👋 I'm Lam, a data engineer.

Snowflake is one of the most popular data warehouse solutions nowaday because of the various features that it can provide you to build a complete data platform. Understanding the Snowflake storage layer not only helps us to have a deep dive into how it organizes the data under the hood but it is also crucial for performance optimization of your queries.

I have participated in fews technical interviews and have discussed with people topics around data engineering and things they have done in the past. Most of them are familiar with Apache Spark, obviously, one of the most adopted frameworks for big data processing. What I have been asked and what I often ask them is simple concepts around RDD, Dataframe, and Dataset and the differences between them. It sounds quite fundamental, right? Not really. If we have more closer look at them, there are lots of interesting things that can help us understand and choose which is the best suited for our project.

Spark is an in-memory data processing framework that can quickly perform processing tasks on very large data sets, and can also distribute tasks across multiple computers. Spark applications are memory heavy, hence, it is obvious that memory management plays a very important role in the whole system.

State is a very crucial part of React applications which will help update the information of the React components to change UI accordingly and make our application interactive with clients. In this article, I will walk you through the usage of state, its characteristic, and how we can use state in an efficient way.

In the previous blog, I have successfully installed a standalone Ranger service. In this article, I show you how we can customize the logical plan phase of Spark Catalyst Optimizer in order to archive authorization in Spark SQL with Ranger.