Ruth  Nabimanya

Ruth Nabimanya

1622358900

How To Build An n8n Workflow To Manage Different Databases and Scheduling Workflows

If you’ve ever struggled with setting up pipelines for extracting, transforming, and loading data (so-called ETL jobs), managing different databases, and scheduling workflows – know that there’s an easier way to automate these data engineering tasks. In this article, you’ll learn how to build an n8n workflow that processes text, stores data in two databases, and sends messages to Slack.

A few months ago, I completed a Data Science bootcamp, where one week was all about data engineering, ETL pipelines, and workflow automation. The project for that week was to create a database of tweets that use the hashtag #OnThisDay, along with their sentiment score, and post tweets in a Slack channel to inform members about historical events that happened on that day. This pipeline had to be done with Docker Compose and included six steps:

1. Collect tweets with the hashtag #OnThisDay

2. Store the collected tweets in a MongoDB database

3. Extract tweets from the database

4. Process the tweets (clean the text, analyse sentiment)

5. Load the cleaned tweets and their sentiment score in a Postgres database

6. Extract and post tweets with positive sentiment in a Slack channel

This is a fun project that offers lots of learning opportunities about different topics: APIs, text processing with Natural Language Processing libraries, both relational and non-relational databases, social media and communication apps, as well as workflow orchestration. If you’re wondering, like I did, why we had to use two different databases, the answer is simple: for the sake of learning more. Postgres and MongoDB represent not only different database providers, but different kinds of database structures – relational (SQL) vs non-relational (NoSQL) – and it’s useful to be familiar with both.

Though our use case is just for fun, this pipeline can support most common data engineering tasks (e.g. aggregating data from multiple sources, setting up and managing the data flow across databases, developing and maintaining data pipelines).

I was really excited, though also a bit overwhelmed by all the things I had to set up for this project. In total, I spent five days learning the tools, debugging, and building this pipeline with Python (including libraries like TweepyTextBlobVADER, and SQLAlchemy), Postgres, MongoDB, Docker, and Airflow (most frustrating part…). If you’re interested to see how I did this, you can check out the project on GitHub and read this blog post.

But in this article, I’ll show you an easier way to achieve the same result in as much as an hour – with n8n!

#automation #data-engineering #databases #slack-bot #sentiment-analysis #twitter-api #natural-language-processing #no-code

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Buddha Community

How To Build An n8n Workflow To Manage Different Databases and Scheduling Workflows
Ruth  Nabimanya

Ruth Nabimanya

1620640920

How to Efficiently Choose the Right Database for Your Applications

Finding the right database solution for your application is not easy. Learn how to efficiently find a database for your applications.

Finding the right database solution for your application is not easy. At iQIYI, one of the largest online video sites in the world, we’re experienced in database selection across several fields: Online Transactional Processing (OLTP), Online Analytical Processing (OLAP), Hybrid Transaction/Analytical Processing (HTAP), SQL, and NoSQL.

Today, I’ll share with you:

  • What criteria to use for selecting a database.
  • What databases we use at iQIYI.
  • Some decision models to help you efficiently pick a database.
  • Tips for choosing your database.

I hope this post can help you easily find the right database for your applications.

#database architecture #database application #database choice #database management system #database management tool

 iOS App Dev

iOS App Dev

1625133780

SingleStore: The One Stop Shop For Everything Data

  • SingleStore works toward helping businesses embrace digital innovation by operationalising “all data through one platform for all the moments that matter”

The pandemic has brought a period of transformation across businesses globally, pushing data and analytics to the forefront of decision making. Starting from enabling advanced data-driven operations to creating intelligent workflows, enterprise leaders have been looking to transform every part of their organisation.

SingleStore is one of the leading companies in the world, offering a unified database to facilitate fast analytics for organisations looking to embrace diverse data and accelerate their innovations. It provides an SQL platform to help companies aggregate, manage, and use the vast trove of data distributed across silos in multiple clouds and on-premise environments.

**Your expertise needed! **Fill up our quick Survey

#featured #data analytics #data warehouse augmentation #database #database management #fast analytics #memsql #modern database #modernising data platforms #one stop shop for data #singlestore #singlestore data analytics #singlestore database #singlestore one stop shop for data #singlestore unified database #sql #sql database

Ruth  Nabimanya

Ruth Nabimanya

1622358900

How To Build An n8n Workflow To Manage Different Databases and Scheduling Workflows

If you’ve ever struggled with setting up pipelines for extracting, transforming, and loading data (so-called ETL jobs), managing different databases, and scheduling workflows – know that there’s an easier way to automate these data engineering tasks. In this article, you’ll learn how to build an n8n workflow that processes text, stores data in two databases, and sends messages to Slack.

A few months ago, I completed a Data Science bootcamp, where one week was all about data engineering, ETL pipelines, and workflow automation. The project for that week was to create a database of tweets that use the hashtag #OnThisDay, along with their sentiment score, and post tweets in a Slack channel to inform members about historical events that happened on that day. This pipeline had to be done with Docker Compose and included six steps:

1. Collect tweets with the hashtag #OnThisDay

2. Store the collected tweets in a MongoDB database

3. Extract tweets from the database

4. Process the tweets (clean the text, analyse sentiment)

5. Load the cleaned tweets and their sentiment score in a Postgres database

6. Extract and post tweets with positive sentiment in a Slack channel

This is a fun project that offers lots of learning opportunities about different topics: APIs, text processing with Natural Language Processing libraries, both relational and non-relational databases, social media and communication apps, as well as workflow orchestration. If you’re wondering, like I did, why we had to use two different databases, the answer is simple: for the sake of learning more. Postgres and MongoDB represent not only different database providers, but different kinds of database structures – relational (SQL) vs non-relational (NoSQL) – and it’s useful to be familiar with both.

