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How does Bitcoin work?

Bitcoin is a cryptocurrency using the blockchain technology, which I discussed here. It’s completely digital and has no central bank controlling it. Based on the previous article, we now understand what blockchain is & how it works. But it’s probably not clear how that technology relates to a cryptocurrency. The components of a block We […]

Blockchain

An introduction to blockchain – hashes, pointers & blocks

Right now, Cloud infrastructure and big data are two of the most in demand technology areas. I think that blockchain will be next. Let’s look at how blockchain works & some potential use-cases. Before we get into what blockchain is exactly, we need to understand a few terms. First, a hash. A hash function converts […]

Python

An introduction to digital signatures & asymmetric encryption in Python

This article will give you an introduction into asymmetric encryption using RSA. Asymmetric encryption uses two keys: a public key and a private key. The public key, can be provided to anyone, the private key should be kept for your own records. Asymmetric encryption is used heavily online. It’s used to send sensitive information from […]

Spark

Managing small file issues when writing to Hive with Spark SQL

Hive sits on top of HDFS (the Hadoop Distributed File System). It reads the files that reside onto HDFS into a specified schema (column names & types), which we can then query and interact with. One such way we may interact with Hive is using Apache Spark. When we write to HDFS, we often end […]

training a random forest machine learning model

An end to end Random Forest Classifier implementation guide

In this article, we are going to go through some of the key steps to implementing a random forest machine learning model. The data we will be using is from Kaggle – it’s a dataset describing whether patients have diabetes or not. Luckily, this dataset is nice and clean, so we don’t need to worry […]

Getting started with API development in Python

ML, Python

Expose your ML model via a simple API in Python

As data scientists, it is important that we have a method of sharing the insight from our models. In this post, I am going to show you how to create a super simple API, whereby the customer can pass URL parameters to extract data, generated by a Python function. Before we get started, make sure […]

hive

Parameters to make your Hive queries perform better

Hive, in my experience, is a platform which can have extremely variable performance, which can make it difficult to predict how long your jobs are going to take to run. Below are a few key optimisation techniques we can use to help make our lives a bit better! Choose the right file type First of […]

Data, hive

Keeping your Hive queries clean with CTEs

This is a super short & quick article about keeping your queries as readable and performant as possible by using CTEs. When we’re working with a number of different datasets it is really very temptying to use subqueries. However, when your queries start to get very large, this can become difficult to manage with a […]

Data, hive, ML

Working with dates in Apache Hive

Working with dates is one of those tedious things we frequently come across as data engineers. The frustration is that there are simply tonnes of date formats. Let’s list a few: Format Example MM/dd/yy 11/01/21 dd/MM/yy 01/11/21 yy/MM/dd 21/11/01 d/MM/yy 1/11/21 (no leading zeros) MMddyy 110121 ddMMyy 011121 yyyyMMdd 20211101 yyyy-MM-dd HH:mm:ss.SSS 01-11-2021 10:45:12.084 yyyy-MM-dd […]

Spark

Improving performance when calculating percentiles in Spark

Performance is a major concern when you’re working in a distributed environment with a massive amount of data. I’ve discussed Spark performance in quite a lot of detail before here and here. Today I am going to talk specifically about percentiles. Because, calculating percentiles over large distributed datasets is a mammoth task. You will likely […]

time series seasonal decomposition in python

Seasonal decomposition in timeseries analysis: additive vs multiplicative seasonality

Timeseries Decomposition is a mathematical procedure which allows us to transform our single timeseries into multiple series. These help us to extract seasonality information and trend easily. Doing this in Python is quite a simple task, I have outlined it below. However, before we get into that, we need to understand the difference between additive […]

An introduction to timeseries models (AR, MA, ARMA and ARIMA)

Timeseries forecasting is quite a big topic to cover. I’ve spoken about key terminology and exponential smoothing in this article and I’ve spoken about how we might remove timeseries outliers here. In this post, I am going to discuss the different components of the ARIMA model (AR and MA), in addition to the ARIMA model […]