Mike Houngbadji

Posted on Jan 20, 2023 • Updated on Feb 4, 2023

Building a Data Lakehouse for Analyzing Elon Musk Tweets using MinIO, Apache Airflow, Apache Drill and Apache Superset

#dataengineering #datascience #python #database

Every act of conscious learning requires the willingness to suffer an injury to one's self-esteem. That is why young children, before they are aware of their own self-importance, learn so easily.
Thomas Szasz

Motivation

A Data Lakehouse is a modern data architecture that combines the scalability and flexibility of a data lake with the governance and performance of a data warehouse. This approach allows organizations to store and analyze large amounts of structured and unstructured data in a single platform, enabling more efficient and effective data-driven decision making.

To deep dive in this, we will build a Data lakehouse solution for analyzing tweets from Elon Musk using MinIO as storage, Apache Drill as query engine, Apache Superset for visualization and Apache Airflow for orchestration. This article will take you through the process of building and utilizing this solution to gain insights and make data-driven decisions.

Then, we’ll use Docker-Compose to easily deploy our solution.

At the end of this project, we'll be able to view the below dashboard of Elon's Tweets with some useful metrics:

Architecture

Table of Content

What is Apache Airflow?
What is MinIO ?
What is Apache Drill ?
What is Apache Superset ?
Code
- get_twitter_data()
- clean_twitter_data()
- write_to_bucket()
- DAG (Direct Acyclic Graph)
- Apache Drill Configuration
- Apache Superset Configuration
- docker-compose & .env files
Results

What is Apache Airflow

Airflow is a platform created by the community to programmatically author, schedule and monitor workflows.

Apache Airflow is an open-source workflow orchestration written in Python. It uses DAG (Direct Acyclic Graphs) to represent workflows. It is highly customizable/flexible and have a quite active community.

In our project, we'll use Apache-Airflow for orchestrating the job that pull data from Twitter API and write to MinIO (s3 compatible object storage).

💡 You can read more [here](https://airflow.apache.org/).

What is MinIO

MinIO offers high-performance, S3 compatible object storage.

MinIO is an open-source Multi-cloud Object Storage and fully compatible with AWS s3. With MinIO you can host your on-premises or cloud Object storage.

Here, we are using MinIO as an alternative to AWS s3 to store the tweets data as objects in buckets.

💡 You can read more [here](https://min.io/).

What is Apache Drill

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage.

Apache-Drill is a Query engine that enables querying data directly from multiple sources in a single query. It allows to query data where it's (the data) sits without having to load into a warehouse. It's supports many sources like AWS s3, RDBMS, MongoDB, Druid, etc.

Hence, with Drill's capability of querying data directly from and s3 buckets, we'll use it to query the tweets data directly from MinIO without having to load into a warehouse.

💡 You can read more [here](https://drill.apache.org/).

What is Apache Superset

Apache Superset is a modern data exploration and visualization platform.

Superset is fast, lightweight, intuitive, and loaded with options that make it easy for users of all skill sets to explore and visualize their data, from simple line charts to highly detailed geospatial charts.

We will use Superset for visualizing charts and get insights on the tweets data stored in MinIO Buckets directly using Apache-Drill Query Engine.

💡 You can read more [here](https://superset.apache.org/).

Code

The full code can be accessed here:

Source code:

https://github.com/mikekenneth/twitter_data-lakehouse_minio_drill_superset

get_twitter_data()

Below is the python Task that pulls Elon’s tweets from Twitter API into a Python list:

import os
import requests
from airflow.decorators import dag, task

@task
def get_twitter_data():
    TWITTER_BEARER_TOKEN = os.getenv("TWITTER_BEARER_TOKEN")

    # Get tweets using Twitter API v2 & Bearer Token
    BASE_URL = "https://api.twitter.com/2/tweets/search/recent"
    USERNAME = "elonmusk"
    FIELDS = {"created_at", "lang", "attachments", "public_metrics", "text", "author_id"}

    url = f"{BASE_URL}?query=from:{USERNAME}&tweet.fields={','.join(FIELDS)}&expansions=author_id&max_results=50"
    response = requests.get(url=url, headers={"Authorization": f"Bearer {TWITTER_BEARER_TOKEN}"})
    response_content = response.json()

    data = response_content["data"]
    includes = response_content["includes"]
    return data, includes

clean_twitter_data()

Below is the python Task that cleans & transforms the tweets in the adequate format:

from uuid import uuid4
from datetime import datetime
from airflow.decorators import dag, task

@task
def clean_twitter_data(tweets_data):
    data, includes = tweets_data

    batchId = uuid4().hex
    batchDatetime = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

    # Refine tweets data
    tweet_list = []
    for tweet in data:
        refined_tweet = {
            "tweet_id": tweet["id"],
            "username": includes["users"][0]["username"],  # Get username from the included data
            "user_id": tweet["author_id"],
            "text": tweet["text"],
            "like_count": tweet["public_metrics"]["like_count"],
            "retweet_count": tweet["public_metrics"]["retweet_count"],
            "created_at": tweet["created_at"],
            "batchID": batchId,
            "batchDatetime": batchDatetime,
        }
        tweet_list.append(refined_tweet)
    return tweet_list, batchDatetime, batchId

write_to_bucket()

