Data Pipelines

Originally published on Efficiently (Substack) This is a continuation of a previous blog post about efficient data partitioning. In the previous post, I discussed how data layout on disk impacts analytics performance. This post focuses on tactical implementation using open source technologies. Topics I’ll cover: HDFS Blocks + Block Size Block sizes + tradeoffs Background Data organization on disk dramatically affects analytics performance. I previously explored row-oriented, columnar, and hybrid storage models — now let’s connect these concepts to modern data infrastructure. ...

Originally published on Efficiently (Substack) A few years ago, I gave a talk at Spark Summit 2020 about File Formats covering Avro, ORC, and Parquet. I received numerous questions about that topic, responding point-to-point, leaving the knowledge confined to those forums alone. That isn’t helpful for most people. This post aims to fix that. In this series, I’ll outline the primitives of this topic and then explore the hands-on details. Problem In the efficiency space, minimizing “work” is key. Whether work requires compute, network, or storage, “the goal of efficient data usage is to get the most accurate answer in the fastest and cheapest way possible.” ...

Originally published on Efficiently (Substack) My name is Vinoo and I’ll be your guide, writer, and (likely) ranter throughout this series. 0. What this is Recently, I had a friend recommend Austin Kleon’s Show Your Work. I read it over the course of a short plane ride and realized… it’s time to start showing my work. Publicly. This blog series, at its core, is a collection of things learned while building analytical tools, creating data products, and consuming data products. Mostly, it’s intended as a technical series for technical audiences, but the direction remains uncertain. ...

Data pipelines are the foundation for success in data analytics, so understanding how they work is of the utmost importance. Join us for four hours of expert-led sessions that will give you insight into how data is moved, processed, and transformed to support analytics and reporting needs. You’ll also learn how to address common challenges like monitoring and managing broken pipelines, explore considerations for choosing and connecting open source frameworks, commercial products, and homegrown solutions, and more. ...

The data pipeline has become a fundamental component of the data science, data analyst, and data engineering workflow. Pipelines serve as the glue that links together various components of the data cleansing, data validation, and data transformation process. However, despite its importance to the data ecosystem, constructing the optimal data pipeline is generally an afterthought - if it’s considered at all. This makes any changes to the central pipeline highly error-prone and cumbersome. With the ever-growing demand for new kinds of data, especially from external vendors, constructing pipelines that are scalable and that allow for monitoring is pivotal for the safe and continued use of data. ...