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Pyodbc and sqlite3 differences

So I am doing a assignment which is to connect to a database and do operations on it. For that I have chosen sqlite3 and for connecting to the database I found the ODBC driver for python is pyodbc.
My questions are, what is the difference between using pyodbc and doing it using the library sqlite3, i.e., import sqlite3 ? And is the pyodbc driver integrated in sqlite3?

pyodbc is an API that allows you to interact with any database that provides provides a odbc driver for it's database. If you use the SQLite library directly and one day want to switch to another database, you will have to revise your code to either use pyodbc or the database specific API for the database you are migrating to.
You can alternatively connect to SQLite using pyodbc by using the SQLite pyodbc driver. See response here: Connect to SQLite3 server using PyODBC, Python

The big advantage of using Python's built-in sqlite3 module is that it is built-in; there are no other dependencies. If your app uses pyodbc and SQLite ODBC then both of those external components must be available for the application to function.
If this is a personal project then you can obviously install what is necessary, but if this is ever going to be widely deployed then you will need to deal with the additional requirements if you choose pyodbc. Specifically, your [Python] app can register a dependency on pyodbc such that pip install your_app also installs pyodbc, but it cannot (practically) accommodate the requirement for the SQLite ODBC driver automatically so your installation instructions will need to address that.
And is the pyodbc driver integrated in sqlite3?
No. The SQLite ODBC driver is completely separate from both Python [sqlite3] and pyodbc.

Connecting ElasticSearch to Superset

I need to connect ElasticSearch to Superset for visualization. When I checked in Superset in Sources> Databases, it mentioned to use SQLAlchemy URI and Database for testing the connection.
In our case, ElasticSearch is connected with Python library and not using SQLAlchemy.
Is there any way to connect Elastic Search with Superset using Python library and if so could you please help by mentioning the way to connect?

you can connect by the way mentioned in the docs.You need to add the dbapi python package mentioned in superset installer document and that will in turn help you connect to the elk using the url elasticsearch+http://{user}:{password}#{host}:9200/
pip install elasticsearch-dbapi

Can Python directly read in a Hive table?

Is there a module or a connector that allows Python directly access Hive tables? Any help would be greatly appreciated.

Python has a Pyhive library that you can use to connect to hive database and run query against them.
it allows you to connect through JDBC and Kerberos Authentication. However, it's bit buggy and JDBC over SSL seems not supported.
This is how you connect
connection = hive.connect(host='HIVE_HOST',
port=10000,
database='temp',
username='HIVE_USERNAME',
password='HIVE_PASSWORD',
auth='CUSTOM')
You can find more details here.
http://allabouthadoop.net/how-to-access-hive-with-python-script/
And these are the two issues I am talking about
https://github.com/dropbox/PyHive/issues/258
https://github.com/dropbox/PyHive/issues/257

MySQL connector

I am trying to build a tool that one step of it is connecting to MySQL database.
Just I am so confused about ODBC. If I want to build a cross platform connector by python, should I use python connector or ODBC connector?
I know JDBC, but ODBC stands for Open Database Connectivity. It looks like more compatible.
Could anyone help me clarify that? Thank you very much.

Python has its own DB API v2.0 abstraction layer to connect to databases (it serves the same purpose of ODBC or JDBC but for Python). You should use one of the DB API v2.0 compliant oursql, MySQLdb or PyMySQL packages to connect to MySQL from Python. All these packages are cross platform and will work on Linux, Windows and MacOS X. oursql, and MySQLdb are wrappers for libmysql and PyMySQL is a pure Python implementation.
Note that there are DB API v2.0 implementations (like pyodbc) that provide ODBC connectivity, so conceptually you could connect to MySQL via ODBC, but this would have inferior performance than the above mentioned "native" drivers because of the extra abstraction layers.

Best way to access Firebird DB from a remote desktop.

I have a Firebird DB set up on my computer and I want to be able to retrieve data from a different computer. What is the best way to go about this?
I am running windows and using python.

Install firebird client to the client pc
To connect firebird programmaticaly from python you should install a python Firebird driver.
For Python 2.x, you can use kinterbasdb. This is the legacy driver and I think it is not actively developed but only maintained.
To connect windows based server database from kinterbasdb you can use
Import kinterbasdb as k
k.init(type_conv = 300) #
con = k.connect(dsn='127.0.0.1:c:\\db\\test.fdb', user='sysdba', password='masterkey', charset='YOUR_CHARSET', dialect=3)
Of course you should adjust connection parameters according to your system. Kinterbasdb documentation is here
If you want use an ORM, you can use SqlAlchemy which uses kinterbasdb for Firebird Support
For Python 3k you can use pyfirebirdsql which also supports Python 2.5+ and under active development, but not supported by SqlAlchemy yet.

Run Firebird server on the computer with database file and connect to it from remote computer. You will need in Firebird client library installed on remote computer.

I think we need a bit more info.
Do you want database access - as in "I want to be able to edit table layout and define new tables, views, procedures and so on" ?
Or do you only need to get data from the database using python ?
The latter could be achieved by installing a Firebird client (in essence its a dll (fbclient.dll)) and then use a connect string from python to connect to your database.