Kodey | Data Science | Data Engineering | Machine Learning
Data Engineering & Data Science For Everyone | Python Code | PySpark | Machine Learning | Statistics | Predictive Analytics | Data VIsualization | Time Series Analysis | NLP
Melting dataframes is the process of taking a short, wide table and making it into a long thin one, using column headings as categorical data within the resulting dataframe. In the below example we have a dataframe which shows the total kilometres walked and cycled per person. NAME BIKEKM WALKKM Kieran 77 178 Bobby 79 […]
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Notebooks make it easy to test our code on the fly and check the output dataframe looks correct. However, it is good practice to run some unit tests with some edge cases – things you may not see very often & may not be in your sample data and it’s also important to check that […]
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Testing is one of the most painful parts of data engineering, especially when you have a really huge data set. But it is an absolutely necessary part of every project, as without it, we can’t have complete confidence in our data. We can write unit tests using libraries like PyTest, which I will cover in […]
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Most of our time as data engineers is spent cleaning up data. We have to deal with null values; duplicated rows; weird date formats; inconsistently capitalised words; accents (like the Spanish tilde in words); outliers and other nasty stuff. So, in this post we’re going to cover the below: Handling null values Handling duplicated data […]
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Whenever you start poking around a dataset to see what you’ve got to play with, you probably immediately write ‘import pandas as pd’ – why? Because Pandas is the gold standard in data analysis libraries; it’s so simple & yet so powerful. The problem is, Pandas just doesn’t scale, so if you’re going to be […]
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I mentioned in a previous article that for performance reasons, you should avoid the use of UDF’s wherever possible. And while that statement still stands, if you absolutely must use a UDF, you should consider a Pandas UDF rather than those that come out of the box with Spark. The standard UDF’s in Spark operate […]
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Data serialisation plays a critical role in the performance of our data analytics scripts. In this article, we’ll discuss serialisation as a whole & then dive into Kryo serialisation in PySpark. What is serialisation? Often, we store our data in arrays, tables, trees, classes and other data structures, which are inefficient to transport. So, we […]
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When we ingest data into Spark it’s automatically partitioned and those partitions are distributed across different nodes. So, imagine we have bought some data into Spark, it might be split across partitions like this: Partition 1 Partition 2 Partition 3 1, 7, 5, 9, 12 15, 21, 12, 17, 19 45, 32, 25, 99 When […]
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In the last article on performance tuning Apache Spark, we spoke about caching, UDF’s and limiting your input data, which are all simple ways to see some potentially drastic performance improvements. In this post, we’re going to talk about the environmental factors that you can control to overcome some of the more common Spark issues. […]
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Apache Spark provides us with a framework to crunch a huge amount of data efficiently by leveraging parallelism which is great! Tweaking it can be a bit of an art which is not so great! In this article, I will talk about some of the ways that I have successfully tuned the performance of my […]
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