If you want to access databases and query/interact/edit them there are myriad options. I’ve split off the tools that specialize in visualisation and plotting under data dashboards. You can sometimes get db-editing like behaviour out of spreadsheets. See Data organization in spreadsheets
Just some links to follow up later in this section. You can probably move along.
The big-data storage format is conceptually a special weird database for arrays of numbers. Conceptually it would be easy to make a nice viewer for these files. In practice all the major players are annoying for various reasons and I usually write python scripts to visualise the data I need.
HDF® View is some kind of Java viewer that you can download from the HDF Group after registering.
They do not have a normal source repository, and the entire project has a faintly depressing feeling of clunkiness but I am sure it is fine, maybe.
As seen at spatial dataviz, NASA GISS: Panoply 3 is a simple viewer for certain popular scientific data formats, netCDF, HDF and GRIB Data. It is simple but has really weird quirks, e.g. there are some combinations of axes that you cannot view simultaneously, and there is no obvious (to me) pattern in which.
vitables is a python hdf5 gui.
I have not used it because installation was a pain last time I tried, but that was a long ime ago and development has continued since then.
Open and explore HDF5 files in JupyterLab. Can handle very large (TB) sized files, and datasets of any dimensionality.
Based on, I think, H5web. Sounds great, but does not support recent jupyterlab versions, and I have experience-based reasons to regard jupyterlab-as-a-GUI to be a way of adding extra failure points to an app which would be better without jupyter.
plain python script
import h5py import matplotlib.pyplot as plt data_f = h5py.File('myfile.h5', 'r') arr = data_f['test']['a'] columns = 5 rows = 4 fsize = 6 fig = plt.figure(figsize=(fsize *columns/rows, fsize)) for i in range(0, columns*rows): img = arr[i] ax = fig.add_subplot(rows, columns, i+1) plt.imshow(img) ax.set_axis_off() plt.tight_layout(pad = 1) plt.show()
Directus wraps your new or existing SQL database with a realtime GraphQL+REST API for developers, and an intuitive admin app for non-technical users.
OpenRefine (previously Google Refine) is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.
CastleDB is used to input structured static data.
Everything that is usually stored in XML or JSON files can be stored and modified with CastleDB instead.
CastleDB looks like any spreadsheet editor, except that each sheet has a data model.
The model allows the editor to validate data and eases user input.
For example, when a given column references another sheet row, you will be able to select it directly.
CastleDB stores both its data model and the data contained in the rows into an easily readable JSON file.
It uses the JSON format with newlines to store its data, which in turn allows RCS such as GIT or SVN to diff/merge the data files.
Adorably it comes with a video game map-tile editor.
sqlitebrowser does only sqlite but is open source and featureful.
sqlitestudio is a qt-based sqlite manager/browser, also open source.
MySQL workbench is the MySQL graphical tool for database administration. I stopped using this years ago (Version 5.1) because it was unusably unstable and would constantly crash. Maybe it is better these days, a few versions later, but I not longer use MySQL.
datasette provides a read-only Web JSON api for SQLite. This makes it easy to bui;d tools that visualise the data, although is not an actual UI as such.
A simple and lightweight SQL client desktop/terminal with cross database and platform support.
- sqlite-viewer is a pure in-browser SQLite file viewer
Redash consists of two parts:
Query Editor Think of JS Fiddle for SQL queries. It’s your way to share data in the organization in an open way, by sharing both the dataset and the query that generated it. This way everyone can peer review not only the resulting dataset but also the process that generated it. Also it’s possible to fork it and generate new datasets and reach new insights.
Dashboards/Visualizations once you have a dataset, you can create different visualizations out of it, and then combine several visualizations into a single dashboard. Currently it supports charts, pivot table and cohorts.
dbeaver(Java Eclipse app)
Free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports all popular databases: MySQL, PostgreSQL, MariaDB, SQLite, Oracle, DB2, SQL Server, Sybase, MS Access, Teradata, Firebird, Derby, etc.