Organizations constantly hire certified Azure Data Engineers to convert unstructured data into smart structured data. Moreover, collecting appropriate and relevant data not only help businesses drive better decisions but also give a better future perspective. Furthermore, the proper use of information also supports improvement in customer service. This has been one of the causes for a sudden rise in demands for data engineers and data scientists. In this blog, you will get a step-by-step guide to how you can become a Microsoft Certified Azure Data Engineer. Also, we provide an expert preparation guide for the Azure Exam DP-203: Data Engineering on Microsoft Azure with the related training and supervision.
The Azure Data Engineer certification authenticates your understanding to combine, transform, and combine data from multiple systems into structures that are proper for building analytics solutions.
Azure Data Engineer Roles and Responsibilities
- An applicant for the Azure Data Engineer certification must have subject matter expertise combining, transforming, and combining data from different structured and unstructured data arrangements into arrangements that are proper for establishing analytics solutions.
- Accountabilities for this role incorporate serving stakeholders understand the data through exploration, building and maintaining secure and compliant data processing pipelines by using different tools and methods. The expert practices many Azure data services and linguistics to store and give cleansed and improved datasets for the analysis.
- An Azure Data Engineer also supports the guarantee that data pipelines and data repositories are high-performing, productive, organized, and secure, given a particular set of business specifications and restraints. This professional works with unanticipated problems quickly and reduces data loss. An Azure Data Engineer also outlines, implements, observes, and optimizes data principles to satisfy the data pipeline inadequacies.
Required Knowledge
A candidate for this credential must have a firm knowledge of data processing linguistics, such as Python, SQL, or Scala, and they require understanding parallel processing and data, architecture models.
let’s jump to the course outline:
DP-203 Exam: Course Structure
Microsoft presents a course outline for the exam DP-203 that comprises the significant sections for gaining more real knowledge during the training time. The topics are:
Design and Implement Data Storage
Designing a data storage structure
- Designing an Azure Data Lake solution (Microsoft Documentation: Azure Data Lake Storage Gen2)
- Suggesting the file types for storage (Microsoft Documentation: Example scenarios)
- Recommending file types for the analytical queries (Microsoft Documentation: Query data in Azure Data Lake using Azure Data Explorer)
- Efficient querying (Microsoft Documentation: Designing for querying)
- Folder structure that shows the levels of data transformation (Microsoft Documentation: Copying and transforming the data in Azure Data Lake Storage Gen2)
- Designing a distribution plan (Microsoft Documentation: Designing distributed tables)
- Data archiving solution
Designing a partition strategy
- Partition plan for the files
- A partition plan for analytical workloads
- Partition strategy for efficiency/performance (Microsoft Documentation: Designing the partitions for query performance)
- Making a partition strategy for the Azure Synapse Analytics (Microsoft Documentation: Partitioning tables)
- Identifying when partitioning is required in the Azure Data Lake Storage Gen2
Designing the serving layer
- Star schemas (Microsoft Documentation: Overview of Star schema)
- Making slowly changing dimensions
- Making a dimensional hierarchy (Microsoft Documentation: Hierarchies in tabular models)
- Solution for temporal data (Microsoft Documentation: Temporal tables in the Azure SQL Database and Azure SQL Managed Instance)
- Incremental loading (Microsoft Documentation: Incrementally load data from a source data store to a destination data store,
- Analytical stores (Microsoft Documentation: Selecting an analytical data store in the Azure, Azure Cosmos DB analytical store)
- Metastores in the Azure Synapse Analytics and the Databricks (Microsoft Documentation: Azure Synapse Analytics shared metadata tables)
Implementing the physical data storage structures
- Compression (Microsoft Documentation: Data compression )
- Partitioning (Microsoft Documentation: Data partitioning strategies)
- Implementing the sharding (Microsoft Documentation: What is Sharding pattern, Adding a shard using Elastic Database tools)
