Random sampling is a technique in which each sample has an equal probability of being chosen. A sample chosen randomly is meant to be an unbiased representation of the total population.
In the big data world, we have an enormous total population: a population that can prove tricky to truly sample randomly. Thankfully, Hive has a few tools for realizing the dream of random sampling in the data lake. Continue reading →
Creating Hive tables is a common experience to all of us that use Hadoop. It enables us to mix and merge datasets into unique, customized tables. And, there are many ways to do it.
We have some recommended tips for Hive table creation that can increase your query speeds and optimize and reduce the storage space of your tables. And it’s simpler than you might think. Continue reading →
Today we’ll briefly showcase how to join a static dataset in Spark with a streaming “live” dataset, otherwise known as a DStream. This is helpful in a number of scenarios: like when you have a live stream of data from Kafka (or RabbitMQ, Flink, etc) that you want to join with tabular data you queried from a database (or a Hive table, or a file, etc), or anything you can normally consume into Spark. Continue reading →
ORC, or Optimized Row Columnar, is a popular big data file storage format. Its rise in popularity is due to it being highly performant, very compressible, and progressively more supported by top-level Apache products, like Hive, Crunch, Cascading, Spark, and more.
I recently wanted/needed to write ORC files from my Spark pipelines, and found specific documentation lacking. So, here’s a way to do it. Continue reading →