Skewness in data, in the most general sense, refers to the measure of symmetry, or more precisely, the lack of symmetry in a data distribution. When it comes to data engineering and especially while dealing with real-world big data scenarios using platforms like Microsoft Azure or the certification exam DP-203 Data Engineering on Microsoft Azure, handling skewness in data distribution is an essential skill to have under your belt.
Why is Handling Skewness Important?
Data skewness impacts the performance and efficiency of data operations. Abnormally skewed data affect overall system performance, especially while dealing with big data frameworks like Azure Databricks. More the skew, more uneven the distribution of data across partitions, leading to potentially unbalanced loads. Uneven loads lead to certain nodes being overloaded with work and others idle or less engaged, leading to decreased efficiency in processing.
Thus, handling skewness in data is key to optimizing your data pipeline, and in an Azure Data Engineering exam, you may encounter a variety of scenarios or questions around this theme.
Techniques for Handling Data Skew on Azure
There are several ways we can manage skewed data in Azure. Here are three basic strategies:
- Repartitioning: As the name suggests, repartitioning involves the redistribution of data across various nodes or partitions to ensure a balanced load. The
repartition()function in Spark is often employed for this purpose. - Salting: Salting involves the addition of random noise to a skewed key to distribute it more evenly across all nodes. In the case of a join operation, salting is applied to the tables on both sides of the join, ensuring that the skewed keys do not end up on the same executor.
- Bucketing: This option involves the creation of buckets and distributing the data across these buckets. Bucketing can help speed up join operations due to less data being shuffled when a join is performed. However, this option is not always recommended due to the overhead of managing the bucketed tables.
Implementation of Techniques
Here are examples of how one might implement these techniques while working with Spark on Azure Databricks:
Repartitioning
from pyspark.sql.functions import col
dataframe = dataframe.repartition(10, col("column_with_skew"))
In the above code, the data is repartitioned to 10 partitions using the column_with_skew. This helps in spreading the data evenly making it less skewed.
Salting
from pyspark.sql.functions import rand
salted_df = dataframe.withColumn("salted_key", concat(col("key"), lit("-"), (rand()*100).cast("int")))
In the above code, the “salting” strategy is implemented by adding a random integer between 0 to 100 into a new column ‘salted_key’. This helps to distribute the data more evenly.
Please keep in mind that these are just techniques. How and where to use these techniques efficiently will always depend on your specific data scenario.
Dealing with skewed data in a cloud database system like Azure is not just a niche topic for your DP-203 Data Engineering exam on Microsoft Azure. It’s an important skill in the real world where imbalanced data distribution may lead to inefficient data operations. However, understanding and efficiently handling skewness can boost your overall system performance and put you ahead in your data engineering journey. So, let’s keep this in our toolkit while preparing for the DP-203 exam and beyond.
Practice Test
True or False: Skew in data occurs when the data distribution is uniform.
- True
- False
Answer: False.
Explanation: Skew in data refers to a situation where data is not uniformly distributed but is biased or leans towards one direction.
True or False: Skewed data can affect the performance of Azure Data Lake Storage Gen
- True
- False
Answer: True.
Explanation: Skew in data results in uneven distribution of data which in turn can affect data processing and performance of Azure Data Lake Storage Gen
True or False: Over partitioning data in Azure Synapse Analytics can help handle skewness in data.
- True
- False
Answer: False.
Explanation: Over partitioning data may lead to smaller partitions that can create performance overhead. Therefore, it is not a recommended method for handling data skew.
Which Azure tool can be used to handle skew in data?
- a) Azure Synapse Analytics
- b) Azure Data Lake Storage Gen2
- c) Azure Machine Learning
- d) Azure Purview
Answer: a) Azure Synapse Analytics.
Explanation: Azure Synapse Analytics, with its advanced analytics capabilities, can be used to handle skew in data.
True or false: Understanding the source of the skew in data is not essential in handling it.
- True
- False
Answer: False.
Explanation: It’s necessary to understand the source of skew in data to select the right technique to handle it and improve data quality.
In Microsoft Azure, which of the following may contribute to skew in data?
- a) Poor data planning
- b) High data cardinality
- c) Over partitioning
- d) Bad machine learning model
Answer: a) Poor data planning, b) High data cardinality, and c) Over partitioning.
