If you’ve been wondering what Hadoop is and why you should be using it, this article by our Guest blogger Jenny Richards is a must read. Jenny is a content marketer for RemoteDBA.com which is one of the leading companies in the country that provides remote DBA support services. In this article you can grasp the basics of its features, and how you can benefit from it.
Apache Hadoop, more commonly referred to as Hadoop, is an open-source framework that is mainly used to process and store big data. Every business that interacts with big data requires software solutions like Hadoop for a number of reasons, but before delving into these, you should know its basic characteristics.
As used today, Hadoop may also refer to the ecosystem, or additional software, users need to have beside or on top of Hadoop to use it effectively, including Apache HBase, Apache Pig, Apache Spark and Apache Hive.
Basic Hadoop Features
Understanding Hadoop features is critical to your understanding of how Hadoop works. Currently, Hadoop remains the most widely used analytics platform for big data, which is why some people think Hadoop to be the only such platform. However, the market is teeming with a number of good alternatives to Apache Hadoop, though the latter still boasts a superior feature set.
The following are some of the most important features of Hadoop that have caused it to lead the big data analytics environment.
Open-source: Hadoop is an open-source program (i.e. not commercial), which means it’s available and can be contributed to free of charge. However, it’s worth mentioning that there are also a number of commercial versions available on the market.
Software framework: Hadoop is more than just a software program – it comes with everything you need not only to develop the software application, but also to run it, including tool sets and connections.
Distributed framework: Hadoop utilizes a distributed framework called Hadoop Distributed File System (HDFS), which means that data will be divided then stored in several computers. Computations can occur laterally in multiple machines, provided they are connected. The distribution gives Hadoop extremely high processing speeds, and allows an enterprise to process millions of data MBs simultaneously at different nodes.
Hadoop Common: This provides the file system level abstractions and OS, and it also contains libraries and utilities for purchasing other models.
Hadoop YARN: This is a resource management solution which is in charge of the resources required to run computing clusters. The clusters are used for user application scheduling.
Hadoop Map Reduce: This is an innovative programming model that the Hadoop framework utilizes to process data.
Locational settings: All Hadoop-compatible file systems offer location information for nodes, such as their network switch identity. Hadoop applications in turn utilize the information to schedule work and eliminate redundancy.
Hadoop clustering: Clusters in Hadoop have individual master nodes serving several ‘worker nodes’. These master nodes also function as NameNodes, TaskTrackers, DataNodes and JobTrackers, while the slave/worker nodes are only Data Nodes and TaskTrackers. With this arrangement, worker nodes can be specialized to handle either data or computing tasks.
Hadoop requirements: Hadoop needs Java Runtime Environment, JRE 1.6 and higher as well as the Secure Shell (SSH) to be set up between clusters and nodes. The SSH is responsible for running routine startup and shutdown scripts.
File system: Hadoop utilizes an FTP file system which stores all enterprise data within remotely accessible FTP servers. This makes it seamlessly possible to enlist Remote DBA Support services to be in charge of managing your Hadoop ecosystem.
AECC Clusters: Clusters which are hosted on Amazon Elastic Compute Cloud (on-demand) utilize the Amazon S3 file system. However, this option does not come with rack awareness.
WASB file system: The Windows Azure Storage Blobs file system provides an extension for HDFS to enable Hadoop distributions to access Azure Blob-stored data without the need to have permanent clusters.
Why you should implement Hadoop for your Big Data
Many of the large data issues within most enterprises can be solved using conventional data analytics approaches, meaning that Hadoop is not always the recommended solution for all big data needs. So, when exactly should you go for Hadoop or alternative distributed data analytics platforms?
Hadoop is without doubt superior to traditional RDBMS approaches for analytics, and here is why.
Massive storage space: the Hadoop framework can handle and store enormous data volumes. This is done my splitting data into blocks, which are then stored in clusters within cheaper hardware.
Scalability: Hadoop offers a cost-efficient solution for enterprises struggling with their large data set storage systems. With traditional RDBMSs, it can be very costly to scale upwards to accommodate enormous data volumes since the data would have to be down-sampled, with assumption-based classifications and then the raw data would be deleted.
Quality: because RDBMS have no raw data, enterprises would have to settle for outputs of compromised quality, without the option of reverting back to the old data should the need for this arise at some point in the future.
Flexibility: with Hadoop, you have both structured and unstructured data options, which allow you to get only the most relevant information from big data sources e.g. clickstream data, email, social media etc.
Failure resilience: where data is stored in a single node, any failure could result in catastrophic effects, but Hadoop has duplicate data stored within different nodes in a cluster.
Perhaps not an advantage in the very definition, it says something for Hadoop that big-name organizations like EBay, Google, Yahoo!, Etsy and Twitter use Hadoop for their big data.
Implementing Hadoop: leave it to the professionals
Companies want their Hadoop implementation to be spearheaded by actual professionals. There are different technologies and files systems merging together, and only trained and experienced personnel would know exactly how to handle them successfully.
Today many companies are enlisting the services of a remote services Hadoop expert, who also offers tips on governance strategies and can be in charge of operational management activities. This takes away the cost of having to hire an in-house Hadoop DBA, who doesn’t come cheap. Companies don’t want to risk their most valuable asset, their big data, by having an untrained person handle the installation, maintenance and management of their Hadoop ecosystem.
So think about that career as a Hadoop expert! You will be much in demand and have a rich, rewarding career ahead of you.