Joe Celko's Complete Guide to NoSQL: What Every SQL Professional Needs to Know about Non-Relational Databases
Format: PDF / Kindle (mobi) / ePub
Joe Celko's Complete Guide to NoSQL provides a complete overview of non-relational technologies so that you can become more nimble to meet the needs of your organization. As data continues to explode and grow more complex, SQL is becoming less useful for querying data and extracting meaning. In this new world of bigger and faster data, you will need to leverage non-relational technologies to get the most out of the information you have. Learn where, when, and why the benefits of NoSQL outweigh those of SQL with Joe Celko's Complete Guide to NoSQL.
This book covers three areas that make today's new data different from the data of the past: velocity, volume and variety. When information is changing faster than you can collect and query it, it simply cannot be treated the same as static data. Celko will help you understand velocity, to equip you with the tools to drink from a fire hose. Old storage and access models do not work for big data. Celko will help you understand volume, as well as different ways to store and access data such as petabytes and exabytes. Not all data can fit into a relational model, including genetic data, semantic data, and data generated by social networks. Celko will help you understand variety, as well as the alternative storage, query, and management frameworks needed by certain kinds of data.
- Gain a complete understanding of the situations in which SQL has more drawbacks than benefits so that you can better determine when to utilize NoSQL technologies for maximum benefit
- Recognize the pros and cons of columnar, streaming, and graph databases
- Make the transition to NoSQL with the expert guidance of best-selling SQL expert Joe Celko
calculations like SUM that can be decomposed into any number of steps are called distributive and they work with the combiner. Remember your high school algebra? This is the distributive property and we like it. Calculations that can be decomposed into an initial step, any number of intermediate steps, and a final step are called algebraic. Distributive calculations are a special case of algebraic, where the initial, intermediate, and final steps are all the same. COUNT is an example of such a
time when he or she knows it is in an ACID state. In terms of the microfilm analogy, this is how central records look while waiting for the employees to return their marked-up copies. But this also means that we start with the database at time = t0, and can see it at time = t0, t1, t2, …, tn as we wish, based on the timestamps. Insertions, deletes, and updates do not interfere with queries as locking can. Optimistic concurrency is useful in situations where there is a constant inflow of data that
question: How does the staff use the company internal resources with the cloud? If there is an onsite server problem, you can walk down the hall and see the hardware. If there is a cloud problem, you cannot walk down the hall and your user is still mad. There are no purely technical changes today; the lawyers always get involved. My favorite example (for both the up and down side of cloud computing) was a site to track local high school and college sports in the midwest run by Kyle Godwin. He
updates the data item and performs a COMMIT. These phenomena are not always bad things. If the database is being used only for queries, without any changes being made during the workday, then none of these problems will occur. The database system will run much faster if you do not have to try to protect yourself from these problems. They are also acceptable when changes are being made under certain circumstances. Imagine that I have a table of all the cars in the world. I want to execute a
weighted median, which is a better measure of central tendency. For example: x hi lo =========== 1 1 7 1 2 6 2 3 5 3 4 4 < = median – 4.0 3 5 3 3 6 2 3 7 1 The median for an even number of cases: x hi lo =========== 1 1 6 1 2 5 2 3 4 < = median 3 4 3 < = median = 3.5 3 5 2 3 6 1 RANK and DENSE_RANK So far, we have talked about extending the usual SQL aggregate functions. There are special functions that can be used with the window construct. RANK assigns a sequential