- Executing different table geometries with the Azure Synapse Analytics pools (Microsoft Documentation: Table data types for dedicated SQL pool (formerly SQL DW) in the Azure Synapse Analytics)
- Data redundancy (Microsoft Documentation: Azure Storage redundancy, Method of how storage account is replicated)
- Distributions (Microsoft Documentation: Distributions , Table distribution Examples)
- Data archiving (Microsoft Documentation: Archive on-premises data to the cloud)
Implementing logical data structures
- Building a data solution
- Building a slowly changing dimension and a logical folder structure
- External tables (Microsoft Documentation: Using the external tables with the Synapse SQL, Creating and alter the external tables in Azure Storage)
- Implementing file and folder structures for effective querying and data pruning
Implementing the serving layer
- Delivering the data in a relational star schema
- Delivering data in the Parquet files (Microsoft Documentation: Parquet format in the Azure Data Factory
- Metadata (Microsoft Documentation: Preserve metadata and ACLs using copy activity in Azure Data Factory)
- Implementing a dimensional hierarchy (Microsoft Documentation: Creating and managing the hierarchies)
Design and Develop Data Processing
Ingesting and transforming the data
- Transforming data by using the Apache Spark (Microsoft Documentation: Transforming data in the cloud by using a Spark activity)
- Data by using the Transact-SQL (Microsoft Documentation: SQL Transformation)
- Transforming data by using Data Factory (Microsoft Documentation: Transforming data in Azure Data Factory)
- Cleansing the data (Microsoft Documentation: Data Cleansing, Clean Missing Data module)
- Splitting data (Microsoft Documentation: Split Data Overview, Split Data module)
- shred JSON
- Encoding and decoding the data
- Error handling for the transformation (Microsoft Documentation: Handling SQL truncation error rows in the Data Factory )
- Normalizing and denormalizing the values (Microsoft Documentation: Normalizing the Data module, What is Normalize Data?)
- Transform data by using the Scala (Microsoft Documentation: Extracting, transforming, and loading data by using Azure Databricks)
Designing and developing a batch processing solution
- Developing the batch processing solutions by using the Data Factory, Spark, Data Lake, PolyBase, Azure Synapse Pipelines, and Azure Databricks (Microsoft Documentation: Batch processing, Choosing a batch processing technology in Azure)
- Making data pipelines (Microsoft Documentation: Creating a pipeline, Building a data pipeline)
- Implementing incremental data loads (Microsoft Documentation: Loading data from Azure SQL Database to the Azure Blob storage)
- Developing the slowly changing dimensions
- Security and compliance needs (Microsoft Documentation: Azure security baseline for the Batch, Azure Policy Regulatory Compliance controls)
- Scaling resources (Microsoft Documentation: Creating an automatic formula for scaling the compute nodes)
- Batch size
- Designing and making tests for data pipelines
- Handling the duplicate data (Microsoft Documentation: Handling duplicate data in the Azure Data Explorer, Eliminating the Duplicate Rows module)
- Missing data (Microsoft Documentation: Missing Data )
- Late-arriving data and Upserting the data
- Regressing to a state (Microsoft Documentation: Observing the Batch solutions by counting duties and nodes by state)
- Designing the exception handling (Microsoft Documentation: Azure Batch error handling and detection)
- Batch retention (Microsoft Documentation: Azure Batch best practices)
- Designing a batch processing solution (Microsoft Documentation: Batch processing)
- Debugging Spark jobs by utilizing the Spark UI (Microsoft Documentation: Debug Apache Spark jobs running on Azure HDInsight)
Designing and developing a stream processing solution
- Developing a stream processing solution by utilizing Stream Analytics, and Azure Event Hubs (Microsoft Documentation: Stream processing with Azure Databricks, Stream data into Azure Databricks using the Event Hubs)
- Processing data by using the Spark structured streaming (Microsoft Documentation: Structured Streaming? Apache Spark Structured Streaming)
- Monitoring for performance and functional regressions (Microsoft Documentation: Stream Analytics job monitoring and method to monitor queries)
- Designing and creating the windowed aggregates (Microsoft Documentation: Streaming Analytics windowing functions, Windowing functions)
- Handling the schema drift (Microsoft Documentation: Schema drift in mapping the data flow)
- Process time-series data (Microsoft Documentation: Time handling in the Azure Stream Analytics, What is Time series solutions?)
