Most organizations have failed to achieve the value of predictive analytics. Typical data science workflows are resource intensive and the data environments within many companies are messy. Data requ
In a typical data science workflow, the vast majority of effort is spent on the manual and repetitive work of preparing data and presenting results. Leave the data wrangling to Looker.
Looker speeds up the data cleansing and reporting steps of the traditional data science workflow. Anika explains the unique advantages of using Looker’s modern data platform.
As rewarding as results can be, data science within an organization can be extremely tedious and time-consuming. Historically, data munging and cleaning have taken up the majority of the data scientis
The Hadoop ecosystem has improved markedly over the past few years. Moreover, MPP databases seem to slot in nicely as complementary tools to map-reduce batch jobs, in that they allow analytics teams t
Hear why SQL on Hadoop is the future of analytics. Mike Xu, Looker Data Architect and Evangelist shares how updates to SQL query engines like Spark and Presto are allowing on schema database querying.
Amazon Web Services, Craftsy, and Looker discuss the technical and business challenges of analytics on Hadoop. This panel includes topics including common Hadoop use cases & rationale, roles of Hadoo