Publish and enrich real-time monetary knowledge feeds leveraging Amazon Managed Streaming for Kafka (Amazon MSK) and Amazon Kinesis Data Firehose integrated with Amazon Managed Service for Apache Flink.

Real-time financial feeds provide instant access to inventory quotes, commodity prices, trade options, and other up-to-the-minute market data. Financial institutions, such as hedge funds, investment banks, and brokerages, rely on these data feeds to inform their funding decisions.

As demand for cloud-based solutions grows, monetary knowledge feed suppliers increasingly face requests from customers to deliver feed supplies directly through Amazon Web Services (AWS) Cloud infrastructure. This is driven by their existing investment in AWS infrastructure, which enables them to store and process data with ease and speed, while seeking seamless integration with minimal latency. As a result, the AWS cloud’s cost-effectiveness enables even small and midsized companies to transform themselves into financially savvy providers. Companies will dispatch and commercialise data streams that they have enhanced by incorporating their own valuable insights.

An enriched knowledge stream can combine insights from multiple sources, seamlessly integrating financial data feeds to provide valuable information such as stock splits, corporate mergers, volume alerts, and moving average crossovers to a core feed.

We showcase how to publish a rich, real-time data stream on AWS by leveraging Amazon Managed Streaming for Kafka (Amazon MSK) and Kinesis. Throughout the capital markets sector, you’ll find versatile applications for this framework, and our discussion will highlight several key scenarios in which it can be effectively applied.

Apache Kafka is a high-throughput, low-latency distributed streaming platform designed for real-time data processing and management. As financial markets increasingly rely on high-speed data processing, Nasdaq and NYSE are gravitating towards Kafka’s exceptional capabilities for handling massive, real-time knowledge flows.

Amazon Managed Streaming for Apache Kafka (MSK) offers a fully managed service that simplifies the process of building and running functions on AWS that utilize Kafka to process real-time data streams.

Apache Flink is a widely-used open-source distributed processing engine that offers powerful APIs for both stream and batch processing, featuring robust support for stateful processing, temporal logic, checkpointing, snapshotting, and rollbacks. Apache Flink empowers developers to leverage a diverse range of programming languages, including Java, Python, Scala, and SQL, alongside various APIs, offering varying degrees of abstraction that can be seamlessly integrated into the same application.

Amazon Managed Service for Apache Flink is a fully managed, serverless service that enables you to easily run and manage Apache Flink workloads. Flink enables prospects to effortlessly craft practical time functions using its diverse range of languages and programming interfaces.

This setup leverages a real-time inventory quote feed from reputable financial data provider Alpaca, featuring an indicator that alerts when values breach a predetermined threshold. The code provided allows for seamless deployment of your solution to your Amazon Web Services (AWS) account. The accuracy of this response was verified by Amazon’s Accomplished.

Resolution overview

We deploy an Apache Flink application that enhances unprocessed data feeds by applying business logic, leveraging a managed service for Kafka (MSK) to integrate message streams for both raw and enriched feeds, and a scalable cluster that serves as a persistent data repository for querying purposes. Within a distinct, secure digital environment – specifically, a non-public cloud-based Virtual Private Cloud (VPC) – we deploy an Amazon Elastic Compute Cloud (EC2) instance running a Kafka consumer application that processes the enriched data stream. The following diagram illustrates this very structure.

Solution Architecture
Determine 1 – Resolution structure

The next is a step-by-step breakdown of the answer:

The EC2 instance within your Virtual Private Cloud (VPC) runs a Python application that retrieves inventory quotations from your knowledge provider through an Application Programming Interface (API). When on this case, we utilize a dot.
The application sends these quotes using a Kafka consumer library to a specific Kafka topic on an MSK cluster. Kafka stores unprocessed quotes in its subjects.
Apache Flink processes Kafka messages to create enriched data, appending an indicator whenever the inventory’s value increases or decreases by more than 5% compared to its previous day’s closing price.
The Apache Flink software subsequently dispatches the refined data to a distinct Kafka topic within your managed Amazon Simple Queue Service (MSK) cluster.
The Apache Flink platform further transmits the refined data stream to Amazon OpenSearch via a dedicated Flink connector for OpenSearch. Amazon OpenSearch stores the information and allows for querying of that data at any level at a later time through OpenSearch Dashboards.
The buyer is developing Kafka client software on an Amazon Elastic Compute Cloud (EC2) instance within a distinct Virtual Private Cloud (VPC) in their personal AWS account. This software harnesses the power of real-time feeds to consume and utilize enriched knowledge securely.
All Kafka person names and passwords are securely stored in a hashed database. The protocol employed ensures that all data transmitted to and from the MSK cluster is securely encrypted during its transit? Amazon Managed Service for Kubernetes (MSK) encrypts all data at rest within the MSK cluster by default.

