Overview

As a data engineer working on Azure, you will be responsible for managing various data-related tasks such as identifying data sources, ingesting data from various sources, processing data, and storing data in different formats. You will also be responsible for building and maintaining secure and compliant data processing pipelines using various tools and techniques.Azure data engineers use a variety of Azure data services and frameworks to store and produce cleansed and enhanced datasets for analysis. Depending on the business requirements, data stores can be designed with different architecture patterns, including modern data warehouse (MDW), big data, or Lakehouse architecture.Azure data engineers help stakeholders understand the data through exploration, and they build and maintain secure and compliant data processing pipelines by using different tools and techniques. These professionals use various Azure data services and frameworks to store and produce cleansed and enhanced datasets for analysis. This data store can be designed with different architecture patterns based on business requirements, including modern data warehouse (MDW), big data, or bakehouse architecture.Azure data engineers also help to ensure that the operationalization of data pipelines and data stores are high-performing, efficient, organized, and reliable, given a set of business requirements and constraints. These professionals help to identify and troubleshoot operational and data quality issues. They also design, implement, monitor, and optimize data platforms to meet the data pipelines.Candidates for this exam must have solid knowledge of data processing languages, including SQL, Python, and Scala, and they need to understand parallel processing and data architecture patterns. They should be proficient in using Azure Data Factory, Azure Synapse Analytics, Azure Stream Analytics, Azure Event Hubs, Azure Data Lake Storage, and Azure Data bricks to create data processing solutions.Design and implement data storage (15-20%)Develop data processing (40-45%)Secure, monitor, and optimize data storage and data processing (30-35%)Design and implement data storage (15-20%)Implement a partition strategyImplement a partition strategy for filesImplement a partition strategy for analytical workloadsImplement a partition strategy for streaming workloadsImplement a partition strategy for Azure Synapse AnalyticsIdentify when partitioning is needed in Azure Data Lake Storage Gen2Design and implement the data exploration layerCreate and execute queries by using a compute solution that leverages SQL serverless and Spark clusterRecommend and implement Azure Synapse Analytics database templatesPush new or updated data lineage to Microsoft PurviewBrowse and search metadata in Microsoft Purview Data CatalogDevelop data processing (40-45%)Ingest and transform dataDesign and implement incremental loadsTransform data by using Apache SparkTransform data by using Transact-SQL (T-SQL) in Azure Synapse AnalyticsIngest and transform data by using Azure Synapse Pipelines or Azure Data FactoryTransform data by using Azure Stream AnalyticsCleanse dataHandle duplicate dataHandle missing dataHandle late-arriving dataSplit dataShred JSONEncode and decode dataConfigure error handling for a transformationNormalize and denormalize dataPerform data exploratory analysisDevelop a batch processing solutionDevelop batch processing solutions by using Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, and Azure Data FactoryUse PolyBase to load data to a SQL poolImplement Azure Synapse Link and query the replicated dataCreate data pipelinesScale resourcesConfigure the batch sizeCreate tests for data pipelinesIntegrate Jupyter or Python notebooks into a data pipelineUpsert dataRevert data to a previous stateConfigure exception handlingConfigure batch retentionRead from and write to a delta lakeDevelop a stream processing solutionCreate a stream processing solution by using Stream Analytics and Azure Event HubsProcess data by using Spark structured streamingCreate windowed aggregatesHandle schema driftProcess time series dataProcess data across partitionsProcess within one partitionConfigure checkpoints and watermarking during processingScale resourcesCreate tests for data pipelinesOptimize pipelines for analytical or transactional purposesHandle interruptionsConfigure exception handlingUpsert dataReplay archived stream dataManage batches and pipelinesTrigger batchesHandle failed batch loadsValidate batch loadsManage data pipelines in Azure Data Factory or Azure Synapse PipelinesSchedule data pipelines in Data Factory or Azure Synapse PipelinesImplement version control for pipeline artifactsManage Spark jobs in a pipelineSecure, monitor, and optimize data storage and data processing (30-35%)Implement data securityImplement data maskingEncrypt data at rest and in motionImplement row-level and column-level securityImplement Azure role-based access control (RBAC)Implement POSIX-like access control lists (ACLs) for Data Lake Storage Gen2Implement a data retention policyImplement secure endpoints (private and public)Implement resource tokens in Azure DatabricksLoad a DataFrame with sensitive informationWrite encrypted data to tables or Parquet filesManage sensitive informationMonitor data storage and data processingImplement logging used by Azure MonitorConfigure monitoring servicesMonitor stream processingMeasure performance of data movementMonitor and update statistics about data across a systemMonitor data pipeline performanceMeasure query performanceSchedule and monitor pipeline testsInterpret Azure Monitor metrics and logsImplement a pipeline alert strategyOptimize and troubleshoot data storage and data processingCompact small filesHandle skew in dataHandle data spillOptimize resource managementTune queries by using indexersTune queries by using cacheTroubleshoot a failed Spark jobTroubleshoot a failed pipeline run, including activities executed in external servicesJoin us on this transformative journey into Azure Data Engineering, empowering yourself with the knowledge and skills to conquer the DP-203 Exam and excel in your data engineering career.

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