By Jose Hernandez
I often hear (and read about) discussions around the implementation of a data lake to replace a data warehouse, or deciding on a data lake or a data warehouse? It’s not an either-or question. Data lakes and data warehouses serve similar purposes but for different situations and must also work together to support modern BI and analytics needs.
Before I tackle “how data lakes...
Probably the most important change that has occurred in the last few years is how ubiquitous it is to pay for what we consume (or use), for what we need at the time we need it. From car rides, to dog walking to TV watching, it’s a trend that continues to gain traction. This trend started with cloud computing and the ability to pay for what we use in terms of infrastructure (IaaS),...
By Ashish Trivedi
Combining structured data, semi-structured data to perform analytics is hard. If you’re like most, the data that you need and use every day are spread out throughout your organization. Some is found in your enterprise data warehouse, some in spreadsheets, and some in ODSs. This only accounts for structured data. Semi-structured data will be found in XML...
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