Benefits of Apache\'s Spark, Hive and Hadoop

Hello hello, this is Jordan from SnapStack Solutions, coming to you again with some fresh energy in the new year. I hope you enjoyed the holidays with your closest ones. On behalf of my entire team, I wish you a peaceful mind, a harmonious home, and a successful year! 🙂

I will kick this new year off with some fresh content, but still in a way connected to the previous articles. Just for a reference, last month we talked about the importance of R in data science. As always, I am here to remind you to check it out if you didn’t have the chance to read it. Follow this link here.

Still, I want to cover more on Big data tools, and today I’ll go with three of them that are under the Apache Software Foundation. For those of you who are curious about Apache, it is a nonprofit corporate corporation that supports Apache software projects. It was March 1999, when ASF was initially formed.

However, you may search for yourself more on this topic, while I, on the other hand, will try to cover three Apache technologies, and those are: Spark, Hive, Hadoop.

Let’s see what are they used for, and how important are they for the Big Data as a whole.

Apache Spark

The first one-off is Apache Spark. Most of you may already heard of it, but let’s learn more about this technology. For starters, it is an open-source analytics engine used for big data workloads.

It was born in 2009 at the University of California, Berkeley, while the ones developing it tried to find a way to accelerate the processing jobs in Hadoop Systems.

It is based on Hadoop MapReduce and it provides native bindings for programming languages such as Python, Scala, Java, and R. I cannot go through but to also mention the libraries it includes for Machine Learning – Mllib, stream processing – Spark Streaming, and graph processing – GraphX.

In order to minimize the complexity of the data, the Spark Core Engine is using RDD r Resilient Distributed Dataset. It works in a way that data and partitions are aggregated through a server cluster, where it is handled and stored in another data store, or run through an analytic model.

Benefits of Apache Spark

Speed – Probably the most valuable thing nowadays. The reason why Spark is standing out from the others is that it uses its in-memory engine, making it 100 times faster than MapReduce when ran in-memory and 10 times faster when processed on disk.

Real-time stream – This technology can work with real-time streaming together with the integration of different frameworks.

Many workloads – Spark is able to work with several workloads, such as interactive queries, real-time analytics, machine learning, and graph processing.

Apache Hadoop

Hadoop is another tool that is really important when it comes to this field. It is a collection of open-source software utilities that are made to do computing on a massive amount of data. It processes structured and unstructured data in order to collect, process, and analyze big data.

Just like the previous technology, we will go through the advantages of using Apache Hadoop.

Benefits of Apache Hadoop

Cost-effective – This technology is coming forward with a cost-effective storage solution for massive data sets. In the past, companies would have down-sampled the data and classify it based on different assumptions, just so they can avoid the costs, leaving them with deleted raw data, that afterward would be valuable.

Scalable – Hadoop is a very scalable storage platform and when it comes to storing, it can distribute massive data sets on many cost-efficient servers that work in parallel. It gives the companies the opportunity to handle applications on thousands of nodes, together with thousands of terabytes of data.

Flexible – Companies can use Hadoop to derive valuable analytics from platforms like social media and email conversions. In addition, it can be used for many other activities, like log processing, data warehousing, market campaign analysis, and fraud detection.

Apache Hive

As we are talking about the Hadoop Platform, it is inevitable to mention Hive. So what exactly is Apache Hive?

It is a data warehouse system that is used to summarize, analyze and query massive amounts of data. To better understand this, SQL queries are transformed into different forms, like MapReduce so the activities are reduced to a bigger extent.

Apart from this, the Hive also gives the data a structure that can be stored in a database, so the users can connect to Hive while using a command-line tool or JDBC driver.

Benefits of Apache Hive

Better productivity – This technology is designed for summarizing, querying, and analyzing data. It works for a big range of functions that are interconnected with Hadoop packages like Rhipe, Apache Mahout, and many others.

Cleaner working – Hive includes cleansing, transforming, and modeling data to provide valuable information about different business insights which in the end, benefits the company.

User-friendly – Hive gives the users the opportunity to access the data and increase the response time at the same time. In comparison to other tools, Hive’s response time is way faster.

All in all, we have gone through the basics of these technologies and their advantages. In the world of handling big data, they are playing a crucial part. Of course, there is so much to be said about this, as it is a wide topic, but I’ve tried to bring it closer to you.

Our big data specialists would be more than glad to answer any question you might have. Feel free to check out our social media and get in touch with us. Until next time.

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