Tag

distributed

Julia Crash Course: Dictionaries

An excerpt from Julia as a Second Language by Erik Engheim

This article covers:

·   Storing values on keys in dictionaries.

·   Working with pair objects.

·   Using tuples to create dictionaries.

·   Comparing dictionaries and arrays.

Read it if you’re interested in the Julia language or in how it handles dictionaries.

The Principles of the Data Mesh Architecture

An excerpt from Data Mesh in Action by Jacek Majchrzak, Sven Balnojan, and Marian Siwiak

This excerpt explores the four principles of the Data Mesh as a concept.

Read this article if you are interested in learning what a Data Mesh is and how it is used.

A Deep Learning System from an Engineer’s Perspective

From Engineering Deep Learning Systems by Chi Wang and Donald Szeto

This article presents what prospective readers can expect to learn from this book and why you should learn it.

Read it if you’re a software developer interested in transitioning your skills to the field of deep learning system design or an engineering-minded data scientist who want to build more effective delivery pipelines.

Robust Machine Learning with ML Pipelines

From Data Analysis with Python and PySpark by Jonathan Rioux

This chapter covers using transformer and estimators to prepare data into ML features.

Why Should You Program with Julia?

An excerpt from Julia as a Second Language by Erik Engheim

This article covers:

What type of problems Julia solves.
The limits of statically-typed languages.
Why the world needs a fast dynamically-typed language.
How Julia increases programmer productivity.

Read it if you’re interested in the Julia language and its strengths and weaknesses.

What Is a Data Mesh and What Is It Used for?

An excerpt from Data Mesh in Action by Jacek Majchrzak, Sven Balnojan, and Marian Siwiak

This excerpt covers

●      What is a “Data Mesh”? Our definition of a Data Mesh

●      What are the key concepts of the Data Mesh paradigm?

●      What are the advantages of the Data Mesh?

Big Data is Just a Lot of Small Data: using pandas UDF, part 2

From Data Analysis with Python and PySpark by Jonathan Rioux

This article covers

·         Using pandas Series UDF to accelerate column transformation compared to Python UDF.

·         Addressing the cold start of some UDF using Iterator of Series UDF.

Big Data is Just a Lot of Small Data: using pandas UDF

From Data Analysis with Python and PySpark by Jonathan Rioux

This article covers

·   Using pandas Series UDF to accelerate column transformation compared to Python UDF.

·   Addressing the cold start of some UDF using Iterator of Series UDF.

Your Data under a Different Lens: window functions

From Data Analysis with Python and PySpark by Jonathan Rioux

This article covers window functions and the kind of data transformation they enable.

Introducing Edge Computing

From Making Sense of Edge Computing by Cody Bumgardner

Conceptually, edge computing is concerned with when it’s best to migrate computational functionally toward source of data and when it is best to move the data itself. This abstract concept of function versus data migration drives not only the fundamental motivations of edge computing, but also the broader field of distributed systems. The act of distributing processes makes even the simplest tasks more complicated.

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