- Processing across partitions
- Configuring checkpoints/watermarking while processing (Microsoft Documentation: Checkpoint and replay concepts, Example of watermarks)
- Making tests for the data pipelines (Microsoft Documentation: Test an Azure Stream Analytics job)
- Optimizing pipelines for analytical or transactional purposes (Microsoft Documentation: Query parallelization in Azure Stream Analytics, Optimizing processing with Azure Stream Analytics using repartitioning)
- Handling interruptions
- Designing and configuring the exception handling
- Upserting the data
- Replaying archived stream data
- Designing a stream processing solution (Microsoft Documentation: Stream processing)
Manage batches and pipelines
- Triggering batches (Microsoft Documentation: Triggering a Batch job using Azure Functions)
- Handling the failed batch loads
- Validating the batch loads
- Data pipelines in the Data Factory/Synapse Pipelines (Microsoft Documentation: Managing the mapping data flow graph)
- Scheduling the data pipelines in the Data Factory/Synapse Pipelines (Microsoft Documentation: Creating a trigger)
- Implementing version control for the pipeline artifacts (Microsoft Documentation: Source control in the Azure Data Factory)
- Managing the Spark jobs in a pipeline (Microsoft Documentation: Monitoring a pipeline)
Designing and Implementing Data Security
Designing security for the data policies and standards
- Data encryption for the data at rest and in transit (Microsoft Documentation: Data in transit)
- Designing a data auditing (Microsoft Documentation: Auditing for the Azure SQL Database and the Azure Synapse Analytics)
- Data masking (Microsoft Documentation: Dynamic data masking)
- Data privacy
- Data retention policy (Microsoft Documentation: Understanding the data retention in the Azure Time Series Insights Gen1)
- Purging data based on business requirements (Microsoft Documentation: Enable data purge, Overview of Data purge)
- Designing the Azure role-based access control (RBAC) and POSIX-like Access Control List (ACL) for Data Lake Storage Gen2 (Microsoft Documentation: Access control model in Azure Data Lake Storage Gen2, Access control lists (ACLs))
Implementing data security
- Implementing the data masking (Microsoft Documentation: SQL Database dynamic data masking with the Azure portal)
- Encrypting data at rest and in motion (Microsoft Documentation: Transparenting data encryption for the SQL Database, SQL Managed Instance)
- implement row-level and column-level security
- implementing Azure RBAC (Microsoft Documentation: Azure portal for assigning an Azure role for access to blob and queue data)
- implement POSIX-like ACLs for Data Lake Storage Gen2 (Microsoft Documentation: PowerShell for managing directories and files in Azure Data Lake Storage Gen2)
- implement a data retention policy (Microsoft Documentation: Configuring retention in Azure Time Series Insights Gen1)
- implementing a data auditing strategy (Microsoft Documentation: Auditing for Azure SQL Database and Azure Synapse Analytics)
- manage identities, keys, and secrets across different data platform technologies
- implement secure endpoints (private and public) (Microsoft Documentation: Private endpoints for Azure Storage, Azure SQL Managed Instance securely with public endpoints, Configure public endpoint)
- implement resource tokens in Azure Databricks (Microsoft Documentation: Authentication using Azure Databricks personal access tokens)
- load a DataFrame with sensitive information (Microsoft Documentation: Overview of DataFrames)
- write encrypted data to tables or Parquet files
- manage sensitive information (Microsoft Documentation: Explaining Security Control: Data Protection)
Monitor and Optimize Data Storage and Data Processing
Monitor data storage and data processing
- implement logging used by Azure Monitor (Microsoft Documentation: Overview of Azure Monitor Logs, Collecting custom logs with Log Analytics agent in Azure Monitor)
- configure monitoring services (Microsoft Documentation: Monitoring Azure resources with Azure Monitor, Define Enable VM insights)
- measure performance of data movement (Microsoft Documentation: Overview of Copy activity performance and scalability)
- monitor and update statistics about data across a system (Microsoft Documentation: Statistics in Synapse SQL, UPDATE STATISTICS)
- monitor data pipeline performance (Microsoft Documentation: Monitor and Alert Data Factory by using Azure Monitor)
- measure query performance (Microsoft Documentation: Query Performance Insight for Azure SQL Database)
- monitor cluster performance (Microsoft Documentation: Monitor cluster performance in Azure HDInsight)
- understand custom logging options (Microsoft Documentation: Collecting custom logs with Log Analytics agent in Azure Monitor)
- schedule and monitor pipeline tests (Microsoft Documentation: Monitor and manage Azure Data Factory pipelines by using the Azure portal and PowerShell)
- interpret Azure Monitor metrics and logs (Microsoft Documentation: Overview of Azure Monitor Metrics, Define Azure platform logs)
- interpret a Spark directed acyclic graph (DAG)
Optimize and troubleshoot data storage and data processing
- compact small files (Microsoft Documentation: Explain Auto Optimize)
- rewrite user-defined functions (UDFs) (Microsoft Documentation: Process of modifying User-defined Functions)
- handle skew in data (Microsoft Documentation: Resolve data-skew problems by using Azure Data Lake Tools for Visual Studio)
- handle data spill
- tune shuffle partitions
- find shuffling in a pipeline
- optimize resource management
- tune queries by using indexers (Microsoft Documentation: Automatic tuning in Azure SQL Database and Azure SQL Managed Instance)
- tune queries by using cache (Microsoft Documentation: Performance tuning with a result set caching)
- optimize pipelines for analytical or transactional purposes (Microsoft Documentation: What is Hyperspace?)
