Big Data


Data sets and technology stacks that exceed the processing capacity of traditional software tools is termed as Big Data. In most cases it means when effective capturing, storing, requesting and analyzing the data in relational databases, within a required elapsed time is not allowed by data volumes, formats and sources. Here are some indications that your present data technology, architecture or strategy fits into the Big Data category:

  • Data records are locked and reading operations blocked by frequent write operations. Extraction of business data or search can’t be completed within given time limits as an increasing data volume affects retrieval operation timing.
  • You are told by your IT guy that the addition of new fields to the table will require more than a month of testing as it requires changes in data models and affects all the systems involved in the data table.
  • You have to acquire new hardware with a more powerful CPU and hundreds of gigabytes of memory to process your data on time.

If you are experiencing any of these symptoms you’re certainly dealing with Big Data. Big Data Technologies might help you in that case.

Effective capturing, storing, selecting and processing data of big volume, variety and velocity are allowed by Big Data technology stacks. Internet giants such as Yahoo, Google and Facebook invented these technologies because they first dealt with unstructured data on a large scale. Numerous key terms and principals are the pillars of the Big Data technologies.

Apache™ Hadoop® enables big data applications for both operations and analytics consulting firm and we help clients to maximize the value of petabyte of semi-structure and unstructured data by improving their data foundation and enhancing their predictive analytic capabilities using Cloudera/MapR/Hortonworks Hadoop echo system namely Tez, Spark, Cascading, Pig MapReduce, GraphX, MLLIB, Mahout, Hive, Impala, Drill, Spark SQL, Solr, HBase, Storm, Spark Streaming, Hue, HttpFs, Flume and Sqoop, Sentry, Oozie, Sahara, Juju & ZooKeeper. IBM Pure data Analytics (Netezza), IBM SPSS, IBM Big Insight and nosql Cassandra, Accumulo, MangodB, Amazon SimpleDB & DynomoDb, Couchdb, Microsoft Azure Table Storage, Oracle NoSQL Database. We combine data science and domain expert to enable our clients to implement innovative, data-driven business solutions.
Following are the advantages of moving to big data Hadoop Echo system:
1. Higher Capacity and Lower cost
2. High Availability and Disaster Recovery
3. Multi-Tenancy and Highly secured
4. High Performance and scalable

Higher Capacity and Lower cost (Cost Saving): Hadoop lets you store as much data as you want in structured, semi-structured, video, audio or any type of data you need, simply by adding more servers to a Hadoop cluster. Each new commodity server (which can be commodity x86 machines with relatively small price tags) adds more storage and more processing power to the overall cluster. This makes data storage with Hadoop far more economical than prior methods of data storage. For example, Hadoop can support 4TB of hard disk capacity per node, with each node costing around $2000, while other choices average between $10-12,000 per terabyte.

High Availability: High Availability features eliminate single points of failure at the node; file system metadata, HDFS access, YARN resource management, and job tracking levels. These give you high uptime with zero data loss. You also get no work loss upon node failure to avoid restarting jobs from scratch. Rolling Upgrades let you upgrade live clusters one node at a time to minimize planned downtime.

Disaster Recovery: Disaster recovery features let you develop a true business continuity strategy to overcome a site-wide disaster. HDFS Mirroring lets you create a consistent remote replica or “mirror” for disaster recovery, as well as for load balancing and geographic distribution. Scheduled mirroring sends only block-level differentials to minimize both synchronization time, and bandwidth utilization. Mirroring allows you to recover quickly from file deletion or corruption.

Highly secured: Provides security controls to ensure that, sensitive data is accessible only by authorized users. Hadoop data is protected using standard UNIX file permissions, along with advanced role-based access control lists. You can integrate with Kerberos and/or LDAP via Pluggable Authentication Modules (PAM). Wire-level encryption encrypts data sent between nodes to ensure data privacy

Multi-Tenancy: Capability unique to MapR allows you to manage distinct user groups, data sets, and applications in a single cluster while keeping them isolated from each other. You can run different jobs at the same time safely, securely and efficiently.

