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What is Hadoop good for what it is not?

This article mainly explains about advantages and disadvantages of Hadoop. As the pillar of so many implementations, Hadoop is practically synonymous with big data. Offering dispersed storage, higher scalability, and ultimate performance, many people view this as the standard platform for high volume data infrastructures. To learn more about Hadoop, click on Hadoop Certification.


 Advantages of Hadoop

The following are the advantages of Hadoop:

  • Scalable: Hadoop is a highly scalable storage platform, because it can store and distribute large volume of data sets across hundreds of economical servers that perform in corresponding. Unlike traditional relational database systems (RDBMS) that can’t measure to route large amounts of data, Hadoop assists businesses to run applications on thousands of nodes involving thousands of terabytes of data.
  • Cost effective: Hadoop allows businesses to simply access new data sources and rap into different types of data (both structured and unstructured) to generate value from that dataset. This means businesses can use Hadoop to develop valuable business visions from data sources such as social media, email conversations.  Hadoop can be used for a wide range of purposes, such as log processing, recommendation systems, data warehousing, and market promotion analysis and fraud detection.
  • Fast: Hadoop’s exclusive storage method is based on a distributed file system that basically ‘maps’ data anywhere it is located on a cluster. The tools for data processing are frequently on the same servers where the data is located, resulting in much faster data processing. If you are working with big sizes of unstructured data, Hadoop is able to capably process terabytes of data in just minutes, and petabytes in hours. To learn more about HDFS, click Big Data Hadoop Certification.
  • Resilient Feature: Fault Tolerance is the significant advantage of using Hadoop. During failure, when data is sent to a specific node, data is replicated to other nodes in the cluster.


Here are the disadvantages of Hadoop namely:

  • Security Concerns: Managing multifaceted applications such as Hadoop can be challenging. A simple example can be seen in the Hadoop security model, which is disabled by default due to absolute complexity. If whoever managing the platform lacks of know how to enable it, your data could be at huge risk. Hadoop is also missing encryption at the storage and network levels, which is a major selling point for government agencies and others that prefer to keep their data under wraps.
  • Vulnerable by Nature: Speaking of security, Hadoop makes running it a hazardous suggestion. The framework is written almost entirely in Java, which is one of the most widely used but yet, the controversial programming languages in existence.
  • Not Fit for Small Data: All big data platforms are not suited for small data needs whereas big data is not exclusively made for big businesses. Unfortunately, Hadoop is one of them. The Hadoop Distributed File System (HDFS) lacks the capacity to efficiently support the arbitrary evaluation of small files due to its high capacity design. As a result, it is not recommended for organizations with small quantities of data.
  • Potential Stability Issues: Like all open source software, Hadoop has had its share of problems on stability issues. To avoid these issues, organizations are intensely endorsed to make sure they are running the latest stable version, or run it under a third-party vendor equipped to handle such problems.

To know more about implementation of big data Hadoop, click on Hadoop Big Data Online Course.

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Which better Serves your Big Data Business Needs?

bigdata needs

Source: www.qubole.com

Big data is a powerful tool for business which is looking to improve huge volumes of data for competitive advantage and profit as well. Companies must choose either one of the platform, so that it fits in all solutions for the big data problems.

To know which functionality will serve the business use case, the following are the questions need to be asked while choosing traditional system, Big Data Hadoop (including cloud-based Hadoop services, Qubole).

Questions #1: What type of data is being analysed? (Structured or unstructured)

Structured data is data that occupies within the fixed confines of a file or record. Even in large volumes, the data can be entered, stored, queried and analysed in a simple manner. Traditional database will better serve this type of data.

For example, enterprise resource planning, backup storage for large volumes of data etc.

Semi structured data is data that is not organised into special repository such as database. These data are neither raw data nor types in the conventional database. This type of data is used in data integration.

For example: Web logs that track website activity and call center logs with toll etc.

Unstructured data is data that comes from various sources such as photos, emails, text document, audio files and social media etc. As unstructured data is complex and large in volume, traditional database cannot serve this efficiently.

For example: Facebook, LinkedIn, Logs, Web chats, YouTube etc.

Without structuring the data, Hadoop has the ability to join, aggregate and analyse the multiple data source. Thus Hadoop is the perfect tool for the companies, who are looking to store, manage and analyse large volumes of unstructured data.

Questions #2: Which database system serves better for scalable analytics infrastructure?

Traditional database will serve better for the companies whose workloads are constant and expected.

Scalability allows servers to accommodate increasing demands of workload. Hadoop infrastructure will assist for the companies who have increasing data demands.

Questions #3: Which database system implementation is cost effective?

Cost-effective is the main concern for those companies who are looking to adopt new technologies. While implementing Hadoop, companies must realize advantages of Hadoop development more significant than the cost. Otherwise, Traditional database is best for fluctuating workloads and to meet data storage.