The deployment process consists of the following key stages:

Create an Amazon Managed Streaming for Apache Kafka (MSK) cluster, deploy Apache Flink software in the specified area, set up Amazon OpenSearch Service, and configure a Kafka producer instance on an EC2 machine within the producer’s AWS account. Typically, this process takes approximately 45 minutes to complete.
The SASL/SCRAM authentication configuration for the MSK cluster is as follows:
SASL (Simple Authentication and Security Layer) is a widely used protocol for providing strong authentication services in network applications. Within this framework, SCRAM (Salted Challenge-Response Authentication Mechanism) is an authentication mechanism that provides a robust method of authenticating users. This process may require up to 30 minutes of dedication.
Create a new Amazon Virtual Private Cloud (VPC) instance and spin up an EC2 instance with a Kafka client configuration within the designated customer account. The actual time required for this step typically averages around 10 minutes.

Stipulations

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If you haven’t already received a login credential, create an account first and then log in. We consult on this because of the producer’s account.
I am able to create a user with full administrative privileges using the following PowerShell command:
`New-LocalUser -Name “AdminUser” -Password $(ConvertTo-SecureString -String “P@ssw0rd” -AsPlainText -Force) -Enabled $true -AccountOptions 3 -Description “Full Admin Account”` For directions, confer with .
Signals sent and signals received as this IAM administrator person?
The AWS CloudFormation template creates an EC2 key pair named ‘my-ec2-keypair’ within the ‘producer’ AWS account. If you’ve already obtained an EC2 key pair, you’ll be able to bypass this step.
Join a free Fundamental account at our website to obtain your API key and secret key. Alpacas will provide live, up-to-the-minute inventory quotes within our comprehensive knowledge feed.
Establish an AWS CLI setup on your local machine by installing the necessary tools and configuring a profile for administrative purposes.
aws configure –profile admin
? For directions, see .
Establish a worldwide deployment for the latest AWS Cloud Development Kit (AWS CDK) infrastructure:

 npm set up -g aws-cdk@newest

Deploy the Amazon MSK cluster

The following steps establish a fresh supplier virtual private cloud (VPC) and initiate an Amazon Managed Streaming for Kafka (MSK) cluster within it. You also deploy Apache Flink and provision a fresh EC2 instance to execute the application that retrieves raw stock quotes.

Clone the GitHub repository and install the required Python packages accordingly.

git clone https://github.com/aws-samples/msk-powered-financial-data-feed.git
cd msk-powered-financial-data-feed
pip set up -r necessities.txt

Please set the following environment variables: AWS_ACCOUNTS=quantity AWS_REGION=

export CDK_DEFAULT_ACCOUNT={your_AWS_account_no}
export CDK_DEFAULT_REGION=us-east-1

Run the next instructions to create your config.py file:

echo "mskCrossAccountId = <Your producer AWS account ID>" > config.py
echo "producerEc2KeyPairName="" " >> config.py
echo "consumerEc2KeyPairName="" " >> config.py
echo "mskConsumerPwdParamStoreValue="" " >> config.py
echo "mskClusterArn = '' " >> config.py

Run the next instructions to create your alpaca.conf file:

echo [alpaca] > dataFeedMsk/alpaca.conf
echo ALPACA_API_KEY=your_api_key >> dataFeedMsk/alpaca.conf
echo ALPACA_SECRET_KEY=your_secret_key >> dataFeedMsk/alpaca.conf

