The proposed technical solution for the case study includes the use of several technologies to address the client's problem of lacking a single source of truth and an optimal process for data acquisition, processing, and advanced analytics in real-time. These technologies include Azure Data Lake, Hadoop, NoSQL databases, Docker and Kubernetes for Microservice architecture, Spark for fast data processing, Hive and Hbase for querying and analyzing data, Kafka for handling real-time data streams, REST API for access to the data lake and microservices, Hortonworks for data management, and Hive, Oracle, and SQL Server for relational database management.
Customer Success Outcomes:
The proposed solution for the case study is expected to lead to several customer success outcomes, including:
Improved Data Governance: With a centralized data lake and a single source of truth, the client will have improved data governance, ensuring that all data is accurate, consistent, and up-to-date.
Real-time Analytics: With real-time data ingestion and advanced analytics capabilities, the client will be able to make data-driven decisions in a timely manner, allowing them to stay competitive in the market.
Scalability and Flexibility: The implementation of a microservice architecture, using technologies such as Docker and Kubernetes, will enable the client to easily add new services and features without impacting the existing system.
Improved Performance: With database performance optimization and real-time data processing, the client will experience improved performance, allowing them to handle large amounts of data more efficiently.
Access to Data: With the implementation of REST API, the client will be able to easily access the data lake and microservices, allowing them to integrate their existing systems with the new data architecture.
Improved Reporting: With the implementation of new KPI and reporting, the client will have access to real-time dashboards and reports that provide insights into their data, enabling them to make informed decisions.
Improved Data Science: With Data science complication, the client will have access to tools for data cleaning, transformation, and modeling, and a set of pre-built models that can be used for various analytics use cases.
Improved data security: With Azure Data lake, the client will have improved data security as it provides a scalable, flexible and secure platform to store, process, and analyze large data sets.
Overall, the proposed solution will help the client to improve the efficiency of their data operations, enabling them to make data-driven decisions in a timely manner, and stay competitive in the market.