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Course URL: Data Engineering Bootcamp
Newsletter URL: Data Analytics & Science Insights
You Should Know:
This 5-week bootcamp covers cutting-edge tools and frameworks for data engineering, including Airflow, Iceberg, Databricks, Spark, Kafka, and Azure services. Below are key commands, code snippets, and steps to practice these technologies:
Week 1 – Airflow & Iceberg | Azure Data Lake Gen2
1. Apache Airflow: Automate workflows with DAGs (Directed Acyclic Graphs).
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime
def hello_world():
print("Data Pipeline Executed!")
dag = DAG('data_pipeline', start_date=datetime(2023, 1, 1), schedule_interval='@daily')
task = PythonOperator(task_id='hello_task', python_callable=hello_world, dag=dag)
- Azure Data Lake Gen2: Use `az` CLI to manage storage.
az storage account create --name <storage_name> --resource-group <resource_group> --location eastus --sku Standard_RAGRS az storage fs create --name <filesystem_name> --account-name <storage_name>
Week 2 – Data Lakes & Delta Table
1. Delta Lake: Create and query Delta tables.
from delta import DeltaTable
df.write.format("delta").save("/mnt/delta/events")
DeltaTable.forPath(spark, "/mnt/delta/events").toDF().show()
Week 3 – Databricks & Advanced Spark | Azure SQL Database
1. Spark SQL: Optimize queries.
SELECT FROM delta.<code>/mnt/delta/events</code> WHERE date > '2023-01-01'
2. Azure SQL DB: Connect via `sqlcmd`.
sqlcmd -S <server>.database.windows.net -U <user> -P <password> -d <database>
Week 4 – Streaming with Spark, Kafka & Delta Live
1. Kafka Commands: Start a producer.
kafka-console-producer --broker-list localhost:9092 --topic data_stream
2. Spark Streaming: Read from Kafka.
df = spark.readStream.format("kafka").option("kafka.bootstrap.servers", "localhost:9092").load()
Week 5 – Unstructured Data + AI Contexts | Azure Data Factory
1. Azure Data Factory CLI: Trigger pipelines.
az datafactory pipeline create-run --resource-group <rg> --factory-name <factory> --name <pipeline>
What Undercode Say:
This bootcamp is a goldmine for data engineers. Key takeaways:
– Master Airflow for orchestration.
– Leverage Delta Lake for ACID transactions.
– Use Kafka + Spark for real-time analytics.
– Deploy Azure services for scalable pipelines.
Linux/IT Commands to Practice:
Monitor Kafka topics kafka-topics --list --bootstrap-server localhost:9092 Spark submit job spark-submit --master yarn --deploy-mode cluster --class com.example.Main app.jar Azure blob upload az storage blob upload --account-name <name> --container-name <container> --file <local_path> --name <blob_name>
Expected Output:
A job-ready data engineer with hands-on experience in modern data stacks.
Prediction:
By 2025, Delta Lake and Spark will dominate data engineering, replacing traditional ETL tools. Azure and Databricks will lead cloud-based analytics.
For enrollment, use code “SAI” for 20% off.
References:
Reported By: Saibysani18 8 – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
