bcolz
arctic
bcolz | arctic | |
---|---|---|
1 | 52 | |
955 | 3,031 | |
- | 0.3% | |
0.0 | 7.1 | |
over 1 year ago | 24 days ago | |
C | Python | |
BSD 3-clause "New" or "Revised" License | GNU Lesser General Public License v3.0 only |
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bcolz
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Recommendation for a Database for analysis
What you need for your use case is a column-oriented store. I recommend explore bcolz or apache arrow for a column file-based systems. These are very fast, support memory mapping, uses compression and SSD speed (and even CPU architecture, in case of arrow) optimally almost out of the box, and has good interfaces to Numpy and Pandas (in case you are using Python for final data consumption and analysis). The columnar structure makes it easy to add or delete a column easily (or even dynamically). If you need a more scalable (albeit at the cost of speed) solution, you can devise a schema over a regular columnar db or an nosql db - see arctic from Man group for an example.
arctic
What are some alternatives?
zipline - Zipline, a Pythonic Algorithmic Trading Library
kdb - Companion files to kdb+ and q
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
QuestDB - An open source time-series database for fast ingest and SQL queries
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
pystore - Fast data store for Pandas time-series data
Dask - Parallel computing with task scheduling
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
blaze - NumPy and Pandas interface to Big Data
ta-lib-python - Python wrapper for TA-Lib (http://ta-lib.org/).
NumPy - The fundamental package for scientific computing with Python.
kafka-crypto-questdb - Using Kafka to track cryptocurrency price trends