Nowadays, companies implement hybrid systems which integrate both Hadoop platforms and traditional database to improve the benefits of both the platforms.

Questions #4: Is fast data analysis is critical?

For large data processing, Hadoop was designed and address every file in the database. This process takes time. Fast performance is not critical for few tasks such as, performing analytics, end of day reports to review daily transactions and scanning historical data.

In other scenarios, Traditional database is the better option for the companies who rely on time-sensitive data analysis. Because traditional database performs well for analysing smaller data sets in real.

Some companies use hybrid systems, where small time-sensitive data sets are relied on Traditional database and Hadoop is used to process huge, complex and highly interactive workloads in the companies.

Questions #5: Which approach fits best?

It always depends on companies, as big data analytics providing deeper insights which are leading to real competitive advantages. Based on the persistent and careful work or effort by the companies, the best tool that fits in need is Hadoop.

Companies Move On From Big Data Technology Hadoop

Companies-Move-On-From-Big-Data-Technology-HadoopIncreasing proof Hadoop — a standout among st the most critical innovations of the previous quite a long while for huge information examination — is not staying aware of the world that made it.

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Training and grooming professionals for Big Data industry

Alok KumarZaranTech LLC was featured in CIO Review Magazine July 2015 for their Big Data Analytics training. “To meet industry demand for Big Data Analytics while maintaining their expected high-quality service, ZaranTech selected blending training model as their training solution. Online learning for an organization is the vastly growing field” says Alok Kumar, Training Director at ZaranTech LLC. Online learning is not only about the inclusion of relevant information about the subject but it also includes the use of original and creative ideas .This makes the topic interesting and informative for the client. ZaranTech believes in delivering training that can be easily comprehended by the trainees with the attention to the minute details in the content.

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Big Trends in Big Data Analytics

HadoopHadoop has a set of tools to perform action on information. Distributed analytic frameworks, like Map Reduce, are evolving into distributed resource managers that are step by step turning Hadoop into a general information package, says Hopkins. With these systems, he says, “you will perform many alternative information manipulations and analytics operations by plugging them into Hadoop because the distributed file storage system”.The future state of huge information is going to be a hybrid of on-premises and cloud.

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Understanding Hadoop Technology

HadoopEnormous information is a prevalent theme nowadays in the tech media, as well as among standard news outlets. Also, October’s official arrival of huge information programming system Hadoop 2.0 is producing much more media buzz.  “To comprehend Hadoop, you need to comprehend two major things about it”. They are: How Hadoop stores records, and how it forms information. It is also said: “Envision you had a document that was bigger than your PC’s ability. You couldn’t store that record, correct? Hadoop gives you a chance to store documents greater than what can be put away on one specific hub or server.

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How Big Companies take advantage of Hadoop

HadoopAt this point, you have likely known about Apache Hadoop – the name is derived from an adorable toy elephant however Hadoop is everything except a delicate toy. Hadoop is an open source extend that offers another approach to store and process huge information. While expansive Web 2.0 organizations, for example, Google and Facebook use Hadoop to store and deal with their immense information sets, Hadoop has additionally demonstrated significant value for some organizations.

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Attend a Live WEBINAR about Big Data Hadoo Training on 9-July-2015 @8:00 PM CST ‪#‎ZaranTech


Time : Tuesday July 9th , 2015 @ 8:00 pm CST

You are most welcome to join our Upcoming batch, details of the same is as follows:

Demo Date   : 9th July Tue @ 8:00 PM CST
Class Schedule 13th July Mon, Wed & Fri 8:00 pm CST 3 hrs each session
Attend a Live Demo Session
: Click here to Register


Contact : Ilyas @ 515-978-9788 , Email : ilyas@zarantech.com

Demo Video by Trainer Raji

Attend a Free Live WEBINAR about Hadoop Training on 9-Jul-15 @8:00 PM CST. Register Link  – http://goo.gl/BYufNJ


Hadoop standardization required for industry growth

HadoopWith the latest versions of Hadoop being released the older versions are being modified and the behavior is changing for the same. Developers need to check for the changes in the applications. Since Hadoop platform is developing we need standardization of the process. Vendors and developers try to fix the applications and test them in multiple versions of Hadoop after releasing the product. This has resulted in slow migration of custom built apps to a better version of Hadoop. This complexity has given rise to a platform of Swiss-cheese matrix among st the vendors with customers having the option to choose between one tool and any other tools. They have to resolve the bugs and limitations.

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Wrangling Big Data Requires Novel Tools, Techniques

Big Data HadoopApache Hadoop has opened up lots of possibilities to analyse big data for an organization. There is a lot of complexity for these applications and the method by which this data is managed effectively. Many people like researchers have access to an array of data to discover significant trends and find out patterns for transactions, sports statistics etc. For example in sports data is large so it needs to merge with larger data. Hence companies must use new approach to deal with big data. Continue reading Wrangling Big Data Requires Novel Tools, Techniques