any reason for this project to use hadoop/spark?
Especially when your main goal is learning, I would break it into several phases:


Get familiar with pandas dataframes and visualization using matplotlib. Try loading subsets of your datasets using pandas and visualize them using matplotlib, for example plot the time-series, or word count histograms. This will come in handy lateron to be able to understand the predictions/clusters you will create using machine learning methods. Pandas also provides functions to clean, aggregate and resample the data, which will be useful to align multiple time-series (e.g. weather and time-series) to the same sampling rate.
Familiarize with scikit-learn using the articles and examples in the excellent online documentation. Play with the feature extraction, dimensionality reduction, classification and regression methods relevant to your data, apply them on smaller subsets and visualize the results. Learn about cross-validation and find a good algorithm and parameters that work well on a subset of your data.
One you have a pipeline setup and you want to run it on the full dataset, Spark can come in handy for feature extraction and cross-validation, as those tasks can be often run very well independently and in parallel. Install the Spark libraries on your machine, and reproduce the Pyspark examples (e.g. calculating Pi) in local mode. No need to setup a Hadoop cluster for that, Spark can make full use of your machine in local mode, reading from local files on disk. Once you have that running, try to express the expensive steps of your code from step 2 as Spark RDD operations. As Spark can make use of all cores of your machine, you should already see a speedup - debugging becomes much harder though compared to working with pure Python scripts.
Play with advanced stuff like deep learning, using the word2vec feature extraction. Compare with traditional feature extraction (bag of words).
(optional) If you want to experience the speedup you can get from Spark in a distributed setting, get an account in AWS, put your data on S3 and fire your Spark scripts against a multi-node cluster instantiated using Amazon EMR. 


Regarding data storage, from my experience CSV files or similar work best to load the data into both Pandas and Spark. There are Spark connectors for databases (e.g. Cassandra), but I am not sure about MySQL and Solr. For processed data and intermediate results (e.g. models) I would always use Python's serialization framework pickle, so you can directly get the object instances back into Python without the need to parse file formats and reinstantiate objects.