Apache Hadoop is 100% open source, and pioneered a fundamentally new way of storing and processing data. Instead of relying on expensive, proprietary hardware and different systems to store and process data, Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits. With Hadoop, no data is too big. And in today’s hyper-connected world where more and more data is being created every day, Hadoop’s breakthrough advantages mean that businesses and organizations can now find value in data that was recently considered useless.

Apache Hadoop is an open source framework for distributed storage and processing of large sets of data on commodity hardware. Hadoop enables businesses to quickly gain insight from massive amounts of structured and unstructured data. It is a set of algorithms (an open-source software framework written in Java) for distributed storage and distributed processing of very large data sets (Big Data) on computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures (of individual machines, or racks of machines) are commonplace and thus should be automatically handled in software by the framework.

Key Features

1)Scalable-

Hadoop is a highly scalable storage platform, because it can store and distribute very large data sets across hundreds of inexpensive servers that operate in parallel. Unlike traditional relational database systems (RDBMS) that can’t scale to process large amounts of data, Hadoop enables businesses to run applications on thousands of nodes involving thousands of terabytes of data.

2)Cost effective-

Hadoop also offers a cost effective storage solution for businesses’ exploding data sets. The problem with traditional relational database management systems is that it is extremely cost prohibitive to scale to such a degree in order to process such massive volumes of data. In an effort to reduce costs, many companies in the past would have had to down-sample data and classify it based on certain assumptions as to which data was the most valuable. The raw data would be deleted, as it would be too cost-prohibitive to keep. While this approach may have worked in the short term, this meant that when business priorities changed, the complete raw data set was not available, as it was too expensive to store. Hadoop, on the other hand, is designed as a scale-out architecture that can affordably store all of a company’s data for later use. The cost savings are staggering: instead of costing thousands to tens of thousands of pounds per terabyte, Hadoop offers computing and storage capabilities for hundreds of pounds per terabyte.

3)Flexible

Hadoop enables businesses to easily access new data sources and tap into different types of data (both structured and unstructured) to generate value from that data. This means businesses can use Hadoop to derive valuable business insights from data sources such as social media, email conversations or clickstream data. In addition, Hadoop can be used for a wide variety of purposes, such as log processing, recommendation systems, data warehousing, and market campaign analysis and fraud detection.

4)Fast

Hadoop’s unique storage method is based on a distributed file system that basically ‘maps’ data wherever it is located on a cluster. The tools for data processing are often on the same servers where the data is located, resulting in much faster data processing. If you’re dealing with large volumes of unstructured data, Hadoop is able to efficiently process terabytes of data in just minutes, and petabytes in hours.

5)Resilient to failure

A key advantage of using Hadoop is its fault tolerance. When data is sent to an individual node, that data is also replicated to other nodes in the cluster, which means that in the event of failure, there is another copy available for use.

The MapR distribution goes beyond that by eliminating the NameNode and replacing it with a distributed No NameNode architecture that provides true high availability. Our architecture provides protection from both single and multiple failures.

When it comes to handling large data sets in a safe and cost-effective manner, Hadoop has the advantage over relational database management systems, and its value for any size business will continue to increase as unstructured data continues to grow.

6)Distributed Metadata

The default Hadoop architecture uses a single NameNode to store the metadata. This forces all data into a bottleneck, and limits clusters to 50-200 million files. It also creates a single point-of-failure (SPOF). If the NameNode were to fail, the entire cluster would be useless.

Other distributions try to sidestep the problem by using a secondary NameNode. Secondary NameNodes run as a slave to the primary NameNode, and only replicate data from it on a periodic basis. This means that those depending on a secondary NameNode cannot trust its data integrity.

The only real solution to the NameNode problem is to remove it. With the MapR Distribution no-NameNode solution, there are no practical limits to the number of files that can be stored on MapR. This foundational change in the Hadoop architecture distributes the metadata amongst several nodes, which is illustrated below.

In addition to its benefits for dependability, its database performance boost is also remarkable. With only commodity hardware, you can gain 10-20 times the performance over all other distributions that utilize the centralized metadata structure.

This feature is an architectural improvement to Hadoop that MapR initiated in its infancy. The power it adds to our offering’s dependability and performance makes it untouchable by competitive offerings.

7)Low Latency

Your Hadoop infrastructure needs to be fast. Equally as important, it needs to stay that way. A dirty secret among many Hadoop distributions is the staggering volatility in performance and latency. The MapR M7 disk strategy obviates compactions and defragmentation that can affect performance. Because of this ability, MapR M7 achieves 5x better performance, with low 95th and 99th percentile latencies. The graph below compares the high performance and consistent low latency of the MapR M7 Edition in comparison to other Hadoop distributions.

8)High Availability

High availability (HA) refers to the capability of a Hadoop system to continue functioning, regardless of multiple system failures. For companies running mission-critical applications, HA is a necessity.

The best way to ensure that your distributed system is highly available is by using an architecture that distributes the metadata. The MapR architecture increases performance and removes the SPOF.

The MapR Distribution for Hadoop provides high availability with self-healing and support for multiple failures. This means that your Hadoop infrastructure will be accessible during system failures, system upgrades and data recoveries.

Snapshots

Other distributions use the HDFS snapshot system, which has several downsides when compared to the MapR Distribution for Hadoop:

9)True Point-In-Time

HDFS snapshots only capture data that is closed at the time the snapshot is taken. If you are using snapshots as an automated recovery system, you will have no guarantees that the data is complete. With MapR, you can perform point-in-time recovery of all files and tables, whether they are open or not.

10)Supports All Applications

MapR Snapshots support all Hadoop applications by default.

11)No Data Duplication

MapR snapshots never duplicate your data and share the same storage with your live information. This allows clients to capture snapshots of a 1 petabyte cluster in just seconds.