Generic Solutions

Leveraging the power of our innovation labs and years of experience and research building data centric solutions, HoonarTek has produced a series of generic solutions to solve the complex and volume-intensive data challenges of specific industries. These solutions, including numerous accelerators and intellectual property, are available to clients as part of project engagement.

 

With reusability at the core, we collaborate closely with product vendors to implement these solutions, which are than augmented by vertical solutions for the BFSI, Telecom, Retail and Healthcare sectors.

UNIFIED DATA HUBS

Unified data hubs are designed to optimise enterprise-wide data management. These hubs help to eliminate point-to-point data transfers and reduce excessive pressure from operating systems.

 

Hoonartek’s Unified Data Hub ensures:

  • Single source of truth for all downstream applications.
  • Timely delivery of data.
  • Removal of data redundancy.
  • Efficient monitoring and control of data quality.

BIG DATA INTEGRATION

Big data integration vs traditional in-house integration is not a binary issue. At Hoonartek we work with you to identify the correct use-case technology and its integration with your existing ecosystem. This pragmatic approach is geared towards early delivery of tangible results and real value to your business by leveraging internal data with external big data.

 

Our integration is distro-agnostic (eg. Cloudera, Hortonworks, …). and uses the data lake approach, with Ab Initio running either on or off cluster.

EXPRESS>IT INTEGRATION FRAMEWORK

Hoonartek can save significant costs for clients and projects by avoiding the complex and unreliable use of MS Excel to capture business rules. Instead we offer a more productive, reliable and business-friendly solution via Ab Initio’s Express>It framework. This solution includes UI elements exposed through Express>It.

 

Hoonartek empowers clients with an agile approach to building and deploying applications, improving quality and exponentially reducing time to market. The controlled framework ensures that architectural integrity and support readiness is not compromised.

ARCHIVAL FRAMEWORK

At Hoonartek we understand the needs of data-driven organistions for long-term data retention in order to meet service-level agreements. And that a growing database requires increasingly more storage and continuous maintenance.

 

The application of analytical processes on ‘long’ data to improve decision making, and the mandate to comply with legal and governmental regulations, are the key reasons for long term data retention. This leads to database bloating and degraded system performance.

 

Read more...

 

A size-stabilised database helps to reduce the cost of system & storage, as well as operating costs. It also helps to reduce the cost and risk of business disruption caused by uncontrolled database growth, thereby minimising its impact on operations and application performance.

 

Hoonartek helps organizations to manage explosive data growth by providing an effective solution that;

  • Delivers the multiple benefits of a stable  database environment.
  • Provides an inexpensive and easily accessible, archived database

 

MASTER DATA MANAGEMENT

Hoonartek helps customers to fully leverage their Master Data Management (MDM) solutions using a unified data hub that feeds into their 3rd party MDM tools.

 

This helps to consolidate the multiple source systems, implement data-mastering rules and standardization. Our approach exploits best of breed technology and leverages the data hub to feed the MDM solution.

 

For reference data management, Hoonartek’s solution is integrated with Ab Initio’s Metadata Hub technology to leverage its…

 

Read more...
UI elements, views, meta-model and approval workflow capabilities. This empowers clients to manage critical reference data using a simple process that is integrated with their existing Ab Initio platform and integration processes.

 

OPERATIONAL DATA INTEGRATION

From our extensive experience with operational data Integration we know that, outside of core operations, organizations primarily use data integration in areas such as analytics, reconciliation, synchronization and data warehousing (ETL).

 

In recent times a new class of real-time application has begun to drive real-time core operational processes. The common use-cases of this include decision engines and fraud prevention systems. These applications perform algorithmic searches of vast amount of previously homogenized data, match it to search criteria…

 

Read more...

and deliver results to business. All in a fraction of a second.

 

These applications are part of Hoonartek’s new high performance, core data-driven operational applications, built for high availability, fault tolerance and low latency.

TESTING AUTOMATION

Hoonartek implements software testing frameworks for data-driven testing. These include sets of assumptions, concepts and tools to provide support for automated software testing.

 

The key advantages of this solution include lower cost of maintenance, more predictable testing efforts, reduced test cycles and accelerated delivery.

 

We undertake a careful analysis of an enterprise’s test strategy and plan. Our team then produces test-harnesses for each application to initialize, execute and report upon test results.

 

Read more...
Each code change involves a re-execution of the test-harness to validate the test results.

 

The solution is suitable for large-scale implementations and where there’s a need to maintain a test data bed. Other Hoonartek utilities provide automation to include a subset & masked data from live systems into development, as well as quality assurance. This is suitable when there’s a need for referentially intact, but not personally identifiable, data.

 

This automation framework leverages the Ab Initio testing framework and data obfuscation suite.

ENTERPRISE DATA WAREHOUSES AND MARTS

HoonarTek has successfully deployed custom-built warehousing frameworks for many customers. suitable for the typical integration patterns in data warehouses, including dimension handling, fact processing, extraction, loading, transformation and housekeeping.

 

We also work with Ab Initio’s supplied metadata-driven warehouse (MDWp). We have deployed real-time warehouses with continuous (trickle-feed) and batch data along with other supporting enablers such as archival. We also deploy full aggregation layers for warehouse data into marts.