| [titanic](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/kaggle/titanic.ipynb) | Predicts survival on the Titanic. Demonstrates data cleaning, exploratory data analysis, and machine learning. |
| [spark](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/spark/spark.ipynb) | In-memory cluster computing framework, up to 100 times faster for certain applications and is well suited for machine learning algorithms. |
| [hdfs](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/spark/hdfs.ipynb) | Reliably stores very large files across machines in a large cluster. |
| [s3cmd](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#s3cmd) | Interacts with S3 through the command line. |
| [s3-parallel-put](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#s3-parallel-put) | Uploads multiple files to S3 in parallel. |
| [s3distcp](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#s3distcp) | Combines smaller files and aggregates them together by taking in a pattern and target file. S3DistCp can also be used to transfer large volumes of data from S3 to your Hadoop cluster. |
| [mrjob](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#mrjob) | Supports MapReduce jobs in Python 2.5+ and runs them locally or on Hadoop clusters. |
| [redshift](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#redshift) | Acts as a fast data warehouse built on top of technology from massive parallel processing (MPP). |
| [kinesis](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#kinesis) | Streams data in real time with the ability to process thousands of data streams per second. |
| [lambda](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#lambda) | Runs code in response to events, automatically managing compute resources. |
* [Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython](http://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1449319793)
* [Building Machine Learning Systems with Python](http://www.amazon.com/Building-Machine-Learning-Systems-Python/dp/1782161406)