Core Services Basics

This page is a single tour across all ten core services, one minimal call at a time.

Each example is deliberately small: confirm who you are, then touch identity, storage, databases, messaging, compute, networking, and observability in turn.

The point is not depth - each service has its own "your first X" page for that. The point is to feel how the same SDK shape repeats everywhere.

Prerequisites

Python: pip install boto3 (boto3 1.43.x, Python 3.10+). TypeScript: install the per-service clients you use, for example npm install @aws-sdk/client-s3 @aws-sdk/client-sqs (Node.js 18+).
Credentials configured once via aws configure, environment variables, or an IAM role. The SDK finds them automatically.
A default region set with AWS_REGION, or passed per client. These examples assume us-east-1.

Basic Examples

1. Confirm Your Identity (IAM + STS)

Before anything else, check which identity your SDK calls run as.

# --- Python (boto3) ---
import boto3
 
sts = boto3.client("sts")
me = sts.get_caller_identity()
print(me["Account"], me["Arn"])

// --- TypeScript (AWS SDK v3) ---
import { STSClient, GetCallerIdentityCommand } from "@aws-sdk/client-sts";
 
const sts = new STSClient({});
const me = await sts.send(new GetCallerIdentityCommand({}));
console.log(me.Account, me.Arn);

GetCallerIdentity needs no permissions, so it always works and is the fastest sanity check.
The returned Arn tells you whether you are a user, an assumed role, or a federated session.
This is the identity IAM will evaluate for every other call on this page.
If this fails, fix credentials before debugging any service.

Related: AWS IAM via SDK: Users, Roles & Policies - the identity layer in depth.

2. Store a File (S3)

Put an object into a bucket, then read it back.

# --- Python (boto3) ---
import boto3
 
s3 = boto3.client("s3")
s3.put_object(Bucket="my-bucket", Key="hello.txt", Body=b"hi")
body = s3.get_object(Bucket="my-bucket", Key="hello.txt")["Body"].read()
print(body.decode())

// --- TypeScript (AWS SDK v3) ---
import { S3Client, PutObjectCommand, GetObjectCommand } from "@aws-sdk/client-s3";
 
const s3 = new S3Client({});
await s3.send(new PutObjectCommand({ Bucket: "my-bucket", Key: "hello.txt", Body: "hi" }));
const out = await s3.send(new GetObjectCommand({ Bucket: "my-bucket", Key: "hello.txt" }));
console.log(await out.Body.transformToString());

S3 stores objects (files) inside buckets keyed by a string path.
In boto3 the body is a stream you .read(); in SDK v3 use transformToString().
Use an existing bucket name here; creating one is covered on the S3 page.
Objects are private by default - nothing is public unless you make it so.

Related: Amazon S3 via SDK: Buckets & Objects.

3. Read and Write a Record (DynamoDB)

Put an item and get it back using the document-style interface.

# --- Python (boto3) ---
import boto3
 
table = boto3.resource("dynamodb").Table("Visits")
table.put_item(Item={"id": "user-7", "count": 1})
item = table.get_item(Key={"id": "user-7"})["Item"]
print(item)

// --- TypeScript (AWS SDK v3) ---
import { DynamoDBClient } from "@aws-sdk/client-dynamodb";
import { DynamoDBDocumentClient, PutCommand, GetCommand } from "@aws-sdk/lib-dynamodb";
 
const doc = DynamoDBDocumentClient.from(new DynamoDBClient({}));
await doc.send(new PutCommand({ TableName: "Visits", Item: { id: "user-7", count: 1 } }));
const { Item } = await doc.send(new GetCommand({ TableName: "Visits", Key: { id: "user-7" } }));
console.log(Item);

The document interface lets you use plain Python dicts and JS objects instead of typed attribute values.
id here is the table's partition key; every item needs it.
boto3 uses boto3.resource("dynamodb").Table(...); SDK v3 uses lib-dynamodb.
Reads are eventually consistent unless you request a strongly consistent read.

Related: Amazon DynamoDB via SDK: Your First Table.

4. List Your Databases (RDS/Aurora)

Describe the relational databases in your account.