Below is the python task transforms the tweets list into a Pandas Dataframe, then writes it in our MinIO Object Storage as a Parquet file.

import os
from airflow.decorators import dag, task

@task
def write_to_bucket(data):
    tweet_list, batchDatetime_str, batchId = data
    batchDatetime = datetime.strptime(batchDatetime_str, "%Y-%m-%d %H:%M:%S")

    import pandas as pd
    from minio import Minio
    from io import BytesIO

    MINIO_BUCKET_NAME = os.getenv("MINIO_BUCKET_NAME")
    MINIO_ROOT_USER = os.getenv("MINIO_ROOT_USER")
    MINIO_ROOT_PASSWORD = os.getenv("MINIO_ROOT_PASSWORD")

    df = pd.DataFrame(tweet_list)
    file_data = df.to_parquet(index=False)

    client = Minio("minio:9000", access_key=MINIO_ROOT_USER, secret_key=MINIO_ROOT_PASSWORD, secure=False)

    # Make MINIO_BUCKET_NAME if not exist.
    found = client.bucket_exists(MINIO_BUCKET_NAME)
    if not found:
        client.make_bucket(MINIO_BUCKET_NAME)
    else:
        print(f"Bucket '{MINIO_BUCKET_NAME}' already exists!")

    # Put parquet data in the bucket
    filename = (
        f"tweets/{batchDatetime.strftime('%Y/%m/%d')}/elon_tweets_{batchDatetime.strftime('%H%M%S')}_{batchId}.parquet"
    )
    client.put_object(
        MINIO_BUCKET_NAME, filename, data=BytesIO(file_data), length=len(file_data), content_type="application/csv"
    )

We used the below object nomenclature to create sub-folders to partition the data by year, month & day:

# Result: filename = "tweets/2023/01/10/elon_tweets_112512_6c0d2558ff464091b9daf75c74f28a51.parquet"
filename = f"tweets/{batchDatetime.strftime('%Y/%m/%d')}/elon_tweets_{batchDatetime.strftime('%H%M%S')}_{batchId}.parquet"

DAG (Direct Acyclic Graph)

Below is the DAG itself that allows specifying the dependencies between tasks and define the scheduling interval (in our case, every hour) of the DAG:

from datetime import datetime
from airflow.decorators import dag, task

@dag(
    schedule="0 * * * *",
    start_date=datetime(2023, 1, 10),
    catchup=False,
    tags=["twitter", "etl"],
)
def twitter_etl():
    raw_data = get_twitter_data()
    cleaned_data = clean_twitter_data(raw_data)
    write_to_bucket(cleaned_data)

twitter_etl()

Apache Drill Configuration

To grant Apache Drill access to our MinIO (s3) bucket, we need to defining Access Keys in the Drill core-site.xml file:

<?xml version="1.0" encoding="UTF-8" ?>
<configuration>

    <property>
        <name>fs.s3a.access.key</name>
        <value>minioadmin</value>
    </property>

    <property>
        <name>fs.s3a.secret.key</name>
        <value>minioadmin</value>
    </property>

    <property>
      <name>fs.s3a.endpoint</name>
      <value>http://minio:9000</value>
    </property>

    <property>
      <name>fs.s3a.connection.ssl.enabled</name>
      <value>false</value>
    </property>

    <property>
      <name>fs.s3a.path.style.access</name>
      <value>true</value>
    </property>

</configuration>

Next, we’ll configure the S3 Storage Plugin to specify the bucket (twitter-data) to be access by Apache Drill in the storage-plugins-override.conf file:

"storage": {
  s3: {
    type: "file",
    connection: "s3a://twitter-data",
    workspaces: {
      "root": {
        "location": "/",
        "writable": false,
        "defaultInputFormat": null,
        "allowAccessOutsideWorkspace": false
      }
    },
    formats: {
      "parquet": {
        "type": "parquet"
      },
      "csv" : {
        "type" : "text",
        "extensions" : [ "csv" ]
      }
    },
    enabled: true
  }
}

💡 You can read more here.

Apache Superset Configuration

To be able to query Apache-Drill, we need to build a custom image of apache/superset using superset_drill.Dockerfile in which we'll install the sqlalchemy-drill module the enables the interactions with Apache-Drill:

FROM apache/superset
# Switching to root to install the required packages
USER root
# install requirements for Apache Drill
RUN pip install sqlalchemy-drill
# Switching back to using the `superset` user
USER superset

💡 You can read more here.

docker-compose & .env files

Below is the .env file that we need to create that hold the environmental variables needed to run our pipeline:

💡 You can read this to learn our to generate the TWITTER_BEARER_TOKEN.