Though our use case is just for fun, this pipeline can support most common data engineering tasks (e.g. aggregating data from multiple sources, setting up and managing the data flow across databases, developing and maintaining data pipelines).

I was really excited, though also a bit overwhelmed by all the things I had to set up for this project. In total, I spent five days learning the tools, debugging, and building this pipeline with Python (including libraries like TweepyTextBlobVADER, and SQLAlchemy), Postgres, MongoDB, Docker, and Airflow (most frustrating part…). If you’re interested to see how I did this, you can check out the project on GitHub and read this blog post.

But in this article, I’ll show you an easier way to achieve the same result in as much as an hour – with n8n!

#automation #data-engineering #databases #slack-bot #sentiment-analysis #twitter-api #natural-language-processing #no-code

Ruth  Nabimanya

Ruth Nabimanya

1620633584

System Databases in SQL Server

Introduction

In SSMS, we many of may noticed System Databases under the Database Folder. But how many of us knows its purpose?. In this article lets discuss about the System Databases in SQL Server.

System Database

Fig. 1 System Databases

There are five system databases, these databases are created while installing SQL Server.

  • Master
  • Model
  • MSDB
  • Tempdb
  • Resource
Master
  • This database contains all the System level Information in SQL Server. The Information in form of Meta data.
  • Because of this master database, we are able to access the SQL Server (On premise SQL Server)
Model
  • This database is used as a template for new databases.
  • Whenever a new database is created, initially a copy of model database is what created as new database.
MSDB
  • This database is where a service called SQL Server Agent stores its data.
  • SQL server Agent is in charge of automation, which includes entities such as jobs, schedules, and alerts.
TempDB
  • The Tempdb is where SQL Server stores temporary data such as work tables, sort space, row versioning information and etc.
  • User can create their own version of temporary tables and those are stored in Tempdb.
  • But this database is destroyed and recreated every time when we restart the instance of SQL Server.
Resource
  • The resource database is a hidden, read only database that holds the definitions of all system objects.
  • When we query system object in a database, they appear to reside in the sys schema of the local database, but in actually their definitions reside in the resource db.

#sql server #master system database #model system database #msdb system database #sql server system databases #ssms #system database #system databases in sql server #tempdb system database

Django-allauth: A simple Boilerplate to Setup Authentication

Django-Authentication 

A simple Boilerplate to Setup Authentication using Django-allauth, with a custom template for login and registration using django-crispy-forms.

Getting Started

Prerequisites

  • Python 3.8.6 or higher

Project setup

# clone the repo
$ git clone https://github.com/yezz123/Django-Authentication

# move to the project folder
$ cd Django-Authentication

Creating virtual environment

  • Create a virtual environment for this project:
# creating pipenv environment for python 3
$ virtualenv venv

# activating the pipenv environment
$ cd venv/bin #windows environment you activate from Scripts folder

# if you have multiple python 3 versions installed then
$ source ./activate

Configured Enviromment

Environment variables

SECRET_KEY = #random string
DEBUG = #True or False
ALLOWED_HOSTS = #localhost
DATABASE_NAME = #database name (You can just use the default if you want to use SQLite)
DATABASE_USER = #database user for postgres
DATABASE_PASSWORD = #database password for postgres
DATABASE_HOST = #database host for postgres
DATABASE_PORT = #database port for postgres
ACCOUNT_EMAIL_VERIFICATION = #mandatory or optional
EMAIL_BACKEND = #email backend
EMAIL_HOST = #email host
EMAIL_HOST_PASSWORD = #email host password
EMAIL_USE_TLS = # if your email use tls
EMAIL_PORT = #email port

change all the environment variables in the .env.sample and don't forget to rename it to .env.

Run the project

After Setup the environment, you can run the project using the Makefile provided in the project folder.

help:
 @echo "Targets:"
 @echo "    make install" #install requirements
 @echo "    make makemigrations" #prepare migrations
 @echo "    make migrations" #migrate database
 @echo "    make createsuperuser" #create superuser
 @echo "    make run_server" #run the server
 @echo "    make lint" #lint the code using black
 @echo "    make test" #run the tests using Pytest

Preconfigured Packages

Includes preconfigured packages to kick start Django-Authentication by just setting appropriate configuration.

PackageUsage
django-allauthIntegrated set of Django applications addressing authentication, registration, account management as well as 3rd party (social) account authentication.
django-crispy-formsdjango-crispy-forms provides you with a crispy filter and {% crispy %} tag that will let you control the rendering behavior of your Django forms in a very elegant and DRY way.

Contributing

  • Django-Authentication is a simple project, so you can contribute to it by just adding your code to the project to improve it.
  • If you have any questions, please feel free to open an issue or create a pull request.

Download Details:
Author: yezz123
Source Code: https://github.com/yezz123/Django-Authentication
License: MIT License

#django #python