Explanation: Poor data planning, high data cardinality, and over partitioning may contribute to skew in data.
True or False: Skewness can negatively impact the output of machine learning models.
- True
- False
Answer: True.
Explanation: Skewness in input data can bias the output of machine learning models, leading to incorrect predictions or results.
In the context of data skew, what does cardinality refer to?
- a) The number of partitions in the data
- b) The frequency of unique values in the data
- c) The size of the data set
- d) The quality of the data
Answer: b) The frequency of unique values in the data.
Explanation: In this context, cardinality refers to the frequency of unique values in the data. High cardinality may contribute to data skew.
True or False: Partitioning and bucketing can help handle data skew.
- True
- False
Answer: True.
Explanation: Correct partitioning and bucketing can help distribute data more evenly, thus handling data skew.
Which of the following is not a good method to manage data skew in Microsoft Azure?
- a) Increasing the number of partitions indiscriminately
- b) Educating the data owners about the importance of data quality
- c) Using data cleaning processes to remove outliers
- d) Using Azure Synapse Analytics tools for advanced analytics
Answer: a) Increasing the number of partitions indiscriminately.
Explanation: Increasing the number of partitions indiscriminately can lead to small partitions and performance overhead.
Interview Questions
What is skew in data?
Skew in data refers to the asymmetry in a frequency distribution. In statistics, if the distribution is not symmetrical, then it’s defined as skewed. It can be positive or negative, which indicates the side towards which the distribution is extended.
Which is the correct skew type if the data is concentrated on the left?
If the data is concentrated on the left, it means the skew is negative or left skewness. The left tail of the distribution is longer or fatter.
What is over-sampling in the context of handling skew in data?
Over-sampling is a technique used to handle skew in data. It involves adding more examples from the minority class in the dataset to improve the balance of the classes. However, this technique can lead to overfitting.
How does transforming data help manage skewness in data?
Transforming data can help to normalize the distribution and reduce the skewness. Some transformations like the logarithmic or square root transformations can be useful in recalibrating data to a normal or less skewed distribution.
What is under-sampling in the context of handling skew in data?
Under-sampling is a technique used to handle skew data. It involves removing some observations of the majority class to balance with the minority class. The downside is that it can lead to the loss of important information.
What is a bimodal distribution in relation to skewness in data?
A bimodal distribution is a probability distribution with two different modes, which appear as distinct peaks in the probability density function. This can occur in the presence of skew in data but it is not a measure of skewness itself.
Name two key methods to handle skew in data?
Two key methods to handle skew in data are data transformation and resampling (which includes techniques like over-sampling and under-sampling).
How does Azure Synapse Analytics handle skew in data?
Azure Synapse Analytics uses techniques like data partitioning and indexing to handle skew in data. It can redistribute data to manage large workloads and avoid data skew impacts.
What is the first step for handling skew in data?
The first step in handling skew in data is identifying the skew through visualization or statistical methods to understand whether the skew is positive or negative.
Why is it important to handle skew in data?
It is important to handle skew in data because skew could negatively influence the predictive modelling and might result in problematic or incorrect forecasts. Handling skewness ensures a better and more accurate machine learning model.
How does Azure Data Factory handle skew in data?
Azure Data Factory handles skew in data by allowing users to partition the data during data movement activities which can help in managing data skew by evenly distributing the data.
What is data skew in the context of distributed systems?
In distributed systems, data skew refers to the uneven distribution of data across the system. This can occur when certain nodes end up possessing or processing more data than others, potentially leading to inefficiencies or imbalances in the system.
How is skew different from outliers in data?
Skewness refers to the asymmetry in the overall shape of a distribution while outliers are individual data points that fall a long way outside of the expected range of a dataset. A dataset can be skewed with or without outliers.
How can Synthetic Minority Over-sampling Technique (SMOTE) help with skew in data?
Synthetic Minority Over-sampling Technique (SMOTE) is a technique that can handle skew in data by creating and adding synthetic instances of the minority class to the dataset. This technique can help to make a dataset more balanced and less skewed.
What is the relation between Normalization or Standardization techniques and skewness in data?
Normalization or Standardization techniques transform data into a standard scale. While they do not directly handle skewness, they might help in reducing the impact of skewness by transforming skewed data into a more familiar or useful scale.
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