Mark Needham

In my continued exploration of Apache Pinot I’ve been trying out the GitHub events recipe , which imports data from the GitHub events stream into Pinot.

As I mentioned in my last blog post, I’ve been playing around with Apache Pinot, a data store that’s optimised for user facing analytical workloads.

I’ve recently been playing around with Apache Pinot, a realtime analytical data store that’s used for user facing analytics use cases.

I recently started a new job, working for a FinTech company called Finbourne, who build a data platform for investment data.

Usually when I’m working with Pandas DataFrames I want to add new columns of data, but I recently wanted to add a row to an existing DataFrame.

I ran into an interesting problem when trying to use Altair to visualise a Pandas DataFrame containing vaccination rates of different parts of England.

I’m still playing around with Covid vaccine data, this time exploring how the number of doses varies week by week.

Over the last 13 months I’ve spent countless hours looking at dashboards that showed Coronavirus infection rates, death rates, and numbers of people vaccinated.

I’m still playing around with the UK’s COVID-19 vaccination data and in this blog post we’ll learn how to format a DataFrame that contains a mix of string and numeric values.

I’ve been doing some more exploration of the UK Coronavirus vaccine data, this time looking at the number of people vaccinated by Local Tier Local Authority.

One of the Altair charts on my Covid Vaccine Dashboards Streamlit app shows the % of first doses, but when I first created it there was some padding on the X axis that I wanted to remove.

The UK government publishes Coronavirus vaccinations data on coronavirus.data.gov.uk, but I wanted to create some different visualisations so I downloaded the data and have been playing with it in the mneedham/covid-vaccines GitHub repository.

The Neo4j Graph Data Science Library provides efficiently implemented, parallel versions of common graph algorithms for Neo4j, exposed as Cypher procedures.

The Neo4j Graph Data Science Library provides efficiently implemented, parallel versions of common graph algorithms for Neo4j, exposed as Cypher procedures.

This is another post describing my exploration of Materialize, a SQL streaming database.

This is another post describing my exploration of Materialize, a SQL streaming database.

In a blog post I wrote a couple of weeks ago, we learned how to analyse JSON files using the Materialize SQL streaming database.

jq, the command-line JSON processor, is my favourite tool for transforming JSON documents.

In my continued playing around with the Strava API, I wanted to write a script to download all of my Strava activities to a JSON file.

Whenever I’ve writing scripts that rely on credentials defined as environment variables, I like to create a .env (or equivalent) file containing those variables.

I recently learnt about Materialize, a SQL streaming database, via their Series B fundraising announcement, and thought I’d take it for a spin.

I’m revisiting the Strava API after a two year absence and the approach to authenticating requests has changed in that time.

I recently wanted to create a graph based on an adjacency list, and in this post we’ll learn how to do that using the FOREACH clause and then with the new CALL {} subquery clause.

I recently found myself needing to extract the file name but not file extension from a bunch of file paths and wanted to share a neat technique that I learnt to do it.

I’ve been trying to install pipenv on a new computer and ran into a frustrating issue.

I keep track of the podcasts that I’ve listened to in a Google Doc, having pasted the episode title and podcast name from Player.FM.

One of the most popular use cases for Neo4j is knowledge graphs, and part of that process involves using NLP to create a graph structure from raw text.

In this post we’re going to learn how to filter a Python map/dictionary to return a subset of keys or values.

It’s been several months since our last QuickGraph and the world feels very different than it was back then.

In this post we’re going to learn how to find the dates of all the Sundays in a given month, as well as the Sunday immediately preceding the 1st day in the month, assuming that day isn’t a Sunday.

I’ve been building a little React app that uses the Semantic UI library and found myself wanting to render a custom icon.

In this post we’ll learn how to overcome a problem I encountered while building a small Streamlit application to analyse John Hopkin’s data on the COVID-19 disease.

This is part 4 of my series of posts about querying the Wikidata API, in which I learn how to use SPARQL’s UNION clause to handle an OR condition in a WHERE clause.

This is the third post in a series about querying Wikidata’s SPARQL API.

A couple of weeks ago I wrote a QuickGraph blog post about the Australian Open, in which I showed how to use Neo4j 4.0’s multi database feature.

In this blog post we’re going to build upon the newbie’s guide to querying Wikidata, and learn all about the CONSTRUCT clause.

One on my favourite things about storing data in a graph database is executing path based queries against that data.

After reading one of Jesús Barrasa’s recent QuickGraph posts about enriching a knowledge graph with data from Wikidata, I wanted to learn how to query the Wikidata API so that I could pull in the data for my own QuickGraphs.

Before the release of Neo4j 4.0, taking a dump of a database running within a Docker container was a tricky affair.

As part of the preparation for another blog post, I wanted to export a subset of data from one Neo4j database to another one, which seemed like a blog post in its own right.

It’s time for another QuickGraph, this one based on data from the Australian Open tennis tournament.

I recently came across Streamlit, a tool that makes it easy to build data based single page web applications.

In my continued experiments with the Altair visualisation library, I wanted to set a custom range of data values on the x axis of a chart.

I’ve been playing with the Altair statistical visualisation library and recently ran into an error while trying to render a DataFrame that contained dates.

For our first QuickGraph of the new decade we’re going to explore data from the Official UK Top 40 Chart.

I wanted to enrich the data for a little music application I’m working on and realised it would be a perfect opportunity to try out the Spotify API.

As someone who’s allergic to lots of different things, the introduction of allergen charts in restaurants over the last few years has been very helpful.

Over the last week the Guardian have been counting down their top 100 male footballers of 2019, and on Friday they also published a Google sheet containing all the votes, which seemed like a perfect candidate for a QuickGraph.

I’ve been working on a React Reach Router based application that has several routes and wanted to show a search box in the header unless the user was on the search page.

For a side project that I’m working on I wanted to create a small React application that can query data stored in Elasticsearch, and most of the tutorials I found suggested using a tool called Elastic App Search.