High Performance: Innovations at the file system for faster file access, and an optimized MapReduce shuffle engine allows you to maximize work with minimum hardware than compared to other distributions. Faster file access and a faster optimized shuffle for MapReduce enables customers to achieve more work from their hardware investment. Big” spatial data from imaging and spatial applications share many similar requirements for high performance and scalability with enterprise data, but has its own unique characteristics; spatial data are multi-dimensional, and spatial query processing comes with high computational complexity. Hadoop achieves the goal through spatial partitioning, partition based parallel processing over MapReduce, effective handling of boundary objects to generate correct query results, and multilevel spatial indexing supported customizable spatial query engine. Hadoop provides a scalable and effective solution for analytical spatial queries over large scale spatial datasets.

Scalable: Another primary benefit of Hadoop is its scalability. Rather than capping your data throughout with the capacity of a single enterprise server, Hadoop allows for the distributed processing of large data sets across clusters of computers, thereby removing the data ceiling by taking advantage of a “divide and conquer” method among multiple pieces of commodity hardware. With its distributed file system metadata architecture, Hadoop scales linearly with the number of nodes, with support of up to 1 trillion files. Hadoop clusters are designed to scale to 10,000 nodes to provide plenty of headroom for today’s growing big data deployments. It supports up to 1 trillion tables, millions of columns across trillions of rows, and cell sizes up to 2 GB.

Spark Real-time streaming: Spark Streaming is an interesting extension to Spark that adds support for continuous stream processing to Spark. Most Internet scale systems have real time data requirements alongside their growing batch processing needs. Spark Streaming is designed with the goal of providing near real time processing with approximately one second latency to such programs. Some of the most commonly used applications with such requirements include web site statistics/analytics, intrusion detection systems, and spam filters. Spark Streaming seeks to support these queries while maintaining fault tolerance similar to batch systems that can recover from both outright failures and stragglers. They additionally sought to maximize cost-effectiveness at scale and provide a simple programming model. Finally, they recognized the importance of supporting integration with batch and ad hoc query systems.


TalTeam’s Big Data architects/consultants provide an optional enterprise-grade NoSQL database in Hadoop to run operational and analytical workloads together in a single cluster. Company can run big data Applications on HBase/Cassendra/Accumulo. The flexible wide-column data model, Hadoop scalability, data locality with MapReduce jobs, row-level ACID transactions, strong data consistency, etc. It runs on the same nodes in a cluster with Hadoop and stores data in Hadoop, letting you run database workloads alongside Hadoop analytics. It shares administrative functionality with Hadoop, including capabilities around high availability, disaster recovery, snapshots and security (authentication, authorization, wire-level encryption). It is architected to deliver high performance, continuous low latency (no compaction/defragmentation delays) and extreme scalability.

Data volumes’ exponential growth is caused by the cumulative number of systems and people acting as data sources of textual, verbal, video and transactional information. Insider information and patterns earlier hidden due to lack of proper technologies is comprised in this data. Almost unlimited benefits can be acquired by Organizations which can unravel these troves of information, analyze it and build new products. Through implementation of Big Data systems DataArt has helped many organizations in accomplishing this goal.



  • Data Processing and analytics with Apache Hadoop, Cloudera’s distribution of Hadoop, GridGain, Hazelcast, R, Matlab,Hive, Pig
  • Solutions for Publishing, Travel and Financial industries built with
    1. NoSQL databases, such as MongoDB, CouchDB, Redis, HBase
    2. Fast search with Solr and ElasticSearch for Apache Lucene
  • Distributed web crawling systems
  • Distributed log processing with Splunk
  • Social Web mining, Text Mining with NLTK, OpenNLP, steaming Twitter and Facebook API
  • Research projects on Windows Azure with Hadoop



  • With our rising experience in Big Data technologies we will help you:
  • Build more transparent systems
    1. Businesses can increase security and safety of their systems, lessen search and processing time, improve efficacy of resource management, save energy and help meet customer expectations by making data available on time
  • Customizing your pivotal actions according to the needs of a specific sector by recognizing customers and determining population segmentation
    1. Modifying products and services for client’s needs
    2. Apply real-time micro-segmentation
    3. Deal with customers in a personalized way
  • Substitute or improve human decision with algorithms where applicable
    1. Find concealed insights affecting decision
    2. Automatically calibrate your inventories, expenses, logistics, pricing, etc. to build avant-garde businesses with new data products accessible in the evolving marketplaces

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