The Client

Based in Chicago, the client is one of the prestigious infrastructure service providers which provide application performance management solution for organizations with widely distributed computing environments.

The Business Challenges

Business Challenges

  • Inability to aggregate multi-format data from different sources
  • Large volume of data was becoming difficult to monitor
  • Decreasing operational efficiency of stores due to improper monitoring and delay in problem resolution
  • Alert notification was crucially required
  • Needed comparison reports for understanding business logics

Technical Challenges

  • Multiple types of data sources like MySQL, Intersystem’s Cache, SNMP Messages and Traps
  • No centralized monitoring of network utilization, bandwidth utilization, WAN utilization, DSL data transfer etc
  • Unable to fetch data of small duration
  • No notification of server latency and hence no detection of slow performance
  • Data lying in memory of switches and servers was to be stored
  • Notification of errors and breach of threshold was necessary to take action at the required time
Diaspark’s Solution

Diaspark’s extensive knowledge and over two decades of experience in data engineering services helped the client to bring in business intelligence functionalities

Solution’s Core Features
  • Enhanced functionalities such as evaluation of ETL and reporting tools
  • POC developed to verify and finalize the technology most suitable
  • Designed data model and developed data warehouse
  • Data transformed and integrated from more than 500 data sources that includes MySQL, Intersystem’s Cache, SNMP Messages and Traps
  • Services were created to receive SNMP message and traps from each store in an interval of every 5 and 15 minutes
  • Data cubes were generated for reporting and data mining
  • Created dashboard for end user to generate queries interactively to prepare various reports like country wise/state wise/region wise/store wise comparison of data on time, volume, bandwidth and numerous other factors
  • A map was created displaying all 500 stores across the USA and the performance of each stores displayed on the basis of color coding along with network errors and notifications
  • Large number of reports in various formats (charts, dashboards, tabular) were created using MDX which helped in detailed analysis
  • Customized Google maps to show various stores and related information
  • Created SSIS packages to get data from OLTP database to data warehouse
  • Used SSAS to create Facts, Dimensions and cubes to create OLAP database
Benefits to the Client
  • More granular data availability in data warehouse, drilled down up to 1 minute
  • Better monitoring of the network performance across stores
  • Significant reduction in cost because of reduced downtime of hardware devices, better performance, reduced
  • Escalated operational efficiency
  • Reduced downtime and network issues because of timely monitoring and problem resolution
  • Increase in customer retention and sales

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