Databases viewers / editors

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

HDF5 viewers

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.


silx-kit/h5web: Web-based HDF5 file viewer is a javascript app viewer that seems to be the lightest and claims to be standalone. The demo looks great. For the life of me, I cannot work out how to install and run it.


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. Manual: hdfview,


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.tight_layout(pad = 1)


Directus: Real-time Data Platform (source):

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.


DBeaver Community | Free Universal Database Tool

DBeaver is a universal database management tool for everyone who needs to work with data in a professional way.

With DBeaver you are able to manipulate your data like in a regular spreadsheet, create analytical reports based on records from different data storages, and export information in an appropriate format. For advanced database users DBeaver suggests a powerful SQL-editor, plenty of administration features, abilities of data and schema migration, monitoring database connection sessions, and a lot more.

Out-of-the box DBeaver supports more than 80 databases.

Having usability as its main goal, DBeaver offers:

  • Carefully designed and implemented User Interface
  • Support of Cloud datasources
  • Capability to work with various extensions for integration with Excel, Git and others.

Claims to support most DBs that have a JDBC connector:

MySQL, PostgreSQL, MariaDB, SQLite, Oracle, DB2, SQL Server, Sybase, MS Access, Teradata, Firebird, Derby, etc.



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.

Sqlite browser

sqlitebrowser does only sqlite but is open source and featureful.

sqlite studio

sqlitestudio is a qt-based sqlite manager/browser, also open source.

MySQL Workbench

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 no 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.



redash (redash source)

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.

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