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Empowering an Enterprise with analytics—that’s where the real value creation occurs.  We work with passion and this motive to help organizations transform complete with data governance, robust change management while imbibing a strong culture of data and analytics.

Big Data in the Enterprise

Numbers indicate an explosive growth of Big Data in the Enterprise over the years



Delivering Value

Big Data is delivering the most value to enterprises by decreasing expenses (49.2%) and creating new avenues for innovation and disruption (44.3%).



Big Data Growth

Non-relational analytic data stores are projected to be the fastest growing technology category in Big Data, growing at a CAGR of 38.6% between 2015 and 2020.




84% of enterprises have launched advanced analytics and Big Data initiatives to bring greater accuracy and accelerate their decision-making



Embrace or go extinct

79% of enterprise execs say that companies who don't embrace #BigData will lose market strength & may face extinction

Key Big Data Technologies

These are some of the key technologies we use to develop solutions in Big Data Infrastructure, Big Data Applications and Advanced AI and Machine Learning Applications for our clients.


Apache Kafka

Apache Kafka is a tool that allows you to handle large volumes of rapid data with a relatively modest set of hardware. It is used to create the subscription based messaging functionality that allows asynchronous messaging to work on the basis of large amounts of data. It can process many events per day (LinkedIn has reported Kafka to be ingesting 1 trillion events a day!) and process this data. It can generate messages for parallel consumption in a fault-tolerant manner. Kafka is extremely beneficial to organizations who want to maintain large messaging channels without having the expensive hardware to do it.


Hadoop Stack

Hadoop is well-known for its capabilities for huge-scale data processing. This open source Big Data framework can run on-prem or in the cloud and has quite low hardware requirements. The main Hadoop benefits and features are 
HDFS — Hadoop Distributed File System, oriented at working with huge-scale bandwidth
whereas with MapReduce —  it provides a highly configurable model for Big Data processing. Apache Hadoop 2.0 stack comes with extensive set of libraries from the 


Apache Storm

Storm is another Apache product, a real-time framework for data stream processing, which supports any programming language. Storm scheduler balances the workload between multiple nodes based on topology configuration and works well with Hadoop HDFS. Apache Storm comes with some key benefits such as Great horizontal scalability, Built-in fault-tolerance, Auto-restart on crashes, guaranteed process of every tuple, easily supports JSON processing as well as supports multiple programming languages.



ElasticSearch is a powerful search engine that allows a system to index and find a file (of many possible formats) in real-time. ElasticSearch allows an organization to quickly set up fast and reliable search functionality to implement full-text search with autocomplete, fuzzy search (where you can get an approximate match with the keywords) and also document-oriented search. The last one has a powerful impact on finance and legal firms where massive amounts of historical records have to be accessed to generate search results quickly.



The Hadoop File System is an excellent tool for running MapReduce jobs to process the extensive amounts of data that big data technology is known for. But to make it work, a data ingestion tool is needed that can collect, aggregate and transport that volume of data into the file system. Apache Flume is an excellent tool in this category. It is advantageous to organizations as they can get different sources of data like emails, social media logs, network traffic all ingested in the file system, efficiently and reliably. 



MongoDB is another great example of an open source NoSQL database with rich features, which is cross-platform compatible with many programming languages. It can be used in a variety of cloud computing and monitoring solutions, Stores any type of data with a cloud-native deployment and great flexibility of configuration. Data partitioning across multiple nodes and data centers delivers significant cost savings, as dynamic schemas enable data processing on the go.


R Data Science Language

R is used along with JuPyteR stack (Julia, Python, R) for enabling wide-scale statistical analysis and data visualization. JupyteR Notebook is one of 4 most popular Big Data visualization tools, as it allows composing literally any analytical model from more than 9,000 CRAN (Comprehensive R Archive Network) algorithms and modules, running it in a convenient environment, adjusting it on the go and inspecting the analysis results at once. R supports Apache Hadoop and Spark and easily scales from a single test machine to vast Hadoop data lakes.


Apache Spark

Apache Spark is an excellent alternative  of Apache Hadoop. Spark can process both batch data and real-time data, and operates 100 times faster than MapReduce. Spark provides the in-memory data processing capabilities, which is way faster than disk processing leveraged by MapReduce. In addition, Spark works with HDFS, OpenStack and Apache Cassandra, both in the cloud and on-prem, adding another layer of versatility to big data operations for your business.



TensorFlow is an AI Machine Learning framework that helps in implementing machine learning functionality and generating insights from data, with AI features. TensorFlow can offer many cutting edge advantages to organizations as it can help them run big data experiments on a large scale. It can be set up to find patterns in the data and the same algorithm can then locate similar patterns and specific actions can be triggered on the basis of that.

What we offer

Big Data Consulting

We help define Business Goals to draw insights from Data, identify and formulate use cases, conduct data feasibility, define a broad data governance framework and finally followed by executing one or more PoC (Proof of Concept) followed by review/feedback/learnings from it before going mainstream with your prescribed Big Data Blueprint for your organization

Data Engineering

Map the broad set of Identified use cases and identify the extractable diverse sources of Big Data and thus defining a Data architecture consisting of Real-Time and Non-Real-Time processing of data use cases, the backend architecture, the NoSQL/SQL databases and the Cloud/On-Prem mix architecture before finally processing the same

Business Intelligence

Business Intelligence –  the most comlext and criticial layer in Big Data, ihelmed by data scientists, engineers and analysts ensure massive amounts of data collected is normalized (cleaned), ingested, organized, maintained and presented as actionable information for decision-making while consuming this BI in the form of Applications or Visualizations

Applications and Visualization

Once the Data Engineering and BI has setup a broad infrastructure for mining information and insights it is important to now define a way to discover insights and the way you consume the data output in the form of Applications and or Data Visualizations (or Reports) using powerful Visualization platforms such as Tableau, HighCharts etc.

Data Innovation

Once the baseline for a Big Data culture consisting of the infrastructure, architecture, applications and governance is established, it is time now to think about the next wave of Innovation for your specific organization and your business domain to identify new opportunities for revenue streams, cost cuttings and better profitability


Our Corporate Training comes in very handy after the initial exercises related to Big Data Consulting is concluded to better equip your existing team of engineers and data scientists with practical and implementation level examples of the various Big Data Tools that are perhaps implemented in your existing Data Infrastructure

Case Studies


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