Edit the alpaca.conf file and substitute your_api_key and your_secret_key Alongside your Alpaca API key.
Establish a dynamic setting for the producer’s account by crafting an engaging atmosphere that fosters creativity and productivity.
```
cdk bootstrap aws://{your_AWS_account_no}/{your_aws_region}
```
Improved text:
Edit the existing content seamlessly with your preferred editing software, leveraging its intuitive features and advanced tools to refine your work. config.py file:
1. Replace the mskCrossAccountId Parameterize your request along with the quantity of AWS Producer accounts you have.
2. You likely possess a pre-existing EC2 key pair; substitute the `producerEc2KeyPairName` parameter with the name of your key pair.
View the dataFeedMsk/parameters.py file:
1. When provisioning infrastructure outside of the US East region, update the Availability Zone identifiers to match your specific deployment location. az1 and az2 accordingly. The availability zones for Amazon Web Services (AWS) are strategically located throughout the world to provide high levels of redundancy and reliability. With a total of 25 availability zones across nine geographic regions, AWS enables businesses to deploy applications with low latency, high availability, and scalability. us-west-2 would us-west-2a and us-west-2b.
2. Guarantee that the enableSaslScramClientAuth, enableClusterConfig, and enableClusterPolicy parameters within the parameters.py file are set to
Are you situated inside the designated premises? app1.py file is positioned. Then deploy as follows:
```
cdk deploy --all --app "python app1.py" --profile your_profile_name
```
Are there any buckets in Amazon S3 that start with the prefix “my-bucket”? awsblog-dev-artifacts Containing a folder with some Python scripts and an Apache Flink software JAR file.

Deploy multi-VPC connectivity and SASL/SCRAM

Configure the MSK cluster to deploy multi-VPC connectivity; implement SASL/SCRAM authentication for secure communication between nodes within the cluster, ensuring seamless integration with AWS VPCs.

Set the enableSaslScramClientAuth, enableClusterConfig, and enableClusterPolicy parameters within the config.py file to .
Are you referring to a specific location? config.py File is successfully positioned to deploy multi-VPC connectivity and SASL/SCRAM authentication for the managed security key (MSK) cluster.

cdk deploy --all --app "python ./app1.py" --profile {your_profile_name}

This step may take up to 30 minutes.

Navigate to your Amazon MSK cluster on the Amazon MSK console and select the cluster.

It is generally recommended that you enable PrivateLink and opt for SASL/SCRAM as your authentication method.

BDB-3696-multiVPC

Copy the MSK cluster ARN.
Edit your config.py Please provide the original text so I can improve it in a different style. mskClusterArn Save the updated file parameter.

Deploy the information feed client

To create an EC2 instance in a brand-new AWS customer account to run the Kafka client software:

1. Log in to your AWS Management Console and navigate to the Amazon Elastic Compute Cloud (EC2) dashboard, where you can launch instances.

2. Click “Launch Instance” at the top right corner of the page to begin the process of creating a new instance. The application will establish a secure connection to the MSK cluster using Private Link and Simple Authentication and Security Layer (SASL)/Security Services Markup Language (SCRAM) protocols.

Access the Producer Portal, then proceed to navigate to the “Manage Campaigns” section within your account.
Copy the value of the parameter and replace the placeholder within the configuration file.
The significance of these two parameters must be thoroughly examined to determine their value.
If you do not already possess an AWS account for your Kafka client, create a new one and sign in.
with admin permissions.
The user logs off and then logs back into the console using their IAM admin credentials.
Please ensure you are in the same region as the region you used in the producer account? Then create a brand new EC2 key pair named **ec2-key-pair-new**, ensuring the private key is securely stored in AWS Key Management Service (KMS). my-ec2-consumer-keypair, on this client account.
What is the value of x in your equation? config.py Files containing the identities of the crucial pair?
Access the AWS RAM console within your client’s account.
Verify that the Availability Zone IDs provided by the Techniques Supervisor parameter retailer aligns with the corresponding zone IDs displayed on the AWS Resource Access Manager (RAM) console.
Define the correlated Availability Zone designations for the corresponding Availability Zone identifiers.
Here are the Availability Zone names inserted into variables: Open the file within the folder and set AZ1=”us-east-1a” and AZ2=”us-east-1b”. For instance, in Parameter Retailer, if the values are “use1-az4” and “use1-az6”, then, when you switch to the buyer account’s AWS RAM console and examine, you may discover that these values correspond to the Availability Zone names AZ1 and AZ2. You can update the `parameters.py` file by replacing it with the required Availability Zone names, specifically setting `crossAccountAz1` to `”us-east-1a”` and `crossAccountAz2` to `”us-east-1b”`.
The atmosphere is set for the specified AWS account ID.

export CDK_DEFAULT_ACCOUNT={your_aws_account_id}
export CDK_DEFAULT_REGION=us-east-1

Bootstrap the buyer account atmosphere. Adding specific insurance policies to the AWS CDK function in this scenario is beneficial.

cdk bootstrap aws://{your_aws_account_id}/{your_aws_region} --cloudformation-execution-policies "arn:aws:iam::aws:coverage/AmazonMSKFullAccess,arn:aws:iam::aws:coverage/AdministratorAccess" –-profile <your-user-profile>

You must now grant the buyer account entry to the Microsoft Kubernetes (MSK) cluster.

