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Installing Hadoop on the new M1 Pro and M1 Max MacBook Pro

MacBook Pro

In the previous series of posts, I wrote about how to install the complete Hadoop stack on Windows 11 using WSL 2. And now that the new MacBook Pro laptops are available with the brand new M1 Pro and M1 Max SOCs, here’s a guide on how to install the same Hadoop stack on these laptops. Read more... “Installing Hadoop on the new M1 Pro and M1 Max MacBook Pro”

Installing Hadoop on Windows 11 with WSL2


In the previous post, we saw how to install a Linux distro on Windows 11 using WSL2 and then how to install Zsh and on-my-zsh to make the terminal more customizable. In this post, we’ll see how we can install the complete Hadoop environment on the same Windows 11 machine using WSL. Read more... “Installing Hadoop on Windows 11 with WSL2”

Understanding Apache Hive LLAP

apache hive

Apache Hive is a complex system when you look at it, but once you go looking for more info, it’s more interesting than complex. There are multiple query engines available for Hive, and then there’s LLAP on top of the query engines to make real-time, interactive queries more workable. Read more... “Understanding Apache Hive LLAP”

Installing Zsh and Oh-my-zsh on Windows 11 with WSL2


Before we begin, you might ask, why am I writing on something this trivial? I sold off my old MacBook Pro because I’m super excited about the new M1 Pro MacBook Pros. I have pre-ordered one of those and am waiting for it to come. Read more... “Installing Zsh and Oh-my-zsh on Windows 11 with WSL2”

Querying Hive Tables From a Spring Boot App

In this post, we’ll see how we can query tables that reside in Hive using a Spring Boot application. As always, I’m going to use a Spring Boot web app with a few GET APIs to show how we can query data from Hive. Read more... “Querying Hive Tables From a Spring Boot App”

How To Generate Parquet Files in Java

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Parquet is an open source file format by Apache for the Hadoop infrastructure. Well, it started as a file format for Hadoop, but it has since become very popular and even cloud service providers such as AWS have started supporting the file format. Read more... “How To Generate Parquet Files in Java”

Getting Started with Apache Drill and MongoDB

Not a lot of people have heard of Apache Drill. That is because Drill caters to very specific use cases, it's very niche. But when used, it can make significant differences to the way you interact with data. First, let's see what Apache Drill is, and then how we can connect our MongoDB data source to Drill and easily query data. What is Apache Drill? According to their website, Apache Drill is "Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage." That's pretty much self-explanatory. So, Drill is a tool to query Hadoop, MongoDB, and other NoSQL databases. You can write simple SQL queries that run on the data stored in other databases, and you get the result in a row-column format. The

Connect Apache Spark to your HBase database (Spark-HBase Connector)


There will be times when you’ll need the data in your HBase database to be brought into Apache Spark for processing. Usually, you’ll query the database, get the data in whatever format you fancy, and then load that into Spark, maybe using the `parallelize()`function. This works, just fine. But depending on the size of the data, this could cause delays. At least it did for our application. So after some research, we stumbled upon a Spark-HBase connector in Hortonworks repository. Now, what is this connector and why should you be considering this? The Spark-HBase Connector (shc-core) The SHC is a tool provided by Hortonworks to connect your HBase database to Apache Spark so that you can tell your Spark context to pickup the

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