Good article about In-Memory Data Grids on Cubrid’s blog by Ki Sun Song.
The features of IMDG can be summarized as follows:
- Data is distributed and stored in multiple servers.
- Each server operates in the active mode.
- A data model is usually object-oriented (serialized) and non-relational.
- According to the necessity, you often need to add or reduce servers.
Even if you don’t read it all, but plan to use an IMDG solution, the first two questions you want to ask your vendor are: what the approach you are proposing to deal with the limited memory capacity and what’s the strategy for reliability. You’ll get good answers from well established products, but these answers are not necessarily the ones that provide the exact requirements your solution will need.
Original title and link: Main Features of In-Memory Data Grids ( ©myNoSQL)
Goldmacher estimated that YouTube consumption—user uploads of 48 hours of video a minute and 3 billion videos a day along with roughly 45 petabytes of viewed videos a day—would require at least 9 full-rack Exadata machines at $1.5 million each. There would be at least 18 Exadata machines to handle spikes. Those machines would add up to 14 Exalogic devices to serve data at $1.1 million per system. The software stack under Oracle would include WebLogic middleware, Oracle databases, Exadata optimized storage and Oracle as operating system. The open source comparison included JBoss middleware, MySQL, Hadoop and Red Hat Enterprise Linux as the OS.
Credit Peter Goldmacher (Cowen & Co. analyst)
Two comments (the only I have):
- what advantages would the enterprise stack offer to justify a 5x cost?
- in case all numbers are completely wrong, what’s the advantage of the enterprise stack?
Original title and link: Enterprise Big Data Stack vs Open Source Big Data Stack ( ©myNoSQL)