-level access controls. Apply specific access controls to individual items, such as datasets, notebooks, or pipelines, for granular security.
- Implement row-level, column-level, object-level, and file-level access controls. Implement fine-grained access controls to secure data at the row, column, object, or file level, ensuring data security and compliance
- Implement dynamic data masking. Obscure sensitive information dynamically based on user roles or access levels.
- Apply sensitivity labels to items. Assign sensitivity labels to items like datasets and reports to classify and protect data in line with organizational policies.
- Endorse items. Endorse items in Fabric (e.g., certified or promoted datasets) to signal quality and reliability for organizational use.
Orchestrate processes
- Choose between a pipeline and a notebook (additional link). Decide when to use a pipeline for orchestration and workflow automation or a notebook for advanced data processing and custom logic.
- Design and implement schedules and event-based triggers (additional link). Configure schedules for periodic task execution and event-based triggers to automate workflows based on specific actions or conditions.
- Implement orchestration patterns with notebooks and pipelines, including parameters and dynamic expressions. Integrate notebooks and pipelines, using parameters and dynamic expressions for flexible and efficient workflows.
Design and implement loading patterns
- Design and implement full and incremental data loads. Create efficient full and incremental loading patterns for batch data, ensuring data pipelines handle large-scale data updates effectively.
- Prepare data for loading into a dimensional model. Understand the processes to transform and structure raw data into a dimensional model, optimizing it for analytics and reporting.
- Design and implement a loading pattern for streaming data. Ingest, transform, and load real-time data into destinations like Lakehouses, Warehouses, or KQL databases. Create efficient full and incremental loading patterns for batch data, ensuring data pipelines handle large-scale data updates effectively.
Ingest and transform batch data
- Choose an appropriate data store. Select the best data store (e.g., Lakehouse, Warehouse, or KQL Database) based on performance, scalability, and use case requirements.
- Choose between dataflows, notebooks, and T-SQL for data transformation. Understand when to use dataflows for ETL, notebooks for advanced transformations, or T-SQL for structured querying, based on the data and task complexity.
- Create and manage shortcuts to data. Provide simplified, centralized access to datasets across different workspaces in Fabric.
- Implement mirroring. Synchronize and replicate datasets between environments for consistency and high availability.
- Ingest data by using pipelines. Automate data ingestion workflows from various sources into Fabric destinations.
- Transform data by using PySpark, SQL, and KQL. Use PySpark for big data processing, SQL for structured transformations, and KQL for querying real-time analytics data.
- Denormalize data. Flatten relational datasets to improve querying performance and simplify data models.
- Group and aggregate data. Summarize, report, and analyse using Fabric tools like SQL, PySpark, or KQL.
- Handle duplicate, missing, and late-arriving data. Ensure data quality and accuracy in real-time and batch processing workflows.
Ingest and transform streaming data
- Choose an appropriate streaming engine. Choose the most suitable streaming engine (Eventstreams, Spark Structured Streaming, or KQL) based on data velocity, processing needs, and real-time requirements.
- Process data by using eventstreams. Capture, transform, and route high-velocity streaming data from sources like Event Hubs and IoT devices.
- Process data by using Spark structured streaming. For scalable and fault-tolerant stream processing with advanced transformations and real-time analytics.
- Process data by using KQL. Process and query streaming data in real-time using Kusto Query Language (KQL) for insights and monitoring.
- Create windowing functions. Process and analyze data over defined time intervals, enabling tasks like aggregations and trend detection in streaming scenarios.
Monitor Fabric items
- Monitor data ingestion. Ensure smooth and efficient data loading into Microsoft Fabric components like Lakehouses and Warehouses.
- Monitor data transformation in pipelines, notebooks, or streaming engines, identifying and resolving bottlenecks or errors.
- Monitor semantic model refresh (additional link) to ensure data consistency and up-to-date analytics.
- Configure alerts to notify users about issues or anomalies in data ingestion, transformation, or refresh processes for proactive monitoring and resolution.
Identify and resolve errors
- Identify and resolve pipeline errors, such as execution failures, data ingestion issues, and incorrect configurations.
- Identify and resolve dataflow errors, including mapping errors, connectivity problems, and transformation failures.
- Identify and resolve notebook errors, such as syntax issues, Spark job failures, and runtime exceptions.
- Identify and resolve eventhouse errors, focusing on troubleshooting issues related to real-time event ingestion and storage.
- Identify and resolve eventstream errors (additional link), such as source connectivity problems, routing misconfigurations, and transformation failures.
- Identify and resolve T-SQL errors, including syntax issues, query performance problems, and data inconsistencies.
Optimize performance
- Optimize a lakehouse table (additional link), such as partitioning, indexing, and file optimization to improve query efficiency.
- Optimize a pipeline by addressing bottlenecks, parallelizing tasks, and fine-tuning transformations for faster data processing.
- Optimize a data warehouse, including indexing, schema design, and query optimization for analytics and reporting.
- Optimize eventstreams and eventhouses for efficient real-time data ingestion, routing, and storage.
- Optimize Spark performance (additional link) by configuring cluster resources, caching, and managing shuffles to process large-scale data efficiently.
- Optimize query performance across Fabric components using techniques like query rewriting, indexing, and performance monitoring tools.