# --- Python (boto3) ---
import boto3
 
rds = boto3.client("rds")
for db in rds.describe_db_instances()["DBInstances"]:
    print(db["DBInstanceIdentifier"], db["DBInstanceStatus"])

// --- TypeScript (AWS SDK v3) ---
import { RDSClient, DescribeDBInstancesCommand } from "@aws-sdk/client-rds";
 
const rds = new RDSClient({});
const out = await rds.send(new DescribeDBInstancesCommand({}));
for (const db of out.DBInstances ?? []) console.log(db.DBInstanceIdentifier, db.DBInstanceStatus);

DescribeDBInstances is read-only, so it is a safe way to see what exists.
RDS manages relational engines like PostgreSQL and MySQL; Aurora is AWS's engine in the same API.
You connect to the database itself with a normal SQL driver, not the SDK.
An empty list simply means no instances yet.

Related: Amazon RDS & Aurora via SDK: Your First Database.

5. Move a Message Through a Queue (SQS)

Send a message and receive it back.

# --- Python (boto3) ---
import boto3
 
sqs = boto3.client("sqs")
url = sqs.get_queue_url(QueueName="jobs")["QueueUrl"]
sqs.send_message(QueueUrl=url, MessageBody="job-1")
msgs = sqs.receive_message(QueueUrl=url, WaitTimeSeconds=5).get("Messages", [])
print([m["Body"] for m in msgs])

// --- TypeScript (AWS SDK v3) ---
import { SQSClient, GetQueueUrlCommand, SendMessageCommand, ReceiveMessageCommand } from "@aws-sdk/client-sqs";
 
const sqs = new SQSClient({});
const { QueueUrl } = await sqs.send(new GetQueueUrlCommand({ QueueName: "jobs" }));
await sqs.send(new SendMessageCommand({ QueueUrl, MessageBody: "job-1" }));
const { Messages } = await sqs.send(new ReceiveMessageCommand({ QueueUrl, WaitTimeSeconds: 5 }));
console.log((Messages ?? []).map((m) => m.Body));

A queue buffers messages so a producer and a worker do not have to run at the same time.
WaitTimeSeconds enables long polling, which cuts empty responses and cost.
Receiving does not delete; you delete after processing (see the SQS page).
Use an existing queue named jobs, or create one on the SQS page.

Related: Amazon SQS via SDK: Your First Queue.

6. Publish an Event (SNS)

Publish a message to a topic that fans out to subscribers.

# --- Python (boto3) ---
import boto3
 
sns = boto3.client("sns")
arn = sns.create_topic(Name="alerts")["TopicArn"]
sns.publish(TopicArn=arn, Subject="Hello", Message="first event")
print("published to", arn)

// --- TypeScript (AWS SDK v3) ---
import { SNSClient, CreateTopicCommand, PublishCommand } from "@aws-sdk/client-sns";
 
const sns = new SNSClient({});
const { TopicArn } = await sns.send(new CreateTopicCommand({ Name: "alerts" }));
await sns.send(new PublishCommand({ TopicArn, Subject: "Hello", Message: "first event" }));
console.log("published to", TopicArn);

CreateTopic is idempotent - calling it for an existing name just returns the ARN.
A publish with no subscribers succeeds but reaches no one; add subscriptions on the SNS page.
SNS pushes; each subscriber (email, SQS, Lambda) receives its own copy.
The topic ARN is how every other call references this topic.

Related: Amazon SNS via SDK: Your First Topic.

7. Invoke a Function (Lambda)

Call an existing Lambda function and read its response.

# --- Python (boto3) ---
import boto3, json
 
lam = boto3.client("lambda")
resp = lam.invoke(FunctionName="hello", Payload=json.dumps({"name": "Ada"}))
print(resp["Payload"].read().decode())

// --- TypeScript (AWS SDK v3) ---
import { LambdaClient, InvokeCommand } from "@aws-sdk/client-lambda";
 
const lam = new LambdaClient({});
const resp = await lam.send(new InvokeCommand({
  FunctionName: "hello",
  Payload: JSON.stringify({ name: "Ada" }),
}));
console.log(new TextDecoder().decode(resp.Payload));

Invoke runs the function synchronously by default and returns its output payload.
The payload is bytes in both SDKs, so decode it to read the JSON string.
Use an existing function named hello, or create one on the Lambda page.
A non-empty FunctionError field signals the function threw.

Related: AWS Lambda via SDK: Your First Function.

Intermediate Examples

8. Inspect Compute and Network (EC2 + VPC)

List your instances and your VPCs - both live in the EC2 API surface.

