Scaling Apache Spark is typically the last step before executing a Spark-dependent workflow. In previous articles, we introduced Spark, and showed how to optimize it. Once correctly optimized, scaling Apache Spark becomes trivial. To demonstrate, we return to the NYC taxi dataset originally described here. As of 2019, this dataset contains about 1.5 billion anonymized [...]
One often sees questions in forums asking why, for a particular Spark job, certain configurations outperform others. A naive understanding of Spark might imply that increasing the number of executors or increasing the cores per executor will lead to faster job completions. This is wrong. In this post, we show how to optimize Apache Spark. Faster execution [...]
INTRODUCTION Algorithmic trading gives algorithms (computer programs) the discretion to make trading decisions regarding stock selection, order sizing and order placement. Any analytical technique used to drive trading strategies is a quantitative strategy, therefore algorithmic trading is a subset of quantitative trading. A main goal of algorithmic trading is to eliminate the human element from [...]
You are standing on the side of a steep mountain. You need to descend to the base of the mountain as quickly as possible. Remarkably, this scenario illustrates a central concept in machine learning. But let's get back to the mountain. I'd imagine that the first thing you would do, almost intuitively, would be to [...]
What is machine learning? I could bore you with textbook definitions. Instead, let me use a familiar example. A few days ago, I was teaching a child how to count. This is what transpired: Child: 1, 2, 3, 4, 5, 3, 8... Me: Stop. 1, 2, 3, 4, 5 is correct, but what comes after [...]