Azure Data Engineer [DP203] Q/A | Day 5 Live Session Review

Data Ingestion is an extremely important part of data engineering that involves balancing the needs of source systems, ensuring that you do not disrupt them from their purpose, with the need of loading the data into a data store that is part of an analytical system

This blog post will go through some quick tips including Q/A and related blog posts on the topics that we covered in the Azure Data Engineer Day 5 Live Session which will help you gain a better understanding and make it easier for you to learn the Azure Data Engineer Training Program clear the [DP203] Certification & get a better-paid job.

The previous week, In Day 4 session we got an overview of concepts of Azure Databricks, Read and write data in Azure Databricks, DataFrames in Azure Databricks.

In this week, Day 5 Live Session of the [DP-203] Microsoft Data Engineering Training Program, we covered the concepts of Module 7: Ingest and load data into the data warehouse & Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines where we have covered topics like Workload Management, PolyBase, Copy Activity in Azure Data Factory, & Azure Data Factory or Azure Synapse Pipelines.

We also covered hands-on Import Data With PolyBase And COPY Using T-SQL, Petabyte-Scale Ingestion With Azure Synapse Pipelines, Code-Free Transformation At Scale With Azure Synapse Pipelines, and Orchestrate Data Movement And Transformation In Azure Synapse Pipelines out of our 40 extensive labs.

So, here are some FAQs asked during the Day 5 Live session from Module 7 & 8 Of DP203.

>Ingest and load data into the data warehouse

Data Ingestion is an extremely important part of data engineering that involves balancing the needs of source systems, ensuring that you do not disrupt them from their purpose, with the need of loading the data into a data store that is part of an analytical system.
In this module, we have learned how to ingest the data with a variety of techniques including PolyBase and the Copy command.

>PolyBase

PolyBase could be a data virtualization feature for SQL Server.
Data virtualization suggests that querying external data objects from data as if they were native objects. the foremost basic example is to form a 3rd party info table (such as Oracle) out there for queries as if it had been a SQL Server table

Microsoft BI Tools: Use PolyBase to read Blob Storage in Azure SQL DW Source: Microsoft

Q1. When to use PolyBase?
A. It is common to envision a good form of data sources and data platforms in trendy organizations. By exploiting data virtualization over disparate data sources, you get the subsequent benefits:

A common language to question the info. Your employees don’t got to learn many completely different programming/query languages and you’ll mix data employing a unifying paradigm.
A common model for security. you’ll leverage the SQL Server and Active Directory permissions to change authentication and security administration.
No got to move the data and increase the position.

Q2. Does PolyBase replace data movement?
A. This depends on the scenario. If all you do with your data movement process is moving data for one database to another (for example Mongo to SQL SERVER), data virtualization can very well fulfill the requirement.

> Use data loading best practices in Azure Synapse Analytics

Optimizing and speeding up data loads to minimize the impact on the performance of ongoing queries is a key design goal in data warehousing.
Analytical systems are constantly balanced between loading and querying workloads. Some analytical systems have loading requirements that require data to be available in near real-time, others periodically throughout the business day, or at the end of the month. Most systems you will find have a mixture of these dependents on the data sources being ingested, and the type of work being done with the data.
Loading data is essential because of the need to query or analyze the data to gain insights from it, so one of the main design goals in loading data is to manage or minimize the impact on analytical workloads while loading the data with the highest throughput possible.

Q3. Can anyone explain workload classification again?
A: There are some ways to classify data warehousing workloads, the best and commonest classification is load and question. You load data with insert, update, and delete statements. You question the data exploitation selects. a data warehousing resolution can typically have a work policy for load activity, like distribution a better resource category with a lot of resources. a distinct work policy may apply to queries, like lower importance compared to load activities.
You can additionally subclassify your load and question workloads. Subclassification offers you a lot of management of your workloads. for instance, question workloads will accommodate cube refreshes, dashboard queries, or ad-hoc queries

Q4. What are Singleton updates?
A: Singleton or smaller transaction batch loads should be grouped into larger batches to optimize the Synapse SQL Pools processing capabilities. To be clear, A one-off load to a small table with an INSERT statement may be the best approach, if it is a one-off.
However, if you need to load thousands or millions of rows throughout the day, then singleton INSERTS aren’t optimal against an MPP system. One way to solve this issue is to develop one process that writes the outputs of an INSERT statement to a file, and then another process to periodically load this file to take advantage of the parallelism that Azure Synapse Analytics.

Q5. What are Set-up dedicated data load accounts?
A: A mistake that many people make when first exploring dedicated SQL Pools are to use the service administrator account as the one used for loading data. This account is limited to using the smaller dynamic resource class that can use between 3% and 25% of the resources depending on the performance level of the provisioned SQL Pools.

Instead, it’s better to create specific accounts assigned to different resource classes dependent on the anticipated task. This will optimize load performance and maintain concurrency as required by managing the available resource slots available within the dedicated SQL Pool.