# --- Python (boto3) ---
import boto3
 
ec2 = boto3.client("ec2")
for r in ec2.describe_instances()["Reservations"]:
    for i in r["Instances"]:
        print(i["InstanceId"], i["State"]["Name"])
for v in ec2.describe_vpcs()["Vpcs"]:
    print(v["VpcId"], v["CidrBlock"])

// --- TypeScript (AWS SDK v3) ---
import { EC2Client, DescribeInstancesCommand, DescribeVpcsCommand } from "@aws-sdk/client-ec2";
 
const ec2 = new EC2Client({});
const inst = await ec2.send(new DescribeInstancesCommand({}));
for (const r of inst.Reservations ?? [])
  for (const i of r.Instances ?? []) console.log(i.InstanceId, i.State?.Name);
const vpcs = await ec2.send(new DescribeVpcsCommand({}));
for (const v of vpcs.Vpcs ?? []) console.log(v.VpcId, v.CidrBlock);

EC2 and VPC share the same ec2 client - networking lives under the EC2 API.
DescribeInstances nests instances inside reservations, an easy shape to trip over.
Both calls are read-only, safe to run repeatedly.
Every account has a default VPC unless it was removed.

Related: Amazon EC2 via SDK: Your First Instance and Amazon VPC via SDK: Your First Network.

9. Publish a Custom Metric (CloudWatch)

Emit a metric so you can graph and alarm on your own numbers.

# --- Python (boto3) ---
import boto3
 
cw = boto3.client("cloudwatch")
cw.put_metric_data(
    Namespace="MyApp",
    MetricData=[{"MetricName": "SignUps", "Value": 1, "Unit": "Count"}],
)
print("metric sent")

// --- TypeScript (AWS SDK v3) ---
import { CloudWatchClient, PutMetricDataCommand } from "@aws-sdk/client-cloudwatch";
 
const cw = new CloudWatchClient({});
await cw.send(new PutMetricDataCommand({
  Namespace: "MyApp",
  MetricData: [{ MetricName: "SignUps", Value: 1, Unit: "Count" }],
}));
console.log("metric sent");

A custom metric is just a namespace, a name, and a numeric value.
CloudWatch creates the metric on first publish - no setup call needed.
Once data flows, you can build dashboards and alarms on it.
Keep the namespace stable so related metrics group together.

Related: Amazon CloudWatch via SDK: Metrics & Alarms.

Stack versions: This page was written for boto3 1.43.x (Python 3.10+) and the AWS SDK for JavaScript v3 (Node.js 18+).

Each example is deliberately small: confirm who you are, then touch identity, storage, databases, messaging, compute, networking, and observability in turn.

The point is not depth - each service has its own "your first X" page for that. The point is to feel how the same SDK shape repeats everywhere.

Prerequisites

Python: pip install boto3 (boto3 1.43.x, Python 3.10+). TypeScript: install the per-service clients you use, for example npm install @aws-sdk/client-s3 @aws-sdk/client-sqs (Node.js 18+).
Credentials configured once via aws configure, environment variables, or an IAM role. The SDK finds them automatically.
A default region set with AWS_REGION, or passed per client. These examples assume us-east-1.

Basic Examples

1. Confirm Your Identity (IAM + STS)

Before anything else, check which identity your SDK calls run as.

# --- Python (boto3) ---
import boto3
 
sts = boto3.client("sts")
me = sts.get_caller_identity()
print(me["Account"], me["Arn"])

// --- TypeScript (AWS SDK v3) ---
import { STSClient, GetCallerIdentityCommand } from "@aws-sdk/client-sts";
 
const sts = new STSClient({});
const me = await sts.send(new GetCallerIdentityCommand({}));
console.log(me.Account, me.Arn);

GetCallerIdentity needs no permissions, so it always works and is the fastest sanity check.
The returned Arn tells you whether you are a user, an assumed role, or a federated session.
This is the identity IAM will evaluate for every other call on this page.
If this fails, fix credentials before debugging any service.

Related: AWS IAM via SDK: Users, Roles & Policies - the identity layer in depth.

2. Store a File (S3)

Put an object into a bucket, then read it back.

# --- Python (boto3) ---
import boto3
 
s3 = boto3.client("s3")
s3.put_object(Bucket="my-bucket", Key="hello.txt", Body=b"hi")
body = s3.get_object(Bucket="my-bucket", Key="hello.txt")["Body"].read()
print(body.decode())

// --- TypeScript (AWS SDK v3) ---
import { S3Client, PutObjectCommand, GetObjectCommand } from "@aws-sdk/client-s3";
 
const s3 = new S3Client({});
await s3.send(new PutObjectCommand({ Bucket: "my-bucket", Key: "hello.txt", Body: "hi" }));
const out = await s3.send(new GetObjectCommand({ Bucket: "my-bucket", Key: "hello.txt" }));
console.log(await out.Body.transformToString());

S3 stores objects (files) inside buckets keyed by a string path.
In boto3 the body is a stream you .read(); in SDK v3 use transformToString().
Use an existing bucket name here; creating one is covered on the S3 page.
Objects are private by default - nothing is public unless you make it so.

