Data is moving from on-premise to the cloud at a pace that is faster than ever before. However, the cloud presents its own challenges.

To succeed with data in the cloud, there are numerous decisions that a data expert is faced with - to choose a data warehouse or a data lake or both? What is the right way to architect a cloud data lake and how does it differ from on-premise? And how to reduce the time to value by making this data available for analytics and machine learning?

Creating a sustainable competitive advantage using data goes beyond business intelligence to applications ranging from interactive, streaming and clickstream analytics, machine learning, deep learning and more. Data lakes provide an optimal architecture for such new applications of data. However, achieving success with data lakes depends on many factors. In this presentation, we will describe how cloud data lakes provide a future-focused, enterprise-grade solution to achieve sustainable competitive advantage using data.

We will specifically discuss:

• Common use cases for data lakes
• Differences between data lakes and data warehouses and perils of using them interchangeably
• Data lake architecture best practices covering data ingestion, data layout, governance, data processing, security and financial governance

Joydeep Sen Sarma

Co-Founder & CTO,
Qubole India

Prior to co-founding Qubole, Joydeep worked at Facebook where he bootstrapped the data processing ecosystem based on Hadoop.

Joydeep started the Apache Hive project and led Facebook's Data Infrastructure team. He was also a key contributor on the Facebook Messages architecture team, and brought the power of Apache Hbase to Facebook and to the transactional and reporting backends for Facebook Credits.