Big Data Analytics

We offer an innovative cloud-services framework, which is made up of a growing number of data management products. The productivity of the environment is accelerated by a common user experience across all products, the AI/ML-driven intelligence engine, and a micro-services architecture.

Big Data Parser provides a unique development environment for lean data integration. With this software, your IT organization can view data samples within Big Data Parser Studio and understand their structure and layout through a set of integrated tools.

Big Data Parser enables access to the most difficult data and file formats in Hadoop, reducing the time and cost of developing data handlers by 70 percent. It enables IT organizations to efficiently manage industry standards, binary documents, and hierarchical data.

Big data analytics is the pursuit of extracting valuable insights from raw data that is high in volume, variety, and/or velocity.

Big data analytics systems transform, organize, and model large and complex data sets to draw conclusions and identify patterns. Skilled big data analytics professionals, who generally have a strong expertise in statistics, are called data scientists.

The industry’s only connected data management solution architected to access, integrate, clean, master, govern, and secure big data.

  • Universal Data Access: Access all types of data including transactions, applications, databases, log files, social, machine, and sensor data.
  • High-Speed Mass Ingestion & Extraction: Ingest data from source systems, Hadoop, and target applications using high-performance connectivity, mass ingestion, and dynamic mappings.
  • Data Integration on Hadoop and Spark: Get access to an extensive library of advanced prebuilt data integration transformations on Hadoop and Spark, including embedded Python transformations for operationalizing data science work.
  • Data Profiling on Hadoop: Profile data on Hadoop to understand the data, identify data quality issues, and collaborate on data pipelines.
  • Intelligent Data Parsing on Hadoop: Parse complex multi-structured, hierarchical, unstructured, and industry standard data on Hadoop automatically using Informatica Intelligent Structure Discovery based on CLAIRE ™ engine, our artificial intelligence technology.
  • Visual Design Environment: Builds on top of the open-source Hadoop framework and preserves all the transformation logic without specialized development.
  • Flexible Serverless Deployment: Deploy and manage distributed resources automatically both on-premises and off-premises on Amazon Web Services Elastic MapReduce and Microsoft Azure HDInsight.

Businesses today have an unprecedented opportunity to gain insight from a steady stream of real-time data—for example, clickstreams from web servers, application and infrastructure log data, real-time systems, and data coming from sensors or agents placed on the almost endless variety of devices and machines comprising the Internet of Things. This almost continuous flow of small messages and events can drive decision making and operational intelligence to new heights of agility and responsiveness. However, as many small pieces of data flow in at high rates and accumulate quickly into large volumes, organizations can only derive maximum value from it if they can gather and analyze it immediately.

IoT Data Stream for Machine Data efficiently collects all forms of streaming data and delivers it directly to both real-time and batch processing technologies so companies can leverage it for holistic operational intelligence and big data analytics. IoT Data Stream is a distributed, scalable system that uses established, high performance brokerless messaging technology to greatly simplify streaming data collection through:

  • Lightweight agents for an ecosystem of sources and targets
  • Brokerless messaging transport using a publish/subscribe model
  • Flexibility to connect sources and targets in numerous patterns
  • High performance delivery direct to targets over LAN/WAN
  • Simplified configuration, deployment, administration and monitoring

IoT Streaming Benefits

  • Enable real-time operational intelligence and big data analytics
  • Deliver reliable high performance streaming data collection over LAN/WAN
  • Provide highly available quality of service with guaranteed message delivery
  • Adapt quickly to new streaming data between multiple sources and targets
  • Simplify configuration, deployment, administration and monitoring
  • Increase operational efficiency and lower costs
  • Enable real-time operational intelligence with big data streaming analytics
  • Reduce time-to-value with increased productivity and rapid deployment
  • Deliver information at any latency with one flexible platform
  • Simplify configuration, deployment, administration, and monitoring of real-time streaming
  • Minimize risks associated with complex and evolving open source technologies

Social listening is the process of monitoring digital conversations to understand what customers are saying about a brand and industry online. It is also used to surface feedback that could help to differentiate their brand, product, or service.

