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Big Data Processing

Today’s data processing systems require batch and real-time processing capabilities to effectively support a business. Newer technology and tools are available but most businesses are unsure of which exactly to use. Distributed processing systems like Hadoop can perform large-scale batch processing on huge volumes of data but may not be suitable for real-time analytics due to performance lags. Our Big Data services are focused on providing a wholesome solution to business problems using the right set of tools.

Big Data Services

We build scalable systems that can Store, Process, Visualize, and Predict in near real-time.

We can

  • Port data from relational databases onto NoSQL systems using Sqoop, Pentaho
  • Build efficient read-write in-memory solutions
  • Create visualizations of data stored on relational or non-relational systems using tools such as Pentaho and Tableau
  • Perform social media sentiment analysis

Areas of Expertise

  • Text engineering
  • Data visualization
  • Predictive analytics

Processing in Real-time

System Architecture for Real-time Data Processing
System Architecture for Real-time Data Processing

Lambda Architecture

This a layered approach to achieve near real-time view on an entire dataset. This is not a solution in itself. At QBurst, we essentially map the requirements to the right set of tools to come up with an optimal data processing system.

In-memory solutions for real-time insights

Speed of knowledge delivery can make a huge difference in business decisions. We build read-write in-memory solutions using databases such as Redis, HSQLDB to ensure low latency where required. Distributed memory caching can be implemented in cases where information retrieval alone needs to be speeded up.

Picking a Data Processing Stack

A blind replacement of traditional databases with any non-relational system is hardly a Big Data solution. For the selection of the right toolset, proper evaluation guided by specific requirements of the system is warranted. Never make the choice without a proof of concept.


HBase can handle very large workloads and is suited for data warehousing or large scale data processing and analysis such as web indexing. However, cluster setup can be difficult. Hypertable acts as a smaller, faster HBase, also suitable for search engines and analyzing log data.


This is a scalable, efficient, wide-column database. Like HBase, Cassandra too supports distributed counters. It has high availability and affords operational simplicity as there is only one type of node. Cassandra can be used in real time transaction processing and web analytics.


With query and index properties, MongoDB is SQL-friendly and a popular document store. It is best suited for content management systems, comment storage, or voting.


When the data in hand is interconnected and best represented in graphs, Neo4j is the database to go for. It can be used for network topologies, road maps, social recommendations and more.

Tell us about the data processing problem you now face. We will weigh the options, build some POCs, and give you the most suited solution.