When preparing for the DP-203 Data Engineering on Microsoft Azure exam, one of the essential concepts to understand is data cleansing, also termed as data cleaning or data scrubbing. Data cleansing is a critical process of identifying and correcting or removing corrupt, inaccurate, or irrelevant parts of data in a dataset. This task is paramount because incorrect or inconsistent data can lead to faulty results and impact your data analysis’s credibility.
Why Is Data Cleansing Essential?
In the world of data engineering on Microsoft Azure, the importance of data cleansing cannot be overstated. The primary reason why data cleansing is crucial includes:
- Accuracy: Cleanse data increases the accuracy of the overall data analysis process.
- Consistency: By removing irregularities, the consistency throughout datasets is maintained.
- Decision Making: Clean data aids in making reliable decisions as it provides accurate insights.
- Efficiency: Data cleaning process reduces redundancy and increases the efficiency of data handling.
- Compliance: Certain industries have strict regulations regarding the accuracy and quality of data. Data cleansing helps organizations stay compliant.
Data Cleansing in Azure
In Microsoft Azure, users can leverage various inbuilt tools and services to perform data cleansing tasks.
- Azure Machine Learning: The Azure Machine Learning service provides built-in modules to give robust data cleansing functionality. The “Clean Missing Data” module is one such tool that identifies missing values from the dataset and provides options to replace, remove, or infer them based on specific rules.
- Azure Data Factory: With Mapping Data Flows in Azure Data Factory, users can create data transformation logic without writing a single line of code. The “Derived Column” transformation allows users to replace null values with a default value, thereby cleaning the data.
- Azure Databricks: Azure Databricks offers a powerful platform for big data analytics and provides a collaborative environment for data cleansing. Python and Spark’s data cleaning functions can be used efficiently for data cleansing in Azure Databricks.
Here’s a quick example of how to remove missing values using the Azure Machine Learning service:
- Step 1: Drag and drop the ‘Clean Missing Data’ from the ‘Data Transformation’ category in the left-side panel onto the canvas.
- Step 2: Connect it to the dataset containing missing values that need to be cleaned.
- Step 3: In the ‘Properties’ pane on the right side, you can choose the cleaning mode. The choices are ‘Remove entire row’, ‘Replace with mean’, ‘Replace with median’, ‘Replace with mode’, or ‘Custom substitution value’.
- Step 4: Run the experiment and view the result by right-clicking the module and selecting ‘Visualize’.
Data cleansing is a significant criterion in achieving reliable data analysis on Microsoft Azure. It is an ongoing and vital process for maintaining the accuracy and reliability of your data sources. Therefore, investing time in understanding and applying data cleansing techniques before appearing for the DP-203 Data Engineering on Microsoft Azure exam can be highly beneficial.
Practice Test
True or False: Data cleansing is the process of spotting and rectifying inaccurate or corrupt data from a database.
- True
- False
Answer: True.
Explanation: Data cleansing involves identifying any errors or inaccuracies and then replacing, modifying, or deleting the dirty data in a dataset.
Select the correct definition of data cleansing:
- a) The process of identifying and removing duplicate records.
- b) The process of creating a backup of a database.
- c) The process of identifying and correcting data inaccuracies.
- d) The process of removing excess data from a database.
Answer: c) The process of identifying and correcting data inaccuracies.
Explanation: While data cleansing may include removing duplicates, its main purpose is to identify and correct inaccuracies in the data.
Which of the following is NOT a benefit of cleansing data?
- a) Improved accuracy of analytics.
- b) Enhanced decision-making capabilities.
- c) Increased data storage requirements.
- d) Enhanced productivity.
Answer: c) Increased data storage requirements.
Explanation: Cleaning data actually reduces storage requirements by eliminating unnecessary data.
What are some common methods of data cleansing?
- a) Deleting Duplicates
- b) Ignoring Errors
- c) Equivalence Class Partitioning
- d) Data Transformation
Answer: a) Deleting Duplicates, c) Equivalence Class Partitioning, d) Data Transformation
Explanation: Ignoring Errors is not a valid method of data cleansing.
True or False: Implementing methods for cleansing data can lower the level-of-confidence in the data.
- True
- False
Answer: False.
Explanation: Implementing data cleansing methods increases the level of confidence in the data by reducing inaccuracies.
Which is NOT a typically used data cleansing technique in Microsoft Azure?