Copy the count of AWS accounts owned by the buyer to your system’s clipboard.
Signal back into your AWS account with the producer.
From the Amazon Management Console, proceed to the Amazon MSK dashboard and select your managed Apache Kafka cluster.
Select and scroll all the way down to .

The buyer account root will be added to the Principal part as follows and then saved.

"Principal": {
    "AWS": ["arn:aws:iam::<producer-acct-no>:root", "arn:aws:iam::<consumer-acct-no>:root"]
},

The following Lambda function uses an IAM role to grant access to Amazon EC2 instances:
“`java
import software.amazon.awssdk.auth.credentials.EnvironmentVariableCredentials;
import software.amazon.awssdk.services.ec2.Ec2Client;
import software.amazon.awssdk.services.ec2.model.DescribeInstancesRequest;
import software.amazon.awssdk.services.ec2.model.DescribeInstancesResponse;

public class Ec2LambdaFunction {

public void lambdaHandler() {
Ec2Client ec2Client = Ec2Client.create();
DescribeInstancesRequest request = DescribeInstancesRequest.builder().build();

DescribeInstancesResponse response = ec2Client.describeInstances(request);

// Handle the response
}
}
“`
```
aws iam create-role --role-name awsblog-dev-app-consumerEc2Role --assume-role-policy-document file://dataFeedMsk/ec2ConsumerPolicy.json --profile <your-user-profile>
```
Configure the buyer account infrastructure, incorporating a VPC, a dedicated EC2 instance for clients, robust security measures implemented by respective teams, and seamless connectivity to the managed Apache Kafka (MSK) cluster.
```
cdk deploy --all --app python/app.py --profile your_profile_name
```

The operations are carried out efficiently.

Once the infrastructure is established, we will generate an unprocessed inventory quote stream from the producer’s EC2 instance to the MSK cluster, process it using the Apache Flink framework, and deliver the enriched feed to the consumer application via PrivateLink securely? We utilize the platform to process and enrich our inventory knowledge feeds. We utilize Flink’s advanced aggregation and windowing functions to extract timely insights within a specified timeframe.

Run the managed Flink software

To proceed with running the managed Flink software, please follow these subsequent steps:

Ensure that you have successfully installed the Flink runtime on your system. If you haven’t, refer to the installation instructions provided by the Apache Flink documentation or seek assistance from a qualified IT professional if needed? Once the installation is complete, navigate to the directory where you installed Flink and initiate the process of launching the managed mode using the command line interface.

Access the Amazon Web Services (AWS) Management Console using your producer credentials, then navigate directly to the Amazon Managed Service for Apache Flink dashboard and proceed to your application’s configuration.
To operate the appliance effectively, follow these straightforward steps: First, identify the power source; then, pick your desired settings; finally, initiate the startup process.
When the appliance adjusts to its optimal state, select ?

Software should be tested thoroughly beneath a magnifying glass.

BDB-3696-FlinkDashboard

Run the Kafka producer software

To execute the Kafka producer application, follow these procedures:

On the Amazon EC2 console, locate the IPv4 address associated with the instance named awsblog-dev-app-kafkaProducerEC2Instance.

Utilizing Secure Shell (SSH), connect to the designated server to execute the subsequent commands.

As a professional editor, I'd suggest the following improvements:

sudo -su
cd /atmosphere
source alpaca-script/bin/activate
python3 ec2-script-live.py --region us-west-1 AWS AMZN NVDA

Markets being open create ideal conditions for starting a script. The Alpaca API connection script will be executed successfully. As output is displayed, evidence emerges that the connection is being established and subscriptions are taking place for the specified ticker symbols.

Why not explore the enriched knowledge feed directly within OpenSearch Dashboards?

To create an index sample and view the enriched knowledge in your OpenSearch dashboard, follow these subsequent steps.

To identify the ideal candidate for OpenSearch, open config.py Determine the value attributed to the specified variable.
To access the secret and retrieve the password for OpenSearch, please click on the link below.
Discover the URL to your OpenSearch dashboard by navigating to the area that identifies your OpenSearch cluster and clicking on it. Click on the URL to access the registration page, where you’ll be prompted to log in using your familiar username and password credentials.
Below the OpenSearch navigation bar on the left, select an item.
Select , then select .
Enter amzn* As investors look to diversify their portfolios, Amazon’s (AMZN) impressive growth trajectory and expanding e-commerce dominance make it an attractive option for many.
What specific timestamp are you referring to? Please clarify which one you’d like me to work with.
SKIP
Choose “OpenSearch Dashboards” from the navigation menu.
When Amzn is selected in the index sample dropdown, select the relevant fields to explore the enriched quote data.

