Data is growing faster than ever. Various sources of data are public web, social media, business applications, data storage, machine log data, sensor data, archives, documents, and media, and the sources are growing. Big data analytics is the process of examining large amount of data to determine hidden patterns, unknown correlations and useful information that can be used for making better decisions.
The ultimate aim of big data analysis is to help the organizations make improved business decisions by enabling data scientists, predictive modellers, and analytics professionals to analyse Big Data. Hadoop & Spark the two big data framework have become the dominant paradigm for Big Data processing, and several facts have become clear. Although, they do not conduct exactly the same tasks, and they are not mutually unique, they are able to work together. Additionally, Spark is reported to work up to 100 times faster than Hadoop in certain circumstances, as it does not provide its own distributed storage system.
So what exactly are Hadoop & Spark?
Apache Spark is considered as a robust foil to Hadoop, Big Data’s original technology of choice. Spark is easily manageable, strong and capable Big Data tool for tackling various Big Data challenges.
It is built on top of Hadoop MapReduce and it extends the MapReduce model to efficiently use more types of computations which includes Interactive Queries and Stream Processing.
The main feature of Spark is its in-memory cluster computing that increases the processing speed of an application.
Apache Spark Architecture is based on two main abstractions–
- Resilient Distributed Datasets (RDD)
- Directed Acyclic Graph (DAG)
Apache Hadoop, is a known software framework enabling distributed storage and processing of large datasets using simple high-level programming models. Hadoop is pretty commonly used and is known for being a safe Big Data framework based on a lot of, mostly open source algorithms and programs.
Hadoop is built on four fundamental modules, precise parts of framework that carry out different essential tasks for computer systems meant for Big Data analysis.
- Distributed File systems
- Hadoop Common
Besides these four core modules, there is a plethora of others, but for full deployment, these four are essential. Hadoop represents a very solid and flexible Big Data framework.
Let’s see how Hadoop & Spark are fast becoming the next big thing in Big Data.
1. Spark makes advanced analytics innovative
Spark delivers a framework for advanced analytics right out of the box. This framework includes a tool for accelerated queries, a machine learning library, a graph processing engine, and a streaming analytics engine. As opposed to trying to implement these analytics via MapReduce, which can be nearly impossible even with hard-to-find data scientists, Spark provides prebuilt libraries that are easier and faster to use.
2. Spark provides acceleration at its best
As the pace of business continues to accelerate, the need for real-time results continues to grow. Spark provides parallel in-memory processing that returns results many times faster than any other approach requiring disk access. Instant results eliminate delays that can significantly slow incremental analytics and the business processes that rely on them.
Hadoop on the other hand, is like an old sturdy warrior. It is one of the most used data storing and processing systems and is used by some of the corporate giants in various different markets.
3. Hadoop saves you money
Hadoop serves as low-cost Big Data processing framework. Hadoop is relatively cost-effective because of its seamless scaling capabilities. Hadoop is quite scalable as it distributes very large data sets amongst inexpensive servers. It relies on parallel operations, and this makes it quite profitable.
4. Hadoop is future-proof
Hadoop is simply fault resistant. When it sends data to a particular node in a cluster, it allows for the sent data to be replicated to other nodes in the cluster. So, when the data sent to the node, somehow gets lost, or destroyed, there is a copy available on the other node that can be used.
The general perception is that what makes Spark stand out when compared to Hadoop is its speed. While Hadoop focuses on switching and transferring data through hard disks, Spark runs its operations through memory. Working through logical RAM increases the speed quite significantly, so Spark can handle data analysis faster than Hadoop. Both frameworks have their own advantages and choosing the best can only be dependent on what are you looking for.
We at Nitor are proud to help organizations capitalize on the tremendous potential of Hadoop and Spark. We help you manage and secure your data to derive solid, measurable, data-backed recommendations.
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