- optimize pipeline for descriptive versus analytical workloads (Microsoft Documentation: Optimize Apache Spark jobs in Azure Synapse Analytics)
- troubleshoot a failed spark job (Microsoft Documentation: Troubleshoot Apache Spark by using Azure HDInsight, Troubleshoot a slow or failing job on an HDInsight cluster)
- troubleshoot a failed pipeline run (Microsoft Documentation: Troubleshoot pipeline orchestration and triggers in Azure Data Factory)
Let’s get to some learning resources which definitely will going to help you!
Microsoft Data Engineer Exam DP-203 Preparatory Guide
It is time to cherish some exceptional knowledge sponsors for becoming the Azure Data Engineer Associate
Microsoft Learning Platform – Microsoft manages the DP-203 learning paths, the aspirant must visit the approved site of Microsoft. The candidate can get all desirable knowledge on the place. Also, they will find several Data Engineering on Microsoft Azure learning pathways and documentation. Also, one can reach the guide for Exam DP-203: Data Engineering on Microsoft Azure of Microsoft.
Microsoft Documentations– For Exam DP-203: Data Engineering on Microsoft Azure, the candidates will obtain documentation on all points belonging to the exam. This action is very necessary in order to become a Azure Data Engineer Associate .
- About dashboards, charts, reports, & widgets
- Defining the Azure Cost Management
- Linking work items to the deployments
Instructor-Led Training– The Exam AZ-400: Designing and Implementing Microsoft DevOps Solutions training contributions that Microsoft impersonates itself are presented on their website. The instructor-led training is necessary to support in order to provide for an exam. The candidate can recognize the instructor-led training on the page of the exam on the Microsoft website. The following is the training plan proposed by Microsoft.
Course AZ-400T00-A: Designing and Implementing Microsoft DevOps solutions
Refer to Online Tutorials– Exam AZ-400: Designing and Implementing Microsoft DevOps Solutions Online Tutorial improves your knowledge and produces a pitch understanding of the exam theories. Additionally, they also include exam details and procedures. Nevertheless, these online tutorials give and in-depth information associated with the examination.
Join a Study Group – For becoming the Azure DevOps Engineer, the candidate demands to get and engage in education. So, we are insinuating you to hop some studies associations where everyone can study the plans with the people that have the equal goal. This will influence the applicant completely in their preparation.
Evaluate with Practice Test– The most important action is to halt hands-on practice tests. The Microsoft AZ-400 Practice tests are the one which ensures the claimant about their learning. There are many practice tests achievable on the internet now, the candidate can choose whichever they need. The practice test is helpful in implementing the Exam AZ-400: Designing and Implementing Microsoft DevOps Solutions. So, Start Preparing Immediately!
To Conclude!
Microsoft constantly increasing its learning pathways and groupings to support the candidate and their requirement to keep in speed with today’s alarming and nurturing IT settings. This most attractive upgraded credential will improve the candidate’s consciousness to keep energy with today’s known delegations. So, start the material directly with Testpreptraining!
