https://ift.tt/eA8V8J There are lots of analytics platforms out there. And while they’re all vying for the same customers, they tend to bre...
There are lots of analytics platforms out there. And while they’re all vying for the same customers, they tend to break down into data warehouses, data lakes, and even data “lakehouse” camps. What gave rise to this fragmentation? What are the historical differences between the analytics paradigms, and how many of those constraints still exist on modern platforms?
Leaving data in raw form; performing analysis with different query and programming languages; and building machine learning models are a few of the many reasons companies have found the data lake model appealing. Spinning up task-specific compute clusters and having them share data is another. But data lakes no longer offer the exclusive route to these capabilities.
A modern analytics stack is needed to take on the Internet of Things (IoT), build predictive models, and perform statistical, time series & geospatial analysis. In short, digital transformation relies on an enlightened approach to data analytics. Can data warehouse platforms satisfy these requirements?
To find out, join us for this free 1-hour webinar from GigaOm Research. The Webinar features GigaOm analyst Andrew Brust and special guest Paige Roberts from Vertica, a leader in cross-industry analytical solutions.
The post Bringing Data Warehouse and Data Lake Capabilities Together appeared first on Gigaom.
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