Q6. When data compression is used during ingestion what advantages will be there?
A: When loading large datasets, it’s best to use the compression capabilities of the file format. It ensures that less time is spent on the process of data transfers, using instead the power of Azure Synapse Massively Parallel Processing (MPP) compute capabilities for decompression.
It is fairly standard to maintain curated source files in columnar compressed file formats such as RC, Gzip, Parquet, and ORC, which are all supported import formats.

Q7. Why do we need to split the source files?
A: One of the key architectural components within Azure Synapse Analytics dedicated SQL pools is the decoupled storage that is segmented into 60 parts. You should maintain alignment to multiples of this number as much as possible depending on the file sizes that you are loading, and the number of compute nodes you have provisioned. Since there are 60 storage segments and a maximum of 60 MPP compute nodes within the highest performance configuration of SQL Pools, a 1:1 file to compute node to storage segment may be viable for ultra-high workloads, reducing the load times to the minimum possible.

Q8. What is the difference between Snappy and Parquet format?
A: Snappy could be a compression/decompression library. It doesn’t aim for optimum compression or compatibility with the other compression library; instead, it aims for terribly high speeds and cheap compression.
Snappy is wide used within Google, in everything from BigTable and MapReduce to our internal RPC systems. (Snappy has antecedently been noted as “Zippy)
Parquet could be a columnar format that’s supported by several different processing systems. Spark SQL provides support for each reading and writing Parquet files that mechanically preserves the schema of the original data.

> Transform data with Azure Data Factory or Azure Synapse Pipelines

In this module, we have examined Azure Data Factory and the core components that enable you to create large-scale data ingestion solutions in the cloud.

The components of Azure Data Factory:

Linked services
Datasets
Activity
Pipeline
Triggers
Pipeline runs
Parameters
Control flow
Integration Runtime

Azure data factory Source: Microsoft

Q9. Why do we need to create a linked service?
A: Linked services are much like connection strings that define the connection information needed for Data Factory to connect to external resources such as Azure Data Lake Store or Azure Databricks. With this defined, you can then create datasets which is a named view of data that simply points or references the data within a linked service.

linked service

Q11. What is the difference between trigger and publish all?
A: Triggers represent the unit of processing that determines when a pipeline execution needs to be kicked off. There are different types of triggers for different types of events.
When the data factory is not connected to any repository, it is said to operate in Live mode. In this mode, if you don’t publish the changes, those will be lost once you close the session. It is essentially the same as the “Save” option.

Q12. What is Data Flow Debug?
A: When rectify mode is on, you may interactively build your data flow with a vigorous Spark cluster. The session can shut once you switch rectify off in the Azure data factory. you ought to bear in mind of the hourly charges incurred by Azure Databricks throughout the time that you simply have the rectify session turned on.

Q13. What is the benefit of truncating the table?
A: Removes all rows from a table or such that partitions of a table, while not work the individual row deletions. TRUNCATE TABLE is comparable to the DELETE statement with no wherever clause; but, TRUNCATE TABLE is quicker and uses fewer system and dealings log resources.

Q14. When we could select Polybase in the ADF pipeline?
A: Using PolyBase is an efficient way to load a large amount of data into Azure Synapse Analytics with high throughput. You’ll see a large gain in the throughput by using PolyBase instead of the default BULKINSERT mechanism.

if your source data is in Azure Blob, Azure Data Lake Storage Gen1, or Azure Data Lake Storage Gen2, and the format is PolyBase compatible, you can use copy activity to directly invoke PolyBase to let Azure Synapse Analytics pull the data from the source.
If your source data store and format isn’t originally supported by PolyBase, use The staged copy feature additionally provides you higher turnout. It automatically converts the data into PolyBase-compatible format, stores the data in Azure Blob storage then calls PolyBase to load data into Azure synapse Analytics.

Q 15. What strategy would be adopted when there is a need for the frequent queries of the latest data but not similar to IoT? The frequency could be 15 minutes.
A: You can have a scheduled ADF or azure synapse pipeline with activities that will run after every 15 mins. Depending upon your scenario you can either load the data onto datalake / other sink or maybe just do a quick lookup on that data.

Feedback Received…

Here is some positive feedback from our trainees who attended the session.

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Quiz Time (Sample Exam Questions)!

With our Azure Data Engineer Training Program, we cover 150+ sample exam questions to help you prepare for the [DP-203] Certification.

Check out one of the questions and see if you can crack this…

Ques: Your team has created a new Azure Data Factory environment. You have to analyze the pipeline executions. Trends need to be identified in execution duration over the past 30 days. You need to create a solution that would ensure that data can be queried from Azure Log Analytics.

Which of the following would you choose as the Log type when setting up the diagnostic setting for Azure Data Factory?

A. ActivityRuns
B. AllMetrics
C. PipelineRuns
D. TriggerRuns

Comment with your answer & we will tell you if you are correct or not!

References

Next Task For You

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