Reading streams of Twitter data, save them to Kafka, then process with Kafka Stream API and Spark Streaming

Overview

Using Streaming Twitter Data with Kafka and Spark

Reading streams of Twitter data, publishing them to Kafka topic, process message using Kafka Stream API and Spark Streaming

Make sure that VPN is switched on, so that you can use Twitter. In some countries Twitter is blocked.

Moreover, you should have own consumer_key, consumer_secret, and access_token with its secret inside config.py file

  • Create environment using conda with Python 3.8:
    • conda create -n python38 python=3.8
    • conda activate python38
    • Check requirements inside requirements.txt and install then using conda:
      • conda install -c conda-forge tweepy==4.4.0
      • conda install -c conda-forge kafka-python==2.0.2
  • Kafka should be installed in your machine, check the documentation for installation. if you use brew with Mac you can use brew install kafka
  • Start zookeeper: zookeeper-server-start /usr/local/etc/kafka/zookeeper.properties, port: 2181
  • On another terminal window start broker: kafka-server-start /usr/local/etc/kafka/server.properties, port: 9092 - In terminal window list topics you have: kafka-topics --list --bootstrap-server localhost:9092
  • Create Kafka topic "tweeter" with 1 partition and no replication because we use local machine: kafka-topics --create --topic tweeter --bootstrap-server localhost:9092 --partitions 1 --replication-factor 1
  • Now list again, the topics you have: kafka-topics --list --bootstrap-server localhost:9092
  • Let's see what we have inside the "tweeter" topic kafka-console-consumer --bootstrap-server localhost:9092 --topic tweeter --from-beginning, absolutely noting), but when we start streaming, data will be generated
  • Now run python kafka_producer.py to start stream Twitter and push message to topic.
  • And now check that the data is inside topic with kafka-console-consumer --bootstrap-server localhost:9092 --topic tweeter --from-beginning
  • Congrats! You have done it!

So what's next?

You can use generated data with Kafka Stream and Spark Streaming, and practice more!

Owner
Rustam Zokirov
15x Engineer • Data Engineer
Rustam Zokirov
Desafio 1 ~ Bantotal

Challenge 01 | Bantotal Please read the instructions for the challenge by selecting your preferred language below: Español Português License Copyright

Maratona Behind the Code 44 Sep 28, 2022
VevestaX is an open source Python package for ML Engineers and Data Scientists.

VevestaX Track failed and successful experiments as well as features. VevestaX is an open source Python package for ML Engineers and Data Scientists.

Vevesta 24 Dec 14, 2022
Created covid data pipeline using PySpark and MySQL that collected data stream from API and do some processing and store it into MYSQL database.

Created covid data pipeline using PySpark and MySQL that collected data stream from API and do some processing and store it into MYSQL database.

2 Nov 20, 2021
t-SNE and hierarchical clustering are popular methods of exploratory data analysis, particularly in biology.

tree-SNE t-SNE and hierarchical clustering are popular methods of exploratory data analysis, particularly in biology. Building on recent advances in s

Isaac Robinson 61 Nov 21, 2022
BErt-like Neurophysiological Data Representation

BENDR BErt-like Neurophysiological Data Representation This repository contains the source code for reproducing, or extending the BERT-like self-super

114 Dec 23, 2022
An interactive grid for sorting, filtering, and editing DataFrames in Jupyter notebooks

qgrid Qgrid is a Jupyter notebook widget which uses SlickGrid to render pandas DataFrames within a Jupyter notebook. This allows you to explore your D

Quantopian, Inc. 2.9k Jan 08, 2023
Conduits - A Declarative Pipelining Tool For Pandas

Conduits - A Declarative Pipelining Tool For Pandas Traditional tools for declaring pipelines in Python suck. They are mostly imperative, and can some

Kale Miller 7 Nov 21, 2021
cLoops2: full stack analysis tool for chromatin interactions

cLoops2: full stack analysis tool for chromatin interactions Introduction cLoops2 is an extension of our previous work, cLoops. From loop-calling base

YaqiangCao 25 Dec 14, 2022
A lightweight, hub-and-spoke dashboard for multi-account Data Science projects

A lightweight, hub-and-spoke dashboard for cross-account Data Science Projects Introduction Modern Data Science environments often involve many indepe

AWS Samples 3 Oct 30, 2021
PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io

PyStan PyStan is a Python interface to Stan, a package for Bayesian inference. Stan® is a state-of-the-art platform for statistical modeling and high-

Stan 229 Dec 29, 2022
Python library for creating data pipelines with chain functional programming

PyFunctional Features PyFunctional makes creating data pipelines easy by using chained functional operators. Here are a few examples of what it can do

Pedro Rodriguez 2.1k Jan 05, 2023
PandaPy has the speed of NumPy and the usability of Pandas 10x to 50x faster (by @firmai)

PandaPy "I came across PandaPy last week and have already used it in my current project. It is a fascinating Python library with a lot of potential to

Derek Snow 527 Jan 02, 2023
CRISP: Critical Path Analysis of Microservice Traces

CRISP: Critical Path Analysis of Microservice Traces This repo contains code to compute and present critical path summary from Jaeger microservice tra

Uber Research 110 Jan 06, 2023
Very basic but functional Kakuro solver written in Python.

kakuro.py Very basic but functional Kakuro solver written in Python. It uses a reduction to exact set cover and Ali Assaf's elegant implementation of

Louis Abraham 4 Jan 15, 2022
MoRecon - A tool for reconstructing missing frames in motion capture data.

MoRecon - A tool for reconstructing missing frames in motion capture data.

Yuki Nishidate 38 Dec 03, 2022
2019 Data Science Bowl

Kaggle-2019-Data-Science-Bowl-Solution - Here i present my solution to kaggle 2019 data science bowl and how i improved it to win a silver medal in that competition.

Deepak Nandwani 1 Jan 01, 2022
Tuplex is a parallel big data processing framework that runs data science pipelines written in Python at the speed of compiled code

Tuplex is a parallel big data processing framework that runs data science pipelines written in Python at the speed of compiled code. Tuplex has similar Python APIs to Apache Spark or Dask, but rather

Tuplex 791 Jan 04, 2023
Developed for analyzing the covariance for OrcVIO

about This repo is developed for analyzing the covariance for OrcVIO environment setup platform ubuntu 18.04 using conda conda env create --file envir

Sean 1 Dec 08, 2021
Dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

Dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

dbt Labs 6.3k Jan 08, 2023
Retail-Sim is python package to easily create synthetic dataset of retaile store.

Retailer's Sale Data Simulation Retail-Sim is python package to easily create synthetic dataset of retaile store. Simulation Model Simulator consists

Corca AI 7 Sep 30, 2022