A Big Data Lake for Multilevel Streaming Analytics Ruoran Liu Haruna Isah Farhana Zulkernine School of Computing School of Computing School of Computing Queen’s University Queen’s University Queen’s University Kingston, Canada Kingston, Canada Kingston, Canada
[email protected] [email protected] [email protected] Abstract—Large organizations are seeking to create new security, or authorization [3]. It is a large storage repository that architectures and scalable platforms to effectively handle data holds a vast amount of raw data in its native format until it is management challenges due to the explosive nature of data rarely needed. This is in contrast to the traditional data warehouse seen in the past. These data management challenges are largely approach, also known as schema on write, which requires more posed by the availability of streaming data at high velocity from upfront design and assumptions about how the data will be used. various sources in multiple formats. The changes in data paradigm In a data warehouse, adding data to a database requires data to have led to the emergence of new data analytics and management be transformed into a pre-determined schema before it can be architecture. This paper focuses on storing high volume, velocity loaded. This step often consumes a lot of time, effort, and and variety data in the raw formats in a data storage architecture expense before the data can be used for downstream applications called a data lake. First, we present our study on the limitations of [4]. A data lake is meant to complement and not replace a data traditional data warehouses in handling recent changes in data paradigms.