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Leading Hadoop Vendors in BigData

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Introduction:

Hadoop and Big Data analytics are probably the most overshadowed topics perhaps discussed by security talks. Apache Hadoop and its other counterparts are all enterprise-ready and follow world class standards. Apart from an active community which follows upon on all the required details that we request for, it is one of the best set of solutions that any Organization can blindly start working if it fits all their requirements. Yes, you’ve heard that fact right – they are all open source technologies and all of these technologies are backed with a proactively active community support.

For businesses that need commercial support with Hadoop and their Big Data requirements, there are more than 10 companies that are ready to serve you with them. Let us now take a look at each of these organizations that provide the best of the class Hadoop distributions for Organization to make use of them:

Leading Hadoop Vendors in BigData

Top 10 Big Data Vendors:

The top 10 Vendors offering their Big Data / Hadoop solutions are as follows, let us take a brief look into each of these offerings and try to understand the pros and cons of them. Let’s get a fair bit of an idea about all these vendors with their Big Data / Hadoop offerings.

1. Cloudera:

Cloudera Inc. was founded as a collective efforts of big data geniuses from Google, Oracle, Yahoo and Facebook in the year 2008. Cloudera was the first one to develop and distribute Apache Hadoop based software and is still the largest organization with the largest user base with many customers to their belt. In addition to the core of the distribution based upon Apache Hadoop, Cloudera has provided more proprietary tools such as the Cloudera Management suite to automate the installation process, Cloudera Search to ease the process of search of products. The Cloudera Management suite provides the users reduced deployment time, real time node counts etc.

Key differentiators: CDH is a distribution of Apache Hadoop and related products.

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2. Hortonworks:

Hortonworks was founded in the year 2011 and has then quickly emerged on the leading vendors to provide Hadoop distributions. The Hadoop distribution made available by Hortonworks is also an open source platform based on Apache Hadoop for analyzing, storage and management of Big Data. Hortonworks is the only vendor to provide a 100% open source distribution of Apache Hadoop with no proprietary softwares tagged with it. Hortonworks distribution, HDP 2.0 can be accessed and downloaded from their organization website for free and its installation process is also very easy. The inclusion on YARN into the Hadoop’s ecosystem from the distribution made available by Hortonworks – makes it better than MapReduce, in a sense to enable more integrations of data processing frameworks.

Key differentiators: Hortonworks is a 100% open source solution and a major contributor to the Apache Hadoop project.

3. Amazon Web Services Elastic MapReduce Hadoop Distribution:

Amazon Elastic MapReduce (Amazon EMR) is a sub-project under the umbrella of Amazon related projects – the Amazon Web Services (AWS). It is a web service that allows us to manage those huge Big Data datasets. Amazon EMR with its array full of security features makes it look promising in securing and also to reliably handle big data, log analysis, web indexing, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics. In addition to this, Amazon’s pricing model is also very simple – charges per hour rates and you can accurately predict the monthly subscription fees that you are liable to pay Amazon month over month. Helps plan your Hadoop and Big Data budget upfront and makes it look transparent as well.

Per hour rates range from around $0.011 to $0.27 (that is around $94 a year to around $2367 a year), depending on the size of the instance you select for your Hadoop distribution. The only downside that one can observe with Amazon’s services is that they are a bit difficult to use over the traditional others. The ease of use was what Amazon has concentrated upon and its ease of use is way better than its earlier predecessors. For an individual to use its services alongside Amazon AWS services, he / she should have at least intermediate technical knowledge as a system’s administrator understands all the options of system.

Key differentiators: Amazon’s Elastic Cloud, S3, and DynamoDB integration plus an expensive and flexible pay-as-you-use plan. An added bonus is that EMR plays nice with Apache Spark and the Presto distributed SQL query engine.

4. Dell | Cloudera Apache Hadoop Solution:

Dell’s Statistica Big Data Analytics is a completely integrated, configurable, cloud-enabled software platform that can easily be deployed within few clicks. We can better understand the Market traction and Trends by harvesting the sentiments from Social Media and also from the web. This is done as Dell completely leverages over the benefits of Hadoop, Lucene/Solr search, and Mahout Machine learning to provide us the highly scalable analytic solution running on Dell PowerEdge servers.

Dell summarizes its hardware and its software requirements for the Hadoop cluster simply as, 2 to 100 Linux servers for your Hadoop Cluster, 6GB RAM, 2+ Core, 1TB HDD per server. This in itself should be self-explanatory that the entry point into a Hadoop solution is simple and inexpensive.

Key differentiators: The recent purchase Statistica Big Data Analytics platform features the natural language processing, entity extraction, interactive visualizations and dashboards, databases, and distributed advanced analytic models across Hadoop.