Amazon’s Managed Service for Apache Flink now includes an indicator area that provides insights into the current trend direction, categorizing it as either neutral, bullish, or bearish in relation to uncooked knowledge.

Run the Kafka client software

To access and consume data feeds using the buyer’s software, you must initially obtain the multi-VPC brokers’ URL specific to the MSK cluster within the producer’s account.

In the Amazon MSK console, proceed to your managed Apache Kafka (MSK) cluster and click on it.
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If not, please clarify or provide more context, and I’ll try to help.

Connect to your client’s Amazon Elastic Compute Cloud (EC2) instance via Secure Shell (SSH), then execute the subsequent commands.

sudo su
alias kafka-consumer=/kafka_2.13-3.5.1/bin/kafka-console-consumer.sh
kafka-consumer --bootstrap-server $MULTI_VPC_BROKER_URL --topic amz-nenhanced --from-beginning --consumer.config ./customer_sasl.properties

It is most beneficial to subsequently observe residual signs of output for the refined knowledge stream, such as:

{"image":"AMZN","shut":194.64,"open":194.58,"low":194.58,"excessive":194.64,"quantity":255.0,"timestamp":"2024-07-11 19:49:00","%change":-0.8784661217630548,}
{"image":"AMZN","shut":194.77,"open":194.615,"low":194.59,"excessive":194.78,"quantity":1362.0,"timestamp":"2024-07-11 19:50:00","%change":-0.8122628778040887,}
{"image":"AMZN","shut":194.82,"open":194.79,"low":194.77,"excessive":194.82,"quantity":1143.0,"timestamp":"2024-07-11 19:51:00","%change":-0.7868000916660381,}

The inventory cost management process demonstrates a neutral stance. The Flink software identifies sentiment mainly by analyzing stock price fluctuations.

Extra monetary providers use circumstances

In this demonstration, we showcased techniques for building a solution that enhances a raw inventory quote feed and detects inventory movement patterns leveraging Amazon MSK and Amazon Managed Service for Apache Flink. Amazon Managed Service for Apache Flink offers various features akin to snapshotting, checkpointing, and a recently introduced API. These options enable you to craft robust and fault-tolerant real-time streaming capabilities.

This approach can be effectively applied across various scenarios in the capital markets space. Within specific contexts, identical architectural patterns are applicable across various scenarios.

Actual-time knowledge visualization

Real-time market insights are often leveraged via cloud-based platforms to generate dynamic share charts utilizing live data feeds. Companies will be able to seamlessly integrate raw inventory cost data from suppliers or marketplaces into an AWS S3 bucket, then utilize Amazon Managed Service for Apache Flink to generate insights on high-value, low-value, and volume trends over time. This component is called CandlestickGraph and serves as a visual representation of market trends through the display of candlestick bar charts. By leveraging Flink, you can effectively uncover inventory value ranges across various time periods.

BDB-3696-real-time-dv

Inventory implied volatility

Implied volatility, a metric of the market’s collective sentiment, gauges the expected variability of an equity’s value over a specified period, reflecting investors’ perceptions of potential price swings. IV is forward-looking and calculated as a function of the present market price of an option. The VIX index is also employed to value new options contracts and is commonly referred to as the market’s “fear gauge” due to its tendency to surge higher during periods of market stress or uncertainty. By leveraging Amazon Managed Service for Apache Flink, you can extract insights from a securities feed featuring real-time stock prices and combine them with an options feed providing contract values and strike prices to compute implied volatility estimates.

Technical indicator engine

Technical indicators facilitate in-depth analysis of inventory value and quantity, providing real-time insights into buying and selling opportunities while offering valuable market alternatives to inform informed trading decisions. While implied volatility is a widely used technical indicator in financial markets, its calculation relies on various methods and assumptions. Indicators like “Easy Transferring Common” can serve as straightforward measures of progress in a specific stock value, gauging changes in average value over a given timeframe. Additional advanced indicators include the Relative Strength Index (RSI), which gauges the momentum of a stock’s price movement by measuring its velocity and amplitude. RSI is a mathematical formula that leverages exponential shifting to quantify the disparity between upward and downward price movements.