# Twitter
TWITTER_BEARER_TOKEN="TOKEN"

# Minio
MINIO_ROOT_USER=minioadmin
MINIO_ROOT_PASSWORD=minioadmin
MINIO_BUCKET_NAME='twitter-data'

# Superset
SUPERSET_USERNAME=admin
SUPERSET_PASSWORD=admin

# Apache Airflow
## Meta-Database
POSTGRES_USER=airflow
POSTGRES_PASSWORD=airflow
POSTGRES_DB=airflow

## Airflow Core
AIRFLOW__CORE__FERNET_KEY=''
AIRFLOW__CORE__EXECUTOR=LocalExecutor
AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION=True
AIRFLOW__CORE__LOAD_EXAMPLES=False
AIRFLOW_UID=50000
AIRFLOW_GID=0

## Backend DB
AIRFLOW__DATABASE__SQL_ALCHEMY_CONN=postgresql+psycopg2://airflow:airflow@postgres/airflow
AIRFLOW__DATABASE__LOAD_DEFAULT_CONNECTIONS=False

## Airflow Init
_AIRFLOW_DB_UPGRADE=True
_AIRFLOW_WWW_USER_CREATE=True
_AIRFLOW_WWW_USER_USERNAME=airflow
_AIRFLOW_WWW_USER_PASSWORD=airflow
_PIP_ADDITIONAL_REQUIREMENTS= "minio pandas requests"

And below is the docker-compose.yaml file that allow to spin up the needed infrastructure for our pipeline:

version: '3.4'

x-common:
  &common
  image: apache/airflow:2.5.0
  user: "${AIRFLOW_UID}:0"
  env_file:
    - .env
  volumes:
    - ./app/dags:/opt/airflow/dags
    - ./app/logs:/opt/airflow/logs

x-depends-on:
  &depends-on
  depends_on:
    postgres:
      condition: service_healthy
    airflow-init:
      condition: service_completed_successfully

services:
  minio:
    image: minio/minio:latest
    ports:
      - '9000:9000'
      - '9090:9090'
    volumes:
      - './data:/data'
    env_file:
      - .env
    command: server --console-address ":9090" /data
    healthcheck:
      test:
        [
          "CMD",
          "curl",
          "-f",
          "http://localhost:9000/minio/health/live"
        ]
      interval: 30s
      timeout: 20s
      retries: 3

  drill:
    env_file:
      - .env
    image: apache/drill:latest
    ports:
      - '8047:8047'
      - '31010:31010'
    volumes:
      # If needed, override default settings
      - ./conf/drill/core-site.xml:/opt/drill/conf/core-site.xml
      # Register default storage plugins
      - ./conf/drill/storage-plugins-override.conf:/opt/drill/conf/storage-plugins-override.conf
    stdin_open: true
    tty: true

  superset_drill:
    env_file:
      - .env
    ports:
      - '8088:8088'
    build:
      context: .
      dockerfile: superset_drill.Dockerfile
    volumes:
      - ./superset.db:/app/superset_home/superset.db

  postgres:
    image: postgres:13
    container_name: postgres
    ports:
      - "5433:5432"
    healthcheck:
      test: [ "CMD", "pg_isready", "-U", "airflow" ]
      interval: 5s
      retries: 5
    env_file:
      - .env

  scheduler:
    <<: *common
    <<: *depends-on
    container_name: airflow-scheduler
    command: scheduler
    restart: on-failure
    ports:
      - "8793:8793"

  webserver:
    <<: *common
    <<: *depends-on
    container_name: airflow-webserver
    restart: always
    command: webserver
    ports:
      - "8080:8080"
    healthcheck:
      test:
        [
          "CMD",
          "curl",
          "--fail",
          "http://localhost:8080/health"
        ]
      interval: 30s
      timeout: 30s
      retries: 5

  airflow-init:
    <<: *common
    container_name: airflow-init
    entrypoint: /bin/bash
    command:
      - -c
      - |
        mkdir -p /sources/logs /sources/dags
        chown -R "${AIRFLOW_UID}:0" /sources/{logs,dags}
        exec /entrypoint airflow version

Results

When we access the Apache-Airflow Web UI, we can see the DAG and we can run it directly to see the results.
DAG (Apache-Airflow web UI):
Tweets Parquet file generated in our bucket (MinIO Console):
If needed, we can query our Data-Lakehouse directly using Apache-Drill Web Interface:
Finally, we can visualize our Dashboard (I built this already, but it can be easily modified and the data is stored in the superset.db file):

This is a wrap. I hope this helps you.

About Me

I am a Data Engineer with 3+ years of experience and more years as a Software Engineer (5+ years). I enjoy learning and teaching (mostly learning 😎).

You can get in touch with me through Twitter & LinkedIn or mike.kenneth47@gmail.com.

Top comments (2)

Geazi Anc • Jan 28 '23 • Edited

Great article, friend, thank you! However, below some considerations:

In block code response = json.loads(response.content), could be simply replaced by response = response.json();
In the block code batchId = str(uuid4()).replace("-", ""), could be replaced by batchId = uuid4().hex;

Again, good job. I hope that's helps.

Mike Houngbadji • Feb 4 '23

I have updated the code.
Thanks for the suggestion.

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