In today's ever-changing technology world, software as a service (SaaS) has become a common model. The service is offered to subscribers on a per-need basis. Big data is also following the same service model. In this article, we will discuss the service model followed in the big data technology domain.
Here are some well-known service models for big data as a service (BDaaS):
RackspaceRackspace Hadoop clusters can run Hadoop on Rackspace-managed dedicated servers, public cloud or private cloud.
Rackspace eliminates the issues with managing and maintaining big data manually. It comes with the following features:
- Reduces operation burden by providing 24×7×365 support
- Provides full Hortonworks Data Platforms (HDP) toolset access, including Pig, Hive, HBase, Sqoop, Flume and HCatalog
- Flexible network design with traditional networking up to 10GB
JoyentBased on Apache Hadoop, Joyent is a cloud-based hosting environment for big data projects. This solution is built using the Hortonworks Data Platform. It is a high-performance container-native infrastructure for the needs of today’s mobile applications and real-time Web. It allows the running of enterprise-class Hadoop on the high-performance Joyent cloud.
It also has the following advantages:
- Cutting two-thirds of infrastructure costs by solutions provided by Joyent with the same response time
- 3× faster disk I/O response time by Hadoop clusters on Joyent Cloud
- Accelerates the response times of distributed and parallel processing
- Improves the scaling of Hadoop clusters executing intensive data analytics applications
- Faster results with better response time
QuboleFor big data projects, a Hadoop cluster is provided by Qubole with built-in data connectors and a graphical editor. This enables the utilization of a variety of databases like MySQL, MongoDB and Oracle, and sets the Hadoop cluster on auto-pilot. It provides a query editor for Hive, Pig and MapReduce.
Qubole provides everything-as-a-service, including:
- Query editor for Hive, Pig and MapReduce
- Expression evaluator
- Utilization dashboard
- Extract transform load (ETL) and data pipeline builders
- 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, providing an advanced Hadoop engine for better performance
- For faster queries, I/O is optimized for S3 storage. S3 is secure and reliable. Qubole Data Service offers 5× faster execution against data in S3.
- No need to pay for unused features and applications
- Cloud integration — Qubole Data Service doesn’t require changes to your current infrastructure, meaning it has the flexibility to work with any platform. QDS connectors support 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 assessments
Elastic MapReduceAmazon Elastic MapReduce (EMR) provides a managed Hadoop framework for simplifying big data processing. It’s easy and cost-effective for distributing and processing large amounts of data.
Other distributed frameworks such as Spark and Presto can also run in Amazon EMR to interact with data in Amazon S3 and DynamoDB. EMR handles these use cases with reliability:
- Flexible to use with root access in every instance, supports multiple Hadoop distributions and applications. It’s easy to customize every cluster and install additional applications.
- It’s easy to install Amazon EMR cluster.
- Reliable enough to spend less time monitoring your cluster; retries failed tasks and automatically replaces poorly performing instances.
- Secure, as it automatically configures Amazon EC2 firewall settings for controlling network access to instances
- Process data at any scale with Amazon EMR. 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 can also be used to process vast amounts of genomic data and large data sets efficiently. Genomic data hosted on AWS can be accessed by researchers for free.
MortarMortar 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 created by K. Young, Jeremy Kam, and Doug Daniels in 2011 with the motive to eliminate time-consuming, difficult tasks. This was done so that the scientists could spend their time doing other critical work.
It runs on Java, Jython, Hadoop, etc. for minimizing time invested by users and to let them focus on data science.
It has the following features:
- It frees your team form tedious and time-consuming installation and maintenance.
- Saves time by getting solutions into operations in a short span of time.
- Automatically alerts users of any glitches in technology and applications to ensure that they’re getting accurate and real-time information.
- For deploying a powerful, scalable recommendation engine, the fastest platform is Mortar.
- Mortar 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 for your team by handling infrastructure, deployment and other operations.
- Predict analysis by using the data you already have. Mortar supports approaches like linear regression and classification for analysis.
- Support leading machine-learning technologies like R, Pig and Python for delivering effortless parallelization for complex jobs.
- 99.9% uptime 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 and identifying high-value customers.
- Analyzing of large volumes of text is easily done, whether it is tokenization, stemming, LDA or n-grams.