Big Data

Hoonartek offers a complete portfolio of big-data services, from thought leadership to roadmap, design and development with leading technologies and platforms. We work with clients from early stage/POC, undertaking implementation initiatives to setup the platform and supporting processes. Our solutions are independent of our clients’ ecosystem (eg. MapR, HortonWorks, IBM, Cloudera) and are based on fundamental principles of efficient and reliable data integration.


As well as being vendor-independent, Hoonartek works holistically towards implementing tools around Hadoop, including Hive, HBase, Pig, MongoDB, Oozie, Flume, Splunk, Scala and Spark. HoonarTek’s big-data offerings are organized into two main areas, administration and application development.


Hoonartek’s Big Data Application Services (BDAS) is focused on building and developing processes, capabilities and applications around a client’s big data technology stack. Today, businesses face complex technology challenges that are rarely solved with one big-data tool. HoonarTek manages full application development for big data and allied tools.




Our BDAS group offers comprehensive design and covers multiple use cases that are constantly evolving and transforming and being implemented at client locations across the world.

hoonartek’s offering includes:

  • Project definition of big-data initiative, roadmap & implementation strategy.
  • Complete architecture definition, design for application and integration components, and its implementation.
  • For mature clients, Hoonartek provides end-to-end implementation using the big data stack. Our BDAS service is focused from design to go-live for selected use case/s and project needs.
  • Implementation of data-lake to complement existing information management platforms, including data warehouse, data marts and other analytical data stores.
  • Implementation of reporting tools and environments with big-data technology.
  • Integration of Ab Initio for both on-cluster and off-cluster data processing.


Hoonartek’s Platform Administration Services (PAS) addresses big-data ecosystem administration and architecture.




Hoonartek’s Platform Administration Services include:


  • Creating the software deployment architecture; encompassing design, configuration and implementation, based on assessment and use-case scenarios.
  • Enabling up-stream and down-stream interfaces of the big data platform for different configurations.
  • Creating prototypes of various integration patterns. These are built with HDFS to easily integrate with their existing ecosystem and enables them to rapidly ramp up their projects.
  • Complete data store design, optimization, schema design and interfaces.

Emerging Data Science Practice

Hoonartek as a natural extension to data engineering started making in roads into data sciences. The skills and experience in data analytics consulting and solutions-development add significant value to customers in retail and business banking division in your endeavours to retain and grow profitable relationships within your customer base. Hoonartek’s value proposition pertaining to advanced analytics consists of a range of specialist services and solutions that deliver real value to business:


Hoonartek’s Data Science practice aims to cover

  • Advanced Analytical Consultancy
  • Geo-spatial practice
  • Centralised Leads Management and Retention Management
  • Content Harvesting
  • Business Intelligence


These are customer centric views for customers which help in answering questions to customer lifecycle management from acquiring customers, growing relationship, retaining and rewarding.


Hoonartek possesses expert skills adhering to CRISP-DM methodology of data mining. This is across the broad range of conventional statistical, mathematical & operations-research modelling techniques, and is also proficient in several sophisticated modelling techniques at the cutting edge of data science.Read more an interesting “Data Science” blog and learn more details about our capability and how we can assist.




Big Data