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Big Data Technologies

Big Data & Fast data Technologies are increasingly helping Businesses in improving their analytics

 

What is Big Data Technology?

Big Data Technology can be defined as a Software-Utility that is designed to Analyze, Process and Extract the information from an extremely complex and large data sets which the Traditional Data Processing Software could never deal with.

 

 

We need Big Data Processing Technologies to Analyze this huge amount of Real-time data and come up with Conclusions and Predictions to reduce the risks in the future. Big data Technologies are classified as follows:

Types of Big Data Technologies:

 

Big Data Technology is mainly categorized into two types:

  1. Operational Big Data Technologies

  2. Analytical Big Data Technologies

 

 

The Operational Big Data entails day to day data that is generated. This could be the Online Transactions, Social Media, or the data from a Particular Organization etc. You can even consider this to be a kind of Raw Data which is used to feed the Analytical Big Data Technologies. A few examples of Operational Big Data Technologies include data generated through online bookings, social media sites etc

 

 

​Analytical Big Data is like the advanced version of Big Data Technologies. It is a little complex than the Operational Big Data. In short, Analytical big data is where the actual performance part comes into the picture and the crucial real-time business decisions are made by analyzing the Operational Big Data.

Few examples of Analytical Big Data Technologies can be cited from different domains like Financial services, Healthcare etc

Top Big Data Technologies

 

Top big data technologies are divided into 4 fields as shown below. They are namely Data Storage, Data Mining, Data Analytics and Data Visualization

 

 

 

 

 

 

 

 

 

 

We are applying Big Data Technologies in following areas:

 

Data Storage

Hadoop

 

Hadoop Framework was designed to store and process data in a Distributed Data Processing Environment with commodity hardware with a simple programming model. It can Store and Analyze the data present in different machines with High Speeds and Low Costs.​​​

 

MongoDB

 

The NoSQL Document Databases like MongoDB, offer a direct alternative to the rigid schema used in Relational Databases. This allows MongoDB to offer Flexibility while handling a wide variety of Datatypes at large volumes and across Distributed Architectures.

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Hunk

 

Hunk enables accessing data in remote Hadoop Clusters through virtual indexes and allows to use the Splunk Search Processing Language to analyze your data. With Hunk, you can Report and Visualize large amounts from your Hadoop and NoSQL data sources.​​​​​

 

Our engineering team is also leveraging following Big Data Technologies used in Data Mining.

 

Presto

 

Presto is an open source Distributed SQL Query Engine for running Interactive Analytic Queries against data sources of all sizes ranging from Gigabytes to Petabytes. Presto allows querying data in HiveCassandra, Relational Databases and Proprietary Data Stores.

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Elasticsearch

 

Elasticsearch is a Search Engine based on the Lucene Library. It provides a Distributed, Multitenant-capable, Full-Text Search Engine with an HTTP Web Interface and Schema-free JSON documents.

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Similarly in Data analytics space we are using following technologies:

 

Kafka

 

Apache Kafka is a Distributed Streaming platform. A streaming platform has Three Key Capabilities that includes Publisher, Subscriber and Consumer

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This is similar to a Message Queue or an Enterprise Messaging System.

 

 

Splunk

Splunk captures, Indexes, and correlates Real-time data in a Searchable Repository from which it can generate Graphs, Reports, Alerts, Dashboards, and Data Visualizations. We use it for Application Management, Security and Compliance, as well as Business and Web Analytics.​​​​

 

Spark

 

Spark helps in providing  In-Memory Computing capabilities to deliver Speed, a Generalized Execution Model to support a wide variety of applications, and JavaScala, and Python APIs for ease of development.

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R-Language

 

R is a Programming Language and free software environment for Statistical Computing and Graphics. The R language is widely used among Statisticians and Data Miners for developing Statistical Software and majorly in Data Analysis.​​​​​

 

Blockchain

BlockChain is used in essential functions such as payment, escrow, and title can also reduce fraud, increase financial privacy, speed up transactions, and internationalize markets. Blockchain can be used for achieving the following in a Business Network Environment:

 

  • Shared Ledger is used to append the Distributed System of records across a Business network

  • Smart Contract: Business terms are embedded in the transaction Database and Executed with transactions.

  • Privacy: Ensuring appropriate Visibility, Transactions are Secure, Authenticated and Verifiable

  • Consensus: All parties in a Business network agree to network verified transactions.

 

​In addition we are helping with various Data visualization tools enablement and integration for our Global clients

 

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Emerging Big Data Technologies

 

TensorFlow

 

TensorFlow has a Comprehensive, Flexible Ecosystem of tools, Libraries and Community resources that lets Researchers push the state-of-the-art in Machine Learning and Developers can easily build and deploy Machine Learning powered applications.

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Apache Beam

 

Apache Beam provides a Portable API layer for building sophisticated Parallel-Data Processing Pipelines that may be executed across a diversity of Execution Engines or Runners.

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Docker

We use docker tool that  is designed to make it easier to Create, Deploy, and Run applications by using Containers. Containers allow a developer to Package up an application with all of the parts it needs, such as Libraries and other Dependencies, and Ship it all out as One Package.

Apache Airflow

 

It is a WorkFlow Automation and Scheduling System that can be used to author and manage Data Pipelines. Airflow uses workflows made of Directed Acyclic Graphs (DAGs) of tasks. Defining Workflows in code provides Easier Maintenance, Testing and Versioning.

Kubernetes

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It is a Vendor-Agnostic Cluster and Container Management tool. Kubernetes provides a platform for Automation, Deployment, Scaling, and Operations of Application Containers across Clusters of Hosts.​​​​​

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