- a) Azure Purview
- b) Azure Master Data Services
- c) Azure Synapse Analytics
- d) Azure Data Catalog
Answer: d) Azure Data Catalog.
Explanation: Azure Data Catalog is a service for registering and discovering data, not typically used for data cleansing.
True or False: Data cleansing is an initial, one-time process.
- True
- False
Answer: False.
Explanation: Data cleansing is an ongoing process that needs to be performed regularly to ensure consistent data quality.
In Microsoft Azure, data can be cleansed using which services?
- a) Azure Data Factory
- b) Azure Data Explorer
- c) Azure SQL Data Warehouse
- d) Azure Databricks
Answer: a) Azure Data Factory, b) Azure Data Explorer, d) Azure Databricks
Explanation: These services provide features that enable data cleaning processes. Azure SQL Data Warehouse is now part of Azure Synapse Analytics.
True or False: It’s nonessential to validate data after cleansing.
- True
- False
Answer: False.
Explanation: Validating data post-cleansing is crucial to ensure that the data cleansing process was thorough and accurate.
Data cleansing should be performed on _________ data.
- a) Duplicate
- b) Inaccurate
- c) Outdated
- d) All
Answer: d) All
Explanation: Regardless of the type, data cleansing can and should be applied to all data for the most accurate results.
Interview Questions
What is the function of the “Cleanse” step in Azure data flows?
The “Cleanse” step in Azure data flows remove, replace, or modify data values in a data stream that are incorrect, incomplete, improperly formatted, or duplicated.
What is Unicode normalization for data cleansing in Azure Data Factory?
Unicode normalization is a process of converting text in different Unicode encodings to a standard normalized form, which helps in comparing and searching for data more accurately.
What is a Replace transformation in Azure data flows for data cleansing?
A Replace transformation in Azure data flows is used to substitute a specific data value with a new one. This is often used to replace null or incorrect values with a new corrected or default value.
In Azure data flows, can you cleanse data using a time-frame condition?
Yes, in Azure data flows you can use a time-frame condition to cleanse data. This is useful when, for instance, removing outdated records or records that fall outside a designated timeframe.
Which tool in Azure should be used to cleanse data in a large, complex, multi-step data transformation?
Azure Data Factory and specifically the Mapping Data Flow feature is designed for complex, multi-step transformations including data cleansing.
Why is data cleansing important in the context of Data Engineering on Azure?
Data Cleansing is crucial as it improves data quality, leading to better decision-making for businesses, enhanced operational efficiency, and accurate analytics and reporting.
How can users control how data is parsed during the cleansing process in Azure Data Factory?
Users can control how data is parsed by defining and selecting specific patterns that describe how data should be interpreted.
Can Azure Machine Learning be used for data cleansing?
Yes, Azure Machine Learning provides Data Preparation capabilities which can be used for data cleansing.
How does the “Split” function contribute to data cleansing in Azure data flows?
The “Split” function is used to break apart the data based on a specific delimiter, which can be useful for separating combined data fields into individual fields.
What Azure feature allows businesses to ensure their global data is valid, accurate, and available?
Azure Data Quality Services allows businesses to cleanse, match, standardize and enrich their data, ensuring global data is valid, accurate and appropriately distributed.
Can PowerShell script be useful for data cleansing in Azure Data Factory?
Yes, by calling some specific Azure Data Factory actions via PowerShell, users can implement a powerful and customized data cleansing pipeline.
What are the primary components involved in data cleansing in Azure Data Factory?
The primary components are Data Flow task for transformations, Derived Column Task for adding derived columns, Conditional Split for filtering rows, Lookup for joining tables and Union All for merging datasets.
What are Lambda functions in the context of Azure data cleansing?
Lambda functions are nameless inline computations that can be used in Azure Stream Analytics for various tasks, including data cleansing operations.
Which languages are often used for scripting in helping with data cleansing in Azure services?
Common languages include T-SQL (Azure SQL Database), U-SQL (Azure Data Lake), and Python (Azure Machine Learning and Azure Databricks).
What is ‘profiling’ in Azure Data Catalog and why is it significant for data cleansing?
Profiling in Azure Data Catalog refers to the gathering and presentation of data statistics, such as min, max, average, etc. It’s significant for data cleansing as it helps identify data inconsistencies, errors or anomalies.
extutorials.com