Many data-driven organizations are adopting data lakes to support data discovery, data science, and real-time operational analytics capabilities. In fact, one-third of DBTA readers are planning projects for 2017. The ability to inexpensively store large volumes of data from diverse sources and make that data readily accessible to workers and applications across the enterprise is a huge advantage for companies pursuing new types of analytics, especially those involving the Internet of Things and cognitive computing use cases.
However, building and maintaining a data lake to support new analytics applications involves a number of technical challenges:
- Data architecture
- Data integration data security and governance
- Data security
- Data governance
In this webinar, your hosts highlight common pitfalls, key best practices, and success stories in building and maintaining a data lake for big data analytics that you can trust.