Technologies
In this case study, a variety of technologies were used to implement a big data solution. Hadoop and Spark were utilized for distributed storage and processing of large amounts of data. Scala was the programming language used to write the code for the application. Cloudera was the distribution of Hadoop used, providing a complete big data platform. Informatica was used for data integration and Microservices architecture was used to build the application. Python was used for data processing and Tensorflow was used for machine learning tasks. ERWIN Data Modeler was used for data modeling and ErWin Web-Portal was used to access the data model. Atlassian FishEye and Crucible were used for code review and collaboration.
Customer Success Outcomes:
Improved data processing and analysis: With the use of Hadoop and Spark, the customer's ability to process and analyze large amounts of data was significantly improved.
Increased scalability: The use of a Microservices architecture, allowed the application to scale more easily as the volume of data grew.
Streamlined data integration: Informatica was used to integrate data from various sources into the big data platform, making the data integration process more efficient.
Enhanced machine learning capabilities: The use of Tensorflow allowed the customer to perform more advanced machine learning tasks on the data.
Better data modeling: ERWIN Data Modeler was used to create a clear and accurate data model, which helped the customer better understand their data and make better data-driven decisions.
Improved collaboration and code review: Atlassian FishEye & Crucible allowed the customer to review code and collaborate more effectively with their team.
Greater access to data: Using ErWin Web-Portal, the customer was able to access the data model more easily and make better data-driven decisions.
Overall, the customer was able to gain greater insights from their data, make better data-driven decisions, and scale their big data solution more easily.