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5. MapR:

MapR provides a production-ready distribution which can run both on an online and analytical processing and applications on a single on-premise platform. This means that there can be more than one application that can be run on a single Hadoop cluster, which in turn reduces your operational costs. MapR runs the largest single Production clusters of Hadoop which includes the following:

1. Linear scalability exceeding 100 million files limit in a Hadoop Distributed File System (HDFS), distributed metadata architecture scaling to trillions of files. This allows NoSQL and Hadoop applications to work seamlessly on a single platform without doing much from our side.

2. MapR also provides Hadoop on the highest availability everywhere across all projects and is known as a fact to maintain 99.999% availability.

Key differentiators: MapR is the only distribution amongst all other distributions that allow Hadoop to be accessed via the Network File System, or a NFS. A NFS allows faster access to the required data and provides better system administration without requiring multiple steps to move or to access data.

6. IBM Infosphere Insights:

The IBM Open Platform contains around 16 different Apache projects and has full support for the Open Data Project. Anyone can download the IOP platform for free of charge or select a supported offering and use it on premises. We can use IBM’s Hadoop-as-a-Service on its SoftLayer cloud infrastructure to eliminate the pain of maintaining our own hardware. On its underlying infrastructure, IBM offers its BigInsights for Apache Hadoop product for advanced analytical needs.

IBM’s BigInsights project includes the following: Hadoop, SQL-on-Hadoop, business analytics tools, advanced analytics, accelerators, optimized performance, management, seamless data integration, and real-time streaming analytics.

Key differentiators: IBM is a name that is synonymous to big data. The IBM Open Platform (IOP) uses a 100% open source solution and is absolutely free.

7. Attunity Replicate:

Attunity has a strategic alliance with both Cloudera and Hortonworks. It’s very tough for an individual to pinpoint exactly what this tool does for big data until you see the process in action. Replicate takes data from one of the platforms and translates it into another form. Attunity’s Click2Replicate feature makes it an allowable for you to graphically select your source and your target and then click to replicate the data from your source to target. You can filter the data by various criteria, but the process as a whole is very simple. You don’t really have to worry about the transformation process, as such.

Key differentiators: Attunity automates data transfer into Hadoop from any source and it also automates data transfers out of Hadoop, including both structured and unstructured data.

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8. Datameer Professional

Datameer Professional allows you to ingest, analyze, and visualize terabytes of structured and unstructured data from more than 60 different sources including social media, mobile data, web, machine data, marketing information, CRM data, demographics, and databases to name a few. Datameer also offers you 270 pre-built analytic functions to combine and analyze your unstructured and structured data after ingest. Datameer focuses on big data analytics in a single application built on top of Hadoop. Datameer features a wizard-based data integration tool, iterative point-and-click analytics, drag-and-drop visualizations, and scales from a single workstation up to thousands of nodes. Datameer is available for all major Hadoop distributions.

Key differentiators: The first big data analytics platform for Hadoop-as-a-Service designed for department-specific requirements.

9. DataStax Enterprise Analytics

DataStax delivers powerful integrated analytics to 20 of the Fortune 100 companies and well-known companies such as eBay and Netflix. DataStax is built on open source software technology for its primary services: Apache Hadoop (analytics0, Apache Cassandra (NoSQL distributed database), and Apache Solr (enterprise search). DataStax made the choice to use Apache Cassandra, which provides an “always-on” capability for DataStax Enterprise (DSE) Analytics. DataStax OpsCenter also offers a web-based visual management system for DSE that allows cluster management, point-and-click provisioning and administration, secured administration, smart data protection, and visual monitoring and tuning.

Key differentiators: DataStax uses Apache Cassandra and Apache Hadoop as the database engine and the analytics platform that is highly scalable, fast, and capable of real-time and streaming analytics.

10. FICO Big Data Analyzer

FICO’s Big Data Analyzer is a tool that is purpose-built analytics for business users, analysts, and data scientists who deal with any type of data on Hadoop. FICO’s Big Data Analyzer advantage is that it masks most of the Hadoop’s complexity, allowing any user to gain more understanding about the system and also at the same time more business value from the data. FICO Big Data Analyzer provides an end-to-end analytic modeling lifecycle solution ranging from extracting until exploring data, creating necessary predictive models, discovering the necessary business insights, and using this data to deduce the necessary and actionable decisions.

Key differentiators: The FICO Decision Management Suite includes the FICO Big Data Analyzer, which provides an easy to use tool or an interface for individuals from companies to use big data analytics for the necessary decision management.

Checkout Hadoop Interview Questions

Conclusion:

In this article we have the best and the Top most 10 Apache Hadoop Vendors in Big Data. We have also discussed in a considerable amount about each of these Vendors and its Hadoop Distribution in the world of Big Data. We have also tried to provide a key differentiator tagging to each of these distributions, based on which an Organization can make their necessary decisions to choose which amongst these 10 suite their needs and requirements.

List of Other Big Data Courses:

 Hadoop Adminstartion  MapReduce
 Big Data On AWS  Informatica Big Data Integration
 Bigdata Greenplum DBA  Informatica Big Data Edition
 Hadoop Hive  Impala
 Hadoop Testing  Apache Mahout

 


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