reorchestrate

This post is the continuation of Your binary is no longer safe: Decompilation about the brute-force reverse engineering of binary (compiled) programs using Large Language Models (LLMs) to automate this two-part problem: decompilation and conversion to a m…

This post is about the brute-force reverse engineering of binary (compiled) programs using Large Language Models (LLMs) to automate this two-part problem: decompilation and conversion to a modern programming language.

What is SQLite?

JSON Web Tokens (JWT) are data structures that allow the holder of the token to assert claims that are able to be cryptographically verified.

Plugins are a useful way to allow advanced users to add new functions your software without having to modify the main program itself.

Debezium is a Database Change-Data-Capture (aka CDC) tool that is able to decode open source and proprietary database logs, normalize them to a standard payload format and push them into a series of Kafka topics.

Why Blob Storage Is Risky Many people use Amazon S3 or equivalent services to replace their relational database data warehouses as blob storage coupled with technologies like the compressed-columnar parquet file format offers a reasonably performant, mass…

History of the problem Since Hadoop was released in 2007 users have been struggling to use it to deploy reliable and scalable Extract-Transform-Load (ETL) data pipelines.

One of the famous machine learning challenges is the performing handwritten character recognition (classification) over the MNIST database of handwritten digits.

Whilst playing with the MNIST dataset I found I needed a way of rotating images and so I decided to build an Affine Transform Transformer for Apache Spark ML.

I wrote this a while ago based on this excellent post and added a few more attributes.

Update: This code has been approved and should appear in Apache Spark 2.0.0.

As I have been playing with Apache Spark ML and needed a stemming algorithm I decided to have a go and write a custom transformer myself.

Continues from Part 1. 4 Execution 4.1 The Pipeline Now we have all the components of the pipeline ready all that is needed is to load them into the Spark ML Pipeline().

The most exciting feature of Apache Spark is it’s ‘generality’ meaning the ability to rapidly take some text data, transform it to a graph structure and perform some network analysis with GraphX take that dataset and apply some machine learning algorithms…

In my previous post I wrote some code to demonstrate how to go from the raw database extracts provided monthly by WikiPedia through to loading into Apache Spark GraphX and running PageRank.

Recently I have spent a lot of time reading and learning about graphs and graph analytics which naturally drew me to Apache Spark GraphX having previously played with Neo4J.