Related: Amazon S3 via SDK: Buckets & Objects.

3. Read and Write a Record (DynamoDB)

Put an item and get it back using the document-style interface.

# --- Python (boto3) ---
import boto3
 
table = boto3.resource("dynamodb").Table("Visits")
table.put_item(Item={"id": "user-7", "count": 1})
item = table.get_item(Key={"id": "user-7"})["Item"]
print(item)

// --- TypeScript (AWS SDK v3) ---
import { DynamoDBClient } from "@aws-sdk/client-dynamodb";
import { DynamoDBDocumentClient, PutCommand, GetCommand } from "@aws-sdk/lib-dynamodb";
 
const doc = DynamoDBDocumentClient.from(new DynamoDBClient({}));
await doc.send(new PutCommand({ TableName: "Visits", Item: { id: "user-7", count: 1 } }));
const { Item } = await doc.send(new GetCommand({ TableName: "Visits", Key: { id: "user-7" } }));
console.log(Item);

The document interface lets you use plain Python dicts and JS objects instead of typed attribute values.
id here is the table's partition key; every item needs it.
boto3 uses boto3.resource("dynamodb").Table(...); SDK v3 uses lib-dynamodb.
Reads are eventually consistent unless you request a strongly consistent read.

Related: Amazon DynamoDB via SDK: Your First Table.

4. List Your Databases (RDS/Aurora)

Describe the relational databases in your account.

# --- Python (boto3) ---
import boto3
 
rds = boto3.client("rds")
for db in rds.describe_db_instances()["DBInstances"]:
    print(db["DBInstanceIdentifier"], db["DBInstanceStatus"])

// --- TypeScript (AWS SDK v3) ---
import { RDSClient, DescribeDBInstancesCommand } from "@aws-sdk/client-rds";
 
const rds = new RDSClient({});
const out = await rds.send(new DescribeDBInstancesCommand({}));
for (const db of out.DBInstances ?? []) console.log(db.DBInstanceIdentifier, db.DBInstanceStatus);

DescribeDBInstances is read-only, so it is a safe way to see what exists.
RDS manages relational engines like PostgreSQL and MySQL; Aurora is AWS's engine in the same API.
You connect to the database itself with a normal SQL driver, not the SDK.
An empty list simply means no instances yet.

Related: Amazon RDS & Aurora via SDK: Your First Database.

5. Move a Message Through a Queue (SQS)

Send a message and receive it back.

# --- Python (boto3) ---
import boto3
 
sqs = boto3.client("sqs")
url = sqs.get_queue_url(QueueName="jobs")["QueueUrl"]
sqs.send_message(QueueUrl=url, MessageBody="job-1")
msgs = sqs.receive_message(QueueUrl=url, WaitTimeSeconds=5).get("Messages", [])
print([m["Body"] for m in msgs])

// --- TypeScript (AWS SDK v3) ---
import { SQSClient, GetQueueUrlCommand, SendMessageCommand, ReceiveMessageCommand } from "@aws-sdk/client-sqs";
 
const sqs = new SQSClient({});
const { QueueUrl } = await sqs.send(new GetQueueUrlCommand({ QueueName: "jobs" }));
await sqs.send(new SendMessageCommand({ QueueUrl, MessageBody: "job-1" }));
const { Messages } = await sqs.send(new ReceiveMessageCommand({ QueueUrl, WaitTimeSeconds: 5 }));
console.log((Messages ?? []).map((m) => m.Body));

A queue buffers messages so a producer and a worker do not have to run at the same time.
WaitTimeSeconds enables long polling, which cuts empty responses and cost.
Receiving does not delete; you delete after processing (see the SQS page).
Use an existing queue named jobs, or create one on the SQS page.

Related: Amazon SQS via SDK: Your First Queue.

6. Publish an Event (SNS)

Publish a message to a topic that fans out to subscribers.

