First of all, we must empower analysts to use the skills they have by supporting the lingua franca of analytics: SQL. Presto SQL access to big data breaks down big data silos from a skills perspective.
Second, we must enable interactive, ad-hoc queries. SQL queries on big data have not been impossible in the past, but understanding and using tools such as Hive for handling such queries was difficult for most analysts. Furthermore, even if analysts did use Hive, interactive query performance wasn’t the best. Modern analysts don’t expect to ask a question today and get the answer tomorrow; they need an interactive tool.
Third, wider use of big data requires seeing big data as one of many data sources. Rather than framing separate queries for separate data stores, analysts should be able to ask questions that reach out to multiple sources and platforms and bring back answers as needed to answer the query. This begins breaking down the siloing of big data from other data repositories.
An even greater expansion of the use of big data requires enabling users who may or may not know Presto SQL themselves to leverage familiar BI tools to generate these queries behind the scenes. This entails not just supporting SQL, but the machine generated SQL that BI tools speak.
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