Metaprogramming Python for Big Data
For many companies, understanding what is going on in your business involves lots of data. But, how do you query 10s of billions of data points? How can a company begin to make sense of so much information? Ville Tuulos, Principle Engineer at AdRoll, a company producing tons of big data, demonstrates how AdRoll uses Python to squeeze every bit of performance out of a single high-end server. They m
Mostly encoding - Amazon Redshift
Mostly encodings are useful when the data type for a column is larger than most of the stored values require. By specifying a mostly encoding for this type of column, you can compress the majority of the values in the column to a smaller standard storage size. The remaining values that cannot be compressed are stored in their raw form. For example, you can compress a 16-bit column, such as an INT2
Delta encoding - Amazon Redshift
Delta encodings are very useful for date time columns. Delta encoding compresses data by recording the difference between values that follow each other in the column. This difference is recorded in a separate dictionary for each block of column values on disk. (An Amazon Redshift disk block occupies 1 MB.) For example, suppose that the column contains 10 integers in sequence from 1 to 10. The firs
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