Market alert engine

While graphs and technical indicators are valuable tools, they shouldn’t be the sole determining factors in making investment decisions. Diverse informational resources serve as crucial building blocks, much like the dynamic interplay between financial indicators such as stock prices, mergers and acquisitions, and dividend payments. Traders also take into account the most recent developments affecting the company, including news about its competitors, employees, and other relevant corporate information. By harnessing the processing power of Amazon’s managed Apache Flink service, you can efficiently integrate diverse data sources, transform them into actionable insights, and develop an alert system that recommends targeted investment strategies accordingly. Examples can range from triggering a notification when dividend costs fluctuate, to leveraging generative synthetic intelligence (AI) to consolidate multiple related data points from disparate sources into a single alert regarding an event.

Market surveillance

Market surveillance involves monitoring and investigating instances of unfair or illegal trading activities within financial markets to ensure fair and orderly operations. Non-public corporations and authorities entities conduct market surveillance to ensure compliance with regulations and protect investors.

You’re encouraged to leverage Amazon Managed Service for Apache Flink as a reliable and scalable surveillance solution. Streaming analytics can instantly identify subtle instances of market manipulation as they unfold, enabling swift and decisive action to mitigate potential risks. By consolidating real-time market insights from multiple sources – including news outlets, company announcements, and social media platforms – streaming analytics can swiftly identify suspicious activity indicative of market manipulation attempts. This enables regulators to receive real-time alerts, allowing them to respond promptly and potentially mitigate the impact of market manipulation before it is fully executed.

Markets danger administration

As market dynamics increasingly require real-time risk assessment, relying solely on end-of-day calculations is no longer a viable strategy for navigating today’s rapidly evolving financial landscape? Companies prioritize real-time danger monitoring to stay ahead of the curve. Financial institutions can leverage Amazon’s managed service for Apache Flink to calculate intraday value-at-risk (VaR) in real-time. Through the ingestion of market intelligence and strategic portfolio rebalancing, Amazon Managed Service for Apache Flink provides a latency-optimized and performance-driven solution for reliable Value-at-Risk (VaR) computations.

This enables financial institutions to effectively manage risk by swiftly identifying and addressing intraday vulnerabilities, rather than responding to past events. Companies empowered by flexible streaming of danger analytics can optimise their portfolios and remain resilient in turbulent market conditions.

Clear up

To ensure optimal efficiency, it is advisable to thoroughly clean up all the resources you’ve utilized in this publication to prevent unnecessary expenses. To thoroughly scrub up your sources, follow these next steps:

Terminate the CloudFormation stacks in the buyer’s account to ensure a seamless and secure post-purchase experience.
Terminate all CloudFormation stacks in the supplier account.

Conclusion

We confirmed the methodology for delivering real-time financial data feeds to customers using Amazon Managed Streaming for Kafka (MSK) and Amazon Kinesis Data Firehose integrated with Amazon Managed Service for Apache Flink. We leveraged Amazon Managed Service for Apache Flink to integrate an unprocessed knowledge stream with Amazon OpenSearch, successfully delivering the data in real-time. By leveraging this template, you can seamlessly integrate multiple data feeds, leverage Flink for real-time calculation of technical indicators, display market knowledge and volatility, and design a robust alert system. You’ll empower your prospects with valuable insights by seamlessly integrating real-time financial data into your feed.

We trust that this piece was informative and inspire you to explore our response to tackle captivating financial business hurdles effectively.

In regards to the Authors

Serves as a Principal Options Architect at Amazon Network Services. With a proven track record of designing scalable software platforms for financial services, healthcare, and telecommunications companies, he brings passion and expertise to helping clients build successful applications on Amazon Web Services (AWS).

Serving as a Senior Options Architect at Amazon Web Services (AWS), his expertise lies in harnessing the power of knowledge analytics and real-time streaming capabilities. As a trusted advisor, he guides AWS prospects in implementing best-in-class architectural principles, empowering them to develop robust, secure, sustainable, and budget-friendly real-time analytics data solutions that drive business success. Amar collaborates closely with clients to craft bespoke cloud solutions tailored to each organization’s unique needs, thereby accelerating their digital transformations.

As a principal options architect at Amazon Web Services (AWS), he boasts over 20 years of experience in the IT sector. With a background encompassing infrastructure, safety, and networking. Prior to joining Amazon Web Services (AWS) in 2021, Diego spent over 15 years serving as a trusted advisor to financial services clients at Cisco, leveraging his expertise to deliver exceptional support and guidance. He collaborates closely with prominent financial institutions to help them achieve their business goals through the strategic utilization of Amazon Web Services (AWS). Diