Late-arriving data, sometimes known as delayed data, is one of the most common issues data engineers face. When dealing with big data solutions and data engineering on platforms like Microsoft Azure, handling this late-arriving or delayed data becomes important to ensure the reliability and accuracy of your data analysis.
Understanding Late-Arriving Data:
Late-arriving data is data that reaches your processing system after the time frame for which it is applicable has passed. For example, data that is generated on a particular day (say, Monday), but due to network issues, only reaches your processing systems on the next day (Tuesday). By the time this data has arrived, the time window for Monday’s data processing jobs has passed, and hence, this data ‘arrives late’.
Strategies to Handle Late-Arriving Data in Azure:
Azure provides multiple strategies to handle late-arriving data, which can be selected based on your project needs or environment.
- Employing Windowing Functions:
- Adjusting the Late Arrival Policy:
- Utilizing Output Error Policy:
- Incorporating Azure Data Lake:
- Using Late Data Correction:
Azure Stream Analytics (ASA) makes use of windowing and watermarking techniques to deal with late-arriving data. Streaming data is divided into temporal ‘windows’, and the system processes data for each window separately. For example, you could choose to process data in 5-minute windows, where every 5 minutes, a new window of data is processed.
In Azure Stream Analytics, you can define a ‘late arrival’ policy, which specifies a time period (up to seven days) for which the system should wait for late data. By adjusting this setting, you can manage how late data is processed in your data pipeline.
Azure Stream Analytics allows for an output error policy which controls how to handle an event if writing to output fails. The choice between ‘Drop’ or ‘Retry’ provides flexibility in managing late-arriving or problematic data.
Azure Data Lake can store raw data, allowing you to reprocess data if required. So, if late-arriving data is identified, you can reprocess the data to integrate the late-arriving data.
Azure Data Factory allows late data correction with Mapping Data Flows. It performs a correction by merging new late-arriving data with previously computed aggregations.
Example: Handling Late-Arriving Data Using Azure Stream Analytics
In Azure Stream Analytics, handling late-arriving data can be as simple as defining a “late arrival tolerance”. For example, if you can tolerate a late arrival of up to 10 minutes, your setting would look like this:
"latearrivalmaxdelayinseconds": 600
Another component is the adjustment of the windowing functions, illustrated in this example:
SELECT
system.timestamp AS WindowEnd,
COUNT(*) as Count
INTO
output
FROM
input
GROUP BY TumblingWindow(Duration(minute, 5))
This SQL-like query creates a window of 5 minutes, counting the number of events in each window.
Handling late-arriving data is a crucial aspect of data engineering, particularly in real-world, real-time analytics scenarios. When preparing for DP-203: Data Engineering on Microsoft Azure, understanding how to effectively manage late-arriving data using Azure’s suite of tools and services is indispensable.
Properly managing late-arriving data ensures that your data engineering pipelines are robust, resilient, and ready to provide reliable insights, irrespective of network delays or other factors causing data to ‘arrive late’.
Practice Test
True or False: Late-arriving data doesn’t have any significant impact on the analytics solutions in Microsoft Azure.
- True
- False
Answer: False
Explanation: Late-arriving data, can significantly impact the analytics solutions in Microsoft Azure as it can cause analytics results to be incomplete or incorrect if not properly managed.
What is the term typically used to describe data that arrives later than its expected arrival time?
- A. Missed data
- B. Late-arising data
- C. Late-arriving data
- D. Late-data
Answer: C. Late-arriving data
Explanation: The term “late-arriving data” refers to data that arrives after an expected time and is used in the context of time-sensitive data-related operations.
True or False: Azure allows all data-arriving late to automatically update the corresponding analytics reports.
- True
- False
Answer: False
Explanation: Azure offers tools to manage late-arriving data, but it doesn’t automatically update reports when data arrives late. A proper management strategy needs to be implemented to handle such situations.
What class of Azure Data Factory allows you deal with late-arriving data when ingesting it into the data lake?
- A. DataFlow
- B. Activity Class
- C. Trigger
- D. Tumbling window trigger
Answer: D. Tumbling window trigger
Explanation: Tumbling window trigger, a class of Azure Data Factory, can manage late-arriving data by providing an optional delay parameter to handle it.
True or False: Azure Stream Analytics does not provide any mechanism to handle late or out-of-order data.
- True
- False
Answer: False
Explanation: Azure Stream Analytics provides a feature called “late arrival tolerance window” to handle late or out-of-order data.
Which Azure service helps you to deal with late-arriving data in streaming data pipelines?
- A. Azure Databricks
- B. Azure Data Factory
- C. Azure Stream Analytics
- D. None of the above
Answer: C. Azure Stream Analytics
Explanation: Azure Stream Analytics helps in handling late-arriving data in streaming data pipelines by allowing user-specified waiting periods for out-of-order events.
True or False: SQL-based temporal tables in Azure SQL Database can track historical data to manage late-arriving facts.
- True
- False
Answer: True
Explanation: SQL-based temporal tables in Azure SQL Database can be used to track historical data. This could enable the system to backfill data for late-arriving facts.
Which one is a good approach to handling late-arriving data in data analysis?
- A. Ignoring the late-arriving data
- B. Regular data checks and updates
- C. Always run analytics on real-time data only
- D. None of the above
Answer: B. Regular data checks and updates
Explanation: Regular data checks and updates are a reliable approach to handle late-arriving data because it ensures the completeness of data in analysis.
True or False: Tumbling window triggers in Azure Data Factory allow proactive management of late-arriving data.
- True
- False
Answer: True
Explanation: Tumbling window triggers in Azure Data Factory can be configured with an optional delay parameter for handling late-arriving data proactively.
Which Azure tool incorporates a feature of Watermark Delay to handle late-arriving data in a streaming pipeline?
- A. Azure Databricks
- B. Azure SQL Database
- C. Azure Data Lake
- D. None of the above
Answer: A. Azure Databricks
Explanation: Watermark Delay is a feature inherent to Azure Databricks, that allows it to handle late-arriving data in a streaming pipeline.
Interview Questions
What is late-arriving data in Azure Data Lake?
Late-arriving data refers to the data which due to various reasons does not arrive at the expected time in Azure Data Lake. This could be due to delays in data sources, connectivity problems, or other unforeseen issues.
How does Azure handle late-arriving data in Stream Analytics?
Azure Stream Analytics has policies to handle late-arriving data in real-time streaming pipelines. You can implement late arrival policies with window functions that describe how long to wait for the late events.
Why is it important to consider late-arriving data while designing data solutions?
The handling of late-arriving data is critical for the reliability and accuracy of real-time data analytics. If not handled properly, it can lead to inaccurate results, misleading reports, and overall, a skewed understanding of the data.
How do tumbling window functions within Azure Stream Analytics handle late-arriving data?
Tumbling window functions can handle late-arriving data by waiting for the specified amount of time for an event. If an event arrives after the end of the window, it is considered as late and either dropped or adjusted based on the late arrival policy.
What is the default late arrival policy within Azure Stream Analytics?
The default option for late arrival in Azure Stream Analytics is ‘Adjust’. With this, it attempts to include late events in the correct window.
What is the maximum timespan you can set for Azure Stream Analytics late arrival policy?
For Azure Stream Analytics, you can set a timespan of up to 7 days in the late arrival policy.
What is the purpose of the ‘Drop’ policy for late-arriving data in Stream Analytics?
The ‘Drop’ policy for late-arriving data in Stream Analytics is designed to drop any events that arrive after the window end time.
Does Azure Synapse Analytics have features for handling late-arriving data?
Azure Synapse Analytics does not inherently have features to handle late-arriving data. However, you can use Azure Stream Analytics or Azure Databricks in combination with Synapse for handling late-arriving data.
How does Azure Data Factory handles late-arriving data?
Azure Data Factory doesn’t handle late-arriving data directly. However, it provides the flexibility to design pipelines able to manage late-arriving data by using features like parameterization and trigger-based execution.
What is the role of Azure Event Hubs in handling late-arriving data?
Azure Event Hubs can store the streaming data for a certain period, thereby allowing a system to catch up on processing in case of late-arriving data. It can retain data for up to seven days.
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