ANALYTICS AND BIG DATA

What is big data?

Worldwide accepted 'Big data' refers the huge amount of data at one place, whether it is structured or unstructured. Smart devices, Desktop PC's as well as sensors transit it effectively. Big Data is taking place from innumerable sources with an alarming force, high volume, and variety. In order to achieve out of the box services from Big Data, you need excellent processing capabilities, authentic analytics capacity, and exceptional skills. It leads to better decision power and strategic business moves.

Big Data Development Services:

  • Big Data project assessment
  • System analysis, road mapping and system architecture to support Big Data initiatives
  • Data warehousing architecture, development, and support
  • ETL design and development
  • Querying and reporting
  • Providing security solutions to handle dynamic, unstructured and structured data
  • Custom software development to handle Big Data specific objectives
  • And more…

Transforming Retail Industry

Taking the example of the retail industry, big data technologies like Hadoop can be used to reduce license cost and integrate huge volumes of data, with a minimal impact on your reporting structure.

Hadoop MapReduce is used to leverage data from Teradata. Enterprises can transform and aggregate huge volumes of data into relevant customer insights with the help of technologies like Hive.

Our solution recommends the integration of MongoDB and Hive to integrate these insights and improve the efficiency of your reporting and monitoring systems thereby impacting the overall business growth.

Solutions provided by us have helped our clients achieve efficient data migration and quicker aggregation of large datasets at an affordable cost.

Leveraging Big Data to Avoid Cybersecurity threats

Modern enterprises are faced with challenges of large-scale detection of cybersecurity threats, single platform dependency for detecting repetitive frauds, or Heuristic, probabilistic and signal detection analytics

Horizon Soft leverages big data technologies for feature extraction including conventional ETL, Lucene indexing, and sanitation algorithms. In addition, efficient data mining processes involve analytics such as Mahout classification and other machine learning capabilities. Pattern recognition and heuristic inference engines on Drools are applied for gaining relevant business insights from structured as well as unstructured data.

Our team of solution architects has vast experience in delivering solutions such as real-time detection of high vulnerability attacks, parallel processing of real-time collected data, and data processing of huge datasets for detecting cybersecurity attacks.