# --- Python (boto3) ---
import boto3
 
sns = boto3.client("sns")
arn = sns.create_topic(Name="alerts")["TopicArn"]
sns.publish(TopicArn=arn, Subject="Hello", Message="first event")
print("published to", arn)

// --- TypeScript (AWS SDK v3) ---
import { SNSClient, CreateTopicCommand, PublishCommand } from "@aws-sdk/client-sns";
 
const sns = new SNSClient({});
const { TopicArn } = await sns.send(new CreateTopicCommand({ Name: "alerts" }));
await sns.send(new PublishCommand({ TopicArn, Subject: "Hello", Message: "first event" }));
console.log("published to", TopicArn);

CreateTopic is idempotent - calling it for an existing name just returns the ARN.
A publish with no subscribers succeeds but reaches no one; add subscriptions on the SNS page.
SNS pushes; each subscriber (email, SQS, Lambda) receives its own copy.
The topic ARN is how every other call references this topic.

Related: Amazon SNS via SDK: Your First Topic.

7. Invoke a Function (Lambda)

Call an existing Lambda function and read its response.

# --- Python (boto3) ---
import boto3, json
 
lam = boto3.client("lambda")
resp = lam.invoke(FunctionName="hello", Payload=json.dumps({"name": "Ada"}))
print(resp["Payload"].read().decode())

// --- TypeScript (AWS SDK v3) ---
import { LambdaClient, InvokeCommand } from "@aws-sdk/client-lambda";
 
const lam = new LambdaClient({});
const resp = await lam.send(new InvokeCommand({
  FunctionName: "hello",
  Payload: JSON.stringify({ name: "Ada" }),
}));
console.log(new TextDecoder().decode(resp.Payload));

Invoke runs the function synchronously by default and returns its output payload.
The payload is bytes in both SDKs, so decode it to read the JSON string.
Use an existing function named hello, or create one on the Lambda page.
A non-empty FunctionError field signals the function threw.

Related: AWS Lambda via SDK: Your First Function.

Intermediate Examples

8. Inspect Compute and Network (EC2 + VPC)

List your instances and your VPCs - both live in the EC2 API surface.

# --- Python (boto3) ---
import boto3
 
ec2 = boto3.client("ec2")
for r in ec2.describe_instances()["Reservations"]:
    for i in r["Instances"]:
        print(i["InstanceId"], i["State"]["Name"])
for v in ec2.describe_vpcs()["Vpcs"]:
    print(v["VpcId"], v["CidrBlock"])

// --- TypeScript (AWS SDK v3) ---
import { EC2Client, DescribeInstancesCommand, DescribeVpcsCommand } from "@aws-sdk/client-ec2";
 
const ec2 = new EC2Client({});
const inst = await ec2.send(new DescribeInstancesCommand({}));
for (const r of inst.Reservations ?? [])
  for (const i of r.Instances ?? []) console.log(i.InstanceId, i.State?.Name);
const vpcs = await ec2.send(new DescribeVpcsCommand({}));
for (const v of vpcs.Vpcs ?? []) console.log(v.VpcId, v.CidrBlock);

EC2 and VPC share the same ec2 client - networking lives under the EC2 API.
DescribeInstances nests instances inside reservations, an easy shape to trip over.
Both calls are read-only, safe to run repeatedly.
Every account has a default VPC unless it was removed.

Related: Amazon EC2 via SDK: Your First Instance and Amazon VPC via SDK: Your First Network.

9. Publish a Custom Metric (CloudWatch)

Emit a metric so you can graph and alarm on your own numbers.

# --- Python (boto3) ---
import boto3
 
cw = boto3.client("cloudwatch")
cw.put_metric_data(
    Namespace="MyApp",
    MetricData=[{"MetricName": "SignUps", "Value": 1, "Unit": "Count"}],
)
print("metric sent")

// --- TypeScript (AWS SDK v3) ---
import { CloudWatchClient, PutMetricDataCommand } from "@aws-sdk/client-cloudwatch";
 
const cw = new CloudWatchClient({});
await cw.send(new PutMetricDataCommand({
  Namespace: "MyApp",
  MetricData: [{ MetricName: "SignUps", Value: 1, Unit: "Count" }],
}));
console.log("metric sent");

A custom metric is just a namespace, a name, and a numeric value.
CloudWatch creates the metric on first publish - no setup call needed.
Once data flows, you can build dashboards and alarms on it.
Keep the namespace stable so related metrics group together.

Related: Amazon CloudWatch via SDK: Metrics & Alarms.

Stack versions: This page was written for boto3 1.43.x (Python 3.10+) and the AWS SDK for JavaScript v3 (Node.js 18+).

Core Services Basics

Buscar en la documentación

Core Services Basics