It’s 2017, and big data challenges are as real as they get. Our customers have petabytes of data living in elastic and scalable commodity storage systems such as Azure Data Lake Store and Azure Blob storage.
One of the central questions today is finding insights from data in these storage systems in an interactive manner, at a fraction of the cost.
Interactive Query leverages [Hive on LLAP] in Apache Hive 2.1, brings the interactivity to your complex data warehouse style queries on large datasets stored on commodity cloud storage.
In this session, you will learn how technologies such as Low Latency Analytical Processing [LLAP] and Hive 2.x are making it possible to analyze petabytes of data with sub second latency with common file formats such as csv, json etc. without converting to columnar file formats like ORC/Parquet. We will go deep into LLAP’s performance and architecture benefits and how it compares with Spark and Presto in Azure HDInsight. We also look at how business analysts can use familiar tools such as Microsoft Excel and Power BI, and do interactive query over their data lake without moving data outside the data lake.