Social media listening, also known as social media monitoring, is the process of identifying and assessing what is being said about a company, individual, product or brand on the Internet.

Social listening is the process of tracking conversations around specific topics, keywords, phrases, brands or industries, and leveraging your insights to discover opportunities or create content for those audiences. It’s more than watching @mentions and comments pour in via your social profiles, mobile apps or blogs. If you’re only paying attention to notifications, you’re missing a huge group of people that are talking about you, your brand and your product.

Data Science ( Mining, Prediction, Sentiment and Text Analytics )

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.

It is also a concept to unify statistics, data analysis, machine learning and their related methods in order to understand and analyze actual phenomena with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.

Data Mining for Text Analytics

Sentiment Analysis can be applied on any data streamed from any Social Medial platform eg. (Youtube, Facebook, Twitter, LinkedIn, etc.). Data streamed include text and Media including ‘Image Processing’ and ‘Audio Processing‘.

Data Processing, Database and Big Data Hadoop

  1. Supervised Labeled Data: You give to the computer some pairs of inputs/outputs, so in the future new when new inputs are presented you have an intelligent output. Approaches used for supervised learning are Classification ‘Discrete Labels’ and Regression ‘Real Values’
  2. Un-Supervised Unlabeled Data: You let the computer learn from the data itself without showing what the expected output is. Example: ‘Clustering’. Grouping items based on items similarities or Grouping users based on users’ interests.

Machine Learning and Predictive Analysis

Analytical models are mathematical models that have a closed form solution, i.e. the solution to the equations used to describe changes in a system can be expressed as a mathematical analytic function

A Definition of Business Analytics. Business Analytics is “the study of data through statistical and operations analysis, the formation of predictive models, application of optimization techniques, and the communication of these results to customers, business partners, and college executives.”

Sentiment Analysis

It is a way to evaluate written or spoken language to determine if the expression is favorable, unfavorable, or neutral, and to what degree.

It uses Natural Language Processing (NLP) to collect and examine opinion or sentiment words.

Sentiment analysis helps you understand Social Sentiment of your brand so you can always adjust to the present market situation and satisfy your customers in a better way.

‘NLP’ – Natural Language Processing

Natural language processing (NLP) is an area of Computer Science and Artificial Intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.

‘HCI’ – Human-Computer Interaction

Human-Computer Interaction (HCI) is a multidisciplinary field of study focusing on the design of computer technology and, in particular, the interaction between humans (the users) and computers. While initially concerned with computers, HCI has since expanded to cover almost all forms of information technology design.

Why is ‘HCI’ important? Human-Computer Interaction, often called HCI, is a sociotechnological discipline whose goal is to bring the power of computers and communications systems to people in ways and forms that are both accessible and useful in our working, learning, communicating, and recreational lives.

‘AI’ – Artificial Intelligence

The study of computer systems that attempt to model and apply the intelligence of the human mind.

A branch of computer science dealing with the simulation of intelligent behavior in computers.

The capability of a machine to imitate intelligent human behavior.

Big Data Science Vendor Product Appliances

  • Big Data Hadoop engineer
    • Teradata Data warehouse Miner
    • IBM: Big Insight
    • Microsoft: HD-Insight
    • SAP: Big Data Analytics
    • Oracle Big Data Appliance
    • SAS Big Data Analytics and Hadoop
    • Google Big Data
    • MAP-R Converged Data Platform
    • Cloudera Big Data Platform
  • Big Data Analytics & Mining
    • SAP Big Data Analytics
    • Google Big Query
    • Dell Big Data Analytics
    • SAS Visual Data Mining Machine Learning
    • IBM SPSS Modeler
    • Oracle Big Data Analytics
    • Microsoft Azure Machine Learning
    • Rapid Miner
    • Splunk Big Data Analytics