Is Big Data Analytics married to Cloud Computing?

Is Big Data Analytics right for your organization and is it the right timing? The first and foremost analysis that organization must perform is if the business ready to adopt the benefit from Big Data analysis. Starting by IT is not an ideal start. Big Data Analytics is effective only if your data scientists ask the right business questions. The Data Scientists need to understand the business data set and identify their relationships and patterns that Big Data analysis may illuminate. Success in the Big Data era is about more than size. It’s about getting insight from these huge data sets more quickly. The cloud enables big data processing with cloud computing capabilities for organizations of all sizes. IDC says cloud spending will also rise by 25 percent to $100bn. “Data-optimized” platform-as-a-service (PaaS) products will become increasingly popular, IDC predicts, with Amazon Web Services taking a lead in providing various specialist solutions for businesses. With the Cloud Big Data Platform, you get a production-ready and performance-tested cluster, supported by a broad ecosystem of partners. A recent IDG Enterprise survey of enterprises and small and medium-sized businesses found that 60 percent and 46 percent of these firms said big data is a top priority, respectively.

The study discovered that a total of 70 percent of participants believe their cloud infrastructure investments will grow during the next three years. Another 52 percent of organizations anticipate similar expenditures to grow regarding data analytics, while 42 percent said the same for data mining and 36 percent for data visualization.

The Big Data Analytics and Cloud Computing don’t come with a silver bullet to fix bad data, dysfunctional organizational cultures, or automatically integrate with legacy applications. Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. The cloud model promotes availability and is composed of five essential characteristics: on-demand self-service, broad network access, resource pooling, rapid elasticity and measured service; three service models: Cloud Software as a Service (SaaS), Cloud Platform as a Service (PaaS) and Cloud Infrastructure as a Service (IaaS); and four deployment models: private cloud, community cloud, public cloud and hybrid cloud). The cloud computing model offers the promise of massive cost savings combined with increased IT agility. In addition, cloud computing democratizes big data – any enterprise can now work with unstructured data at a huge scale. In theory, managing cloud-based big data is cost-effective, scalable, and fast to build. Sticking data in Windows Azure Tables, Amazon SimpleDB, or MongoDB is just the start of the data science required to make the most of big data.

A survey by GigaSpaces found that 80 percent of IT executives who consider big data processing important are considering moving their big data analytics into the cloud. The two types of deployment models for cloud computing are public and private. These are offered for general purpose computing needs as opposed to specific types of cloud delivery models.

The public cloud

The public cloud is a set of hardware, networking, storage, services, applications, and interfaces owned and operated by a third party for use by other companies and individuals. These commercial providers create a highly scalable data center that hides the details of the underlying infrastructure from the consumer.

Public clouds are viable because they typically manage relatively repetitive or straightforward workloads

The private cloud

A private cloud is a set of hardware, networking, storage, services, application, and interfaces owned and operated by an organization for the use of its employees, partners, and customers. A private cloud can be created and managed by a third party for the exclusive use of one enterprise.

The private cloud is a highly controlled environment not open for public consumption. Thus, the private cloud sits behind a firewall. The private cloud is highly automated with a focus on governance, security, and compliance. Automation replaces more manual processes of managing IT service to support customers. In this way, business rules and processes can be implemented inside software so that the environment becomes more predictable and manageable.

Big data is an inherent feature of the cloud and provides unprecedented opportunities to use both traditional, structured database information and business analytics with social networking, sensor network data, and far less structured multimedia. Big data applications require data-centric compute architecture, and many solutions include cloud-based APIs to interface with advanced columnar searches, machine learning algorithms, and advanced analytics such as computer vision, video analytics, and visualization tools. If organizations are managing a big data project that demands processing massive amounts of data, the private cloud might be the best choice in terms of latency and security.

Big data technology and cloud computing demand will push global IT spending beyond $2 trillions in 2014, according to IDC. Spending on big data technologies should see a 30 percent rise, with $14 billions expected to be shelled out on the analysis of huge pools of data such as customer behavior and business performance. High-volume big data cloud platforms will be in demand as firms look to gain significant business insight without the initial overhead costs of buying in physical infrastructure to handle the task. Gartner lists Amazon, AT&T, Google, HP, IBM and Microsoft among what it says are the top 10 cloud storage providers

Both cloud computing and big data appear to be at the top of the list of many IT executives. A Sierra Ventures survey of Fortune 500 CIOs and CTOs conducted in 2013 found that 32 percent cited mobile devices and big data as the top tech innovations influencing their companies, while 24 percent said the same regarding the use of the cloud. Are you prepared to deploy your Big Data in Cloud?


Oracle: Top 10 Big Data and Analytics Trends in 2014


Succeeding with Big Data and Analytics in 2014

Prioritizing your daily to-do list can be hard enough, much less your strategic goals for 2014. To help you focus on what really matters, we’re sharing what we believe will be the top 10 big data and analytics trends for the upcoming year.

Download the latest white paper for:

  • Highlights of the major trends, problems, and breakthroughs in big data and analytics
  • Best practices for getting the most from your analytics investments


Data Scientist and Big Data Consultant Positions – Immediate Need!!!

All the 3 positions are in Pennington, NJ. They will be Contract – Perm positions.

Position 1: Data Scientist

  • Data Scientist work with analysts, architects, and software engineers to contribute to the big data analytics program initiatives
  • Will help build a framework of capabilities to support a number of ongoing and planned big data analytics projects
  • Must have good experience on Business Analytics OLAP tools to do visual presentation of Data in Hadoop HDFS.
  • Should have experience with Data Ingestion, Hadoop, Claudera and Stat Models.
  • Prefer Finance or Banking industry background.


Position 2: Big Data Hadoop – Data Ingestion and Tool expert

  • Install and Configure Cloudera CHD4.5 and related tools like Sqoop, Flume, HDFS, MapReduce1/MapReduce2 (YARN), Pig, Hive, HBase, Oozie, Zookeeper, AVRO, Hue and Cloudera Manager,
  • Implement Kerberos security on Big Data Hadoop and HBase.
  • Implement and configure MapReduce2/YARN. 
  • Implement Data Ingestion pattern using Sqoop2, Flume, REST API and other 3rd party tools using Avro serialization tool.
  • Skills Required:
  • At lease 2 years of experience on Big Data Hadoop framework and it components like MapReduce1 and MapReduce2 (YARN), HDFS, Sqoop2, Flume, Pig, Hive, Hue, Cloudera Manager and HBase and ETL tools.
  • 5 to 7 years working experience on Database (Oracle/DB2/MS SQL), Data Warehouse,  ETL and Business Intelligence tools. 
  • Expert level understanding of Big Data Hadoop(Cloudera CDH4.5) installation and configuration of Hadoop tools.
  • Experience on implementing Kerberos security in Hadoop framework and HBase.
  • Strong Data Modeling experience 


Position 3: Big Data Hadoop Developer (Java/Python)

  • 8 – 10 years of working experience on Java J2EE development to handle, manipulate and cleanse Data. 
  • Must have worked at least 1 year on Hadoop HDFS Data Ingestion using AVRO, httpFS, REST API and Java code for Data Ingestion.
  • Must have development experience on Cloudera CDH4.5
  • Skills Required:
  • Strong Java J2EE and Python programming experience for Extraction, Transformation and Loading of Data.
  • 2-3 years experience on Data Ingestion in HDFS using AVRO and Data manipulation using Pig and Hive.
  • Experience on Data Extraction, Data massaging/Transformation and Data Loading to HDFS using Avro.



Chandra Kanumuri – Altimetrik, Corp

Recruiting Manager – People Experience

Ph: 248-281-2538/ Fax: 248-262-2938

Free Book | Predictive Analytics For Dummies (By Alteryx)

Predictive Analytics is no longer a frightening term, in fact it is becoming more entrenched in organizations’ decision and business processes each day. Predictive Analytics for Dummies helps data analysts and decision makers with the uncertainty of:

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