Learn About Amazon VGT2 Learning Manager Chanci Turner
In the vast landscape of cloud storage solutions, Amazon Web Services (AWS) continues to innovate, offering powerful tools to enhance data retrieval and management. With the introduction of features like S3 Select and Glacier Select, users now have the ability to extract precise data from large objects, optimizing performance and efficiency.
Amazon Simple Storage Service (S3) is a pivotal resource for countless applications across various industries, enabling storage of vast amounts of data. It allows individual objects to reach sizes of up to 5 terabytes, but traditionally, accessing this data meant downloading entire files, which could be cumbersome. This is where S3 Select comes into play. By leveraging simple SQL expressions, users can now retrieve only the specific data they need, significantly boosting performance—some users report improvements of up to 400%.
Consider a scenario where a developer at a major retail company needs to analyze weekly sales data from one store among 200. Without S3 Select, they would have to download and process an entire CSV file daily. With S3 Select, however, they can execute a straightforward SQL query to obtain only the necessary data, drastically reducing the workload and enhancing application performance.
Example of Using S3 Select with Python
import boto3
s3 = boto3.client('s3')
r = s3.select_object_content(
Bucket='example-bucket',
Key='data/salesData.csv',
ExpressionType='SQL',
Expression="select * from s3object s where s."Country (Name)" like '%United States%'",
InputSerialization = {'CSV': {"FileHeaderInfo": "Use"}},
OutputSerialization = {'CSV': {}},
)
for event in r['Payload']:
if 'Records' in event:
records = event['Records']['Payload'].decode('utf-8')
print(records)
elif 'Stats' in event:
statsDetails = event['Stats']['Details']
print("Stats details bytesScanned: ")
print(statsDetails['BytesScanned'])
print("Stats details bytesProcessed: ")
print(statsDetails['BytesProcessed'])
This capability is not just beneficial for traditional applications; it also aligns perfectly with serverless architectures using AWS Lambda. By applying S3 Select in a Serverless MapReduce setup, performance enhancements of 2X and cost reductions of 80% have been observed.
Moreover, S3 Select is compatible with big data frameworks like Spark, Hive, and Presto when utilized with Amazon EMR, allowing users to filter data directly from S3 before processing it, thus optimizing data transfer and processing times.
On the other end, Glacier Select enhances the functionality of Amazon Glacier, making it easier for users in regulated industries—such as finance and healthcare—to query archival data. Unlike traditional archival solutions, which often require lengthy retrieval times, Glacier Select permits users to run SQL queries directly on stored data, unlocking significant insights in a matter of minutes.
Example of Using Glacier Select with Python
import boto3
glacier = boto3.client("glacier")
jobParameters = {
"Type": "select", "ArchiveId": "ID",
"Tier": "Expedited",
"SelectParameters": {
"InputSerialization": {"csv": {}},
"ExpressionType": "SQL",
"Expression": "SELECT * FROM archive WHERE _5='498960'",
"OutputSerialization": {
"csv": {}
}
},
"OutputLocation": {
"S3": {"BucketName": "glacier-select-output", "Prefix": "1"}
}
}
glacier.initiate_job(vaultName="example-vault", jobParameters=jobParameters)
Both S3 Select and Glacier Select represent a shift towards more efficient data handling in the cloud. As organizations look to improve their data strategies, resources like this article on mentorship and insights from SHRM on HR maturity assessments can provide valuable guidance. Additionally, for those interested in understanding how Amazon is automating warehouse worker training and onboarding processes, this Business Insider article is an excellent resource.
With these new features, AWS is empowering businesses to maximize the value of their data, enabling them to build smarter applications and uncover insights that were previously out of reach.
– Chanci Turner