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A 𝐦𝐞𝐧𝐭𝐞𝐞 said to me that there’s no course that covers end to end curriculum for Data Engineering.
𝐈 𝐬𝐚𝐢𝐝 𝐢𝐭’𝐬 𝐭𝐫𝐮𝐞.
No course can cover the entirety of Data engineering.
There are just too many important variables that have a play –
Batch
Steaming
SQL
Python
Pyspark
Database types – Relational, NoSQL,
Modeling – Dimensional
On prem HDFS and other big data components.
Cloud – AWS, GCP, Azure – Have you heard about Alibaba? 🙂
So, what can we do?
Well! Hiring Organizations realize this and so they look for fitment.
Instead of covering the entirety of data engineering,
if you “consistently” learn for about a year ( 90 mins a day > 500 hours )
𝐮𝐧𝐝𝐞𝐫 𝐭𝐡𝐞 𝐠𝐮𝐢𝐝𝐚𝐧𝐜𝐞 𝐨𝐟 𝐚 𝐦𝐞𝐧𝐭𝐨𝐫 𝐰𝐡𝐨 𝐜𝐚𝐧 𝐬𝐡𝐨𝐰 𝐲𝐨𝐮 𝐭𝐡𝐞 𝐩𝐚𝐭𝐡 ( 𝐦𝐢𝐧𝐝 𝐲𝐨𝐮, 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐰𝐚𝐥𝐤 𝐭𝐡𝐞 𝐩𝐚𝐭𝐡 ),
then you easily crack a data engineer role!
Of course, not everyone would need a year!
Use this amazing guide by Ankur Bhattacharya to upskill!
If you’ve read so far, do LIKE the post 👍
𝐏.𝐒:
Learn no-nonsense AWS Data Engineering with 300 others:
https://aws.sachin.cloud
You Should Know:
To excel in Data Engineering, you need to master a variety of tools and technologies. Here are some practical steps, commands, and codes to get you started:
1. Batch Processing with Python:
import pandas as pd
data = pd.read_csv('data.csv')
processed_data = data.groupby('category').sum()
processed_data.to_csv('processed_data.csv')
2. Streaming with Apache Kafka:
<h1>Start Zookeeper</h1> bin/zookeeper-server-start.sh config/zookeeper.properties <h1>Start Kafka server</h1> bin/kafka-server-start.sh config/server.properties <h1>Create a topic</h1> bin/kafka-topics.sh --create --topic test --bootstrap-server localhost:9092 <h1>Produce messages</h1> bin/kafka-console-producer.sh --topic test --bootstrap-server localhost:9092 <h1>Consume messages</h1> bin/kafka-console-consumer.sh --topic test --from-beginning --bootstrap-server localhost:9092
3. SQL for Data Engineering:
CREATE TABLE employees ( id INT PRIMARY KEY, name VARCHAR(100), department VARCHAR(50) ); INSERT INTO employees (id, name, department) VALUES (1, 'John Doe', 'Engineering'); SELECT * FROM employees WHERE department = 'Engineering';
4. PySpark for Big Data:
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('example').getOrCreate()
df = spark.read.csv('data.csv', header=True, inferSchema=True)
df.show()
5. Cloud Data Engineering with AWS:
<h1>List S3 buckets</h1> aws s3 ls <h1>Copy file to S3</h1> aws s3 cp localfile.txt s3://mybucket/ <h1>Create an EC2 instance</h1> aws ec2 run-instances --image-id ami-0abcdef1234567890 --instance-type t2.micro --key-name MyKeyPair
6. HDFS Commands:
<h1>List files in HDFS</h1> hdfs dfs -ls /user/hadoop <h1>Copy file from local to HDFS</h1> hdfs dfs -put localfile.txt /user/hadoop/ <h1>Read file from HDFS</h1> hdfs dfs -cat /user/hadoop/localfile.txt
What Undercode Say:
Data Engineering is a vast field that requires a combination of skills in batch and stream processing, SQL, Python, PySpark, and cloud platforms like AWS, GCP, and Azure. By consistently practicing and applying these skills, you can become proficient in data engineering. Utilize the provided commands and codes to start your journey and explore further resources to deepen your understanding. Remember, the key to mastering data engineering lies in continuous learning and practical application.
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References:
Reported By: Sachincw Basic – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
