Different databases have different access patterns. Key-value, document, graph, and columnar databases are useful under different circumstances.
For example, if you are a bank, and you have a database of customers and the transactions they have performed, the ideal access pattern for aggregating the total amount of all transactions might be a columnar store. If the transaction amounts are all in one column, it helps to have all of the columnar entries close together on disk.
But if you want to look at your bank as a social network, and you want to be able to map how money flows between the different people who use your bank, you might want to map your data as a graph database. That would make it easier to query for the connections across the different users in the bank.
CosmosDB is a database from Microsoft that allows for multiple data models and multiple well-defined consistency models. Today’s guest Andy Hoh is a product manager at Azure CosmosDB and he joins the show to describe the product.
Microsoft unveiled CosmosDB at Build, their annual developer conference, which is where I performed this interview. It was a pleasure hanging out at Build in the podcast booths they set up, so thanks to Microsoft for inviting me.
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Deep learning promises to dramatically improve how our world works. To make deep learning easier and faster, we need new kinds of hardware and software–which is why Intel acquired Nervana Systems, a platform for deep learning. Intel Nervana is hiring engineers to help develop a full stack for AI, from chip design to software frameworks. Go to softwareengineeringdaily.com/intel
to apply for a job at Intel Nervana. If you know don’t know much about the company, check out the interviews I have conducted with engineers from the company. You can find these at softwareengineeringdaily.com/intel