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A cloud data pipeline reference:
- Ingest → Process → Analyze: Full lifecycle coverage across AWS, Azure & GCP
- Streaming: Kinesis (AWS) | Event Hubs (Azure) | PubSub (GCP)
- Storage: S3/Glacier | Azure Data Lake | Cloud Storage
- Compute: EMR/Glue | Databricks | DataProc/DataFlow
- Warehouse: Redshift | Synapse | BigQuery
- Visualize: QuickSight | Power BI | Colab/DataLab
You Should Know:
1. AWS Kinesis Command:
To create a Kinesis stream:
aws kinesis create-stream --stream-name MyStream --shard-count 1
2. Azure Event Hubs Command:
To create an Event Hub:
az eventhubs eventhub create --name MyEventHub --resource-group MyResourceGroup --namespace-name MyNamespace
3. Google Pub/Sub Command:
To create a Pub/Sub topic:
gcloud pubsub topics create MyTopic
4. AWS S3 Command:
To upload a file to S3:
aws s3 cp myfile.txt s3://mybucket/
5. Azure Data Lake Command:
To upload a file to Azure Data Lake:
az storage blob upload --account-name MyStorageAccount --container-name MyContainer --name myfile.txt --file myfile.txt
6. Google Cloud Storage Command:
To upload a file to Google Cloud Storage:
gsutil cp myfile.txt gs://mybucket/
7. AWS EMR Command:
To create an EMR cluster:
aws emr create-cluster --name MyCluster --release-label emr-6.5.0 --instance-type m5.xlarge --instance-count 3 --use-default-roles
8. Azure Databricks Command:
To create a Databricks workspace:
az databricks workspace create --name MyWorkspace --resource-group MyResourceGroup --location eastus
9. Google DataProc Command:
To create a DataProc cluster:
gcloud dataproc clusters create MyCluster --region us-central1
10. AWS Redshift Command:
To create a Redshift cluster:
aws redshift create-cluster --cluster-identifier MyCluster --node-type dc2.large --master-username admin --master-user-password Password123 --cluster-type single-node
11. Azure Synapse Command:
To create a Synapse workspace:
az synapse workspace create --name MyWorkspace --resource-group MyResourceGroup --storage-account MyStorageAccount --file-system MyFileSystem --sql-admin-login-user admin --sql-admin-login-password Password123
12. Google BigQuery Command:
To create a BigQuery dataset:
bq mk --dataset MyProject:MyDataset
13. AWS QuickSight Command:
To create a QuickSight analysis:
aws quicksight create-analysis --aws-account-id 123456789012 --analysis-id MyAnalysis --name MyAnalysis --source-entity file://analysis.json
14. Azure Power BI Command:
To create a Power BI workspace:
az powerbi workspace create --name MyWorkspace --resource-group MyResourceGroup
15. Google Colab Command:
To open a Colab notebook:
jupyter notebook MyNotebook.ipynb
What Undercode Say:
Big Data pipelines are the backbone of modern data-driven organizations. Leveraging cloud platforms like AWS, Azure, and GCP allows for scalable, efficient, and cost-effective data processing. The commands provided above are essential for setting up and managing these pipelines. Whether you’re ingesting data, processing it, or visualizing insights, mastering these tools will give you a competitive edge in the world of Big Data.
References:
Reported By: Sahnlam Big – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
