Big data (Hadoop) as a service – How does it work?

Big data as a service

Big data as a service – How does it work?

Overview: In today’s technology world, software as a service (SaaS) is a common model. The service if offered to the subscribers as per need basis. Big data is also following the service model. In this article, I will talk about the service model followed in big data technology domain.

Here is a description for some well-known service models for Big Data as a service








Rackspace:

Rackspace Hadoop clusters could run Hadoop on Rackspace managed dedicated servers, public cloud or private cloud.

OneMetal for cloud big data is provided by Rackspace for Apache Spark and Hadoop. It offers a fully managed bare-metal platform for in-memory processing.

Rackspace eliminates the issues with managing and maintaining big data manually. It comes with the following features

  • It reduces the operation burden by providing 24x7x365 fanatical support.
  • Provides full Hortonworks Data Platforms (HDP) toolset access, including Pig, Hive, HBase, Sqoop, Flume, and Hcatalog.
  • It has a flexible network design with traditional networking up to 10GB.

Going for private clouds comes with public clouds power and efficiency and security and control of Private clouds. The major disadvantage of using private clouds is that it’s difficult to manage and need experts to upgrade, patch and monitor a cloud environment. Rackspace provides an excellent support in this case and there is no need to worry about cloud management.








Joyent:

Based on Apache Hadoop, Joyent is a cloud based hosting environment for big data projects. This solution is built using Hortonworks Data Platform (HDP).

It is a high-performance container-native infrastructure for today’s need of mobile applications and real-time web. It allows running of enterprise-class Hadoop on the high performance Joyent cloud.

The following advantages could be listed now:

  • Two third of the infrastructure costs could be cut by solutions provided by Joyent with the same response time.
  • 3x faster disk I/O response time by Hadoop clusters on Joyent Cloud.
  • It accelerates the response times of distributed and parallel processing.
  • It also improves the scaling of Hadoop clusters executing intensive data analytics application.
  • Data scientists are getting faster results with better response time.

Generally, Big Data applications are considered expensive and difficult to use. Joyent is targeting towards changing this by providing a cheaper and faster solutions

Joyent provides public and hybrid cloud infrastructure for real-time web and mobile applications. Its clients include LinkedIn, Voxer, etc.

Qubole:

For Big Data projects, a Hadoop cluster is provided by Qubole with built-in data connectors and graphical editor. Enables to utilize variety of databases like MySQL, MongoDB, Oracle and sets Hadoop cluster on auto-pilot. It provides a query editor for Hive, Pig and MapReduce.

It provides everything-as-a-service i.e.

  • query editor for Hive, Pig and MapReduce
  • an expression evaluator
  • utilization dashboard
  • ETL and data pipeline builders

Its features are listed below:

  • Runs faster than Amazon EMR
  • Easy to use GUI with built-in connectors and seamless elastic cloud infrastructure.
  • Optimization of resource allocation and management is done by QDS hadoop engine by using daemons. It provides an advanced Hadoop engine for better performance.

Using techniques like advanced caching and query acceleration, Qubole has demonstrated query speeds of up to 5x faster as compared to cloud based hadoop.

  • For faster queries, I/O is optimized for S3 storage. S3 is secure and reliable. Qubole Data Service offers 5x faster execution against data in S3.
  • No need to pay for unused features and application.
  • Cloud Integration i.e. Qubole data service doesn’t require changes to be done to your current infrastructure i.e. it gives flexibility to work with any platform. QDS connectors supports import and export of cloud databases MongoDB, Oracle, PostgresSQL and resources like Google Analytics.
  • Cluster Life Cycle Management with Qubole Data Service for provisioning clusters in minutes, scaling it with demand and running it in environment for easy management of Big Data assessment.









Elastic MapReduce:

Amazon Elastic MapReduce (EMR) provides a managed Hadoop framework for simplifying big data processing. It’s easy, and cost-effective for distributing and processing large amount of data

Other distributed frameworks such as Spark, Presto can also run in Amazon EMR to interact with data in Amazon S3 and DynamoDB. EMR handles these use cases with reliability:

Web indexing Machine learning
Scientific simulation Data Warehousing
Log Analysis Bioinformatics

Its clients include Yelp, Nokia, getty images, reddit, and others. Some of its features are listed below:

  • Flexible to use with root access to every instance, supports multiple Hadoop distributions and applications. It’s easy to customize every cluster and install additional application.
  • It’s easy to install Amazon EMR cluster.
  • Reliable enough to spend less time monitoring your cluster; retrying failed tasks and automatically replaces poorly performing instances.
  • Secure as it automatically configures Amazon EC2 firewall settings. This is for controlling network access to instances.
  • With Amazon EMR, you can process data at any scale. The number of instances can be easily increased and decreased.
  • Low cost pricing with no hidden costs; pay hourly for every instance used. For Example, launch a 10-node Hadoop cluster for as little as $0.15 per hour.

It is used to analyze click stream data for understanding user preferences. Advertisers can analyze click streams and advertising impression logs.

It can also be used to process vast amounts of genomic data and large data sets efficiently.  Genomic data hosted on AWS could be accessed by researchers for free.

Amazon EMR could be used for log processing and helps them in turning petabytes of unstructured and semi-structured data into useful insights.








Mortar:

It is a platform for high-scale data science and built on the Amazon Web Services cloud. It is built on Elastic MapReduce (EMR) to launch Hadoop clusters.  Mortar was founded by K Young, Jeremy Kam, and Doug Daniels in 2011 with a motive to eliminate the time consuming difficult tasks. This was done so that the scientists could spend their work time doing other critical works.

It runs on Java, Jythoo, Hadoop etc. for minimizing time invested by users and to let them focus on data science.

It comes with the following features:

  • It frees your team tedious and time-consuming installation and maintenance.
  • Save time with Mortal by getting solutions into operations in a short span of time.
  • Automatically alerts users for any glitches in technology and application to make sure that they’re getting accurate and real-time information.
  • Vendor changes don’t affect users much because it’s been running on open technologies.

Applications of Mortor platform:

  • For deploying a powerful, scalable recommendation engine, the fastest platform is Mortor.
  • Mortor is fully automated as it runs the recommendation engine from end to end with only one command.
  • It uses industry standard version control which helps in easy adaptation and customization.
  • For analyzing, easily connect multiple data sources to data warehouses.
  • It saves work time of your team by handling infrastructure, deployment, and other operations.
  • Predict analysis by using the data you’re already having. Mortar supports approaches like linear regression, classification for analysis.
  • Support leading machine learning technologies like R, Pig, Python for delivering effortless parallelization for complex jobs.
  • With 99.9% up-time and strategic alerting ensures the trust of users and delivering of analytics pipeline again and again.
  • Predictive algorithms are used for growing the business like predicting demand, identifying high-value customers.
  • Analyzing of large volume of text is easily done, whether its tokenization, stemming or LDA, n-grams.









Summary:

There are a lot of Big Data applications available today and in future there would be faster and cheaper solutions available for users. Moreover, service providers would come with better solutions making the installation and maintenance at a cheaper rate.

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