Hadoop is hard to manage
While Hadoop and the surrounding ecosystem of tools is lauded for its ability to handle massive volumes of structured and unstructured data, the software isn’t easy to manage or use. Hadoop frequently requires extensive internal resources to maintain, and many businesses are left devoting most of their resources to the technology rather than to the actual big data problem they are trying to solve.
Scalability of Change
Many organizations fail to take into account how quickly a big data project can grow and evolve. Big data workloads also tend to be bursty, making it difficult to allocate capacity for resources.
The Big Data Talent Gap
To successfully implement a big data project requires a sophisticated team of developers, data scientists and analysts who also have a sufficient amount of domain knowledge to identify valuable insights.
A key challenge for data science teams is to identify a clear business objective and the appropriate data sources to collect and analyze to meet that objective. Once key patterns have been identified, businesses must be prepared to act and make necessary changes in order to derive business value from them.
The challenge lies in taking into account all costs of the project. Businesses pursuing on-premises projects must remember the cost of training, maintenance and expansion. Big data in the cloud projects must carefully evaluate the service-level agreement with the provider to determine how usage will be billed and if there will be any additional fees.
While the number of big data challenges can be overwhelming, it also presents an opportunity. Those businesses who are able to identify the right infrastructure for their big data project and follow best practices for implementation will see a significant competitive advantage.
- Evaluation of most suitable technology and providing services on the appropriate platform
- Architecture Assessment, Prototyping & Benchmarking
- Installation and Configuration of Hadoop
- Developing applications on Hadoop using Pig, Hive, Scoop, Flume, Kafka, Spark etc
- Hadoop Cluster Monitoring
- Configure Hadoop for performance optimization
- Data Management in Hadoop
CoreCompete has partnered with customers globally to successfully deliver the value of Big Data for their organizations in an agile manner. We have successfully worked on client engagements including:
- Creating Enterprise Data Lake as a foundation for supporting big data innovation
- Delivering the value of machine learning & advanced analytics in specific business contexts
- Development of roadmaps for data management, business intelligence and analytics
- Deployment of Hadoop technologies and open source ecosystem services including Pig, Hive, Scoop, Flume, Kafka, and Spark
- Designing and Implementing next generation technology stacks using Cloud and High Performance Analytics to achieve outcomes that are scalable and cost effective
We combine our decades of experience in data management, big data technologies, advanced analytics, enterprise and cloud deployment models to deliver comprehensive solutions to a broad range of industries such as Banking, Financial Services, Retail and Manufacturing, Healthcare, Telecommunications, and E-Commerce.