Essential Tools For Your Big Data Arsenal

Editor's Notes

• #7: Big Data is new, and you’re likely going to fail as you start. But it’s almost guaranteed, as well, that you won’t know which data to capture, or how to leverage it, without trial and error. As such, if you were to “design for failure,” what key things would you need? You need to reduce the cost of failure, both in terms of time and money. You’d need to build on data infrastructure that supports your iterations toward success and then rewards you by making it easy and cost effective to scale.
• #8: IBM designed IMS with Rockwell and Caterpillar starting in 1966 for the Apollo program. IMS's challenge was to inventory the very large bill of materials (BOM) for the Saturn V moon rocket and Apollo space vehicle.
• #9: Loading a paper tape reader on the KDF9 computer.
• #10: IBM designed IMS with Rockwell and Caterpillar starting in 1966 for the Apollo program. IMS's challenge was to inventory the very large bill of materials (BOM) for the Saturn V moon rocket and Apollo space vehicle.
• #14: This is helpful because as much as 95% of enterprise information is unstructured, and doesn’t fit neatly into tidy rows and columns. NoSQL and Hadoop allow for dynamic schema.
• #21: The industry is talking about Hadoop and MongoDB for Big Data. So should you
• #23: Why not Hbase? MongoDB dramatically more popularMuch easier to useWorks from small scale to large scaleFar closer to the functionality available in RDBMS, including geospatial, secondary indexes, text search40+ languages mean you can work in your preferred programming language
• #24: The industry is not betting on RDBMS for Big Data. Neither should you
• #26: This is where MongoDB fits into the existing enterprise IT stackMongoDB is an operational data store used for online data, in the same way that Oracle is an operational data store. It supports applications that ingest, store, manage and even analyze data in real-time. (Compared to Hadoop and data warehouses, which are used for offline, batch analytical workloads.)
• #28: OrSo not everyone would agree with the term offline big data
• #29: What each of these has in common is that they’re retrospective: they’re about looking at the past to help predict the future. The learnings from these Hadoop applications end up being applied by a different technology. This is where MongoDB comes in.
• #32: Marketing has been breaking people down into segments (Hadoop - user base as a whole) for a long time, while new marketing needs to focus on individuals (user base as a user). CriteoIf you're only optimizing on the aggregate data, you're missing out on the personalization. but i you only do the individual, you're missing out on patterns across your entire user baseYou want to do both but the systems needed to do this (on a single data set) tend to be architecturally at odds with each other.One dataset. That system lives in two different databases which are taking care of the different processing needs on the dataIn order for Hadoop/column-level processing to be useful, you want to have lots of columns. You need your real-time data store to be very rich, or you'll lose information. You don't want to be simplifying that data from the start by putting it into an RDBMS or key-value store. as a specific example, people talk about Hadoop used for log file analysis. Those log files lack a lot of context coming out of your web server for example (IP address, time stamp, etc. but not any real context as to what the log files mean - this is the watch movie button). You can actually have a much richer version of that interaction by keeping that data in a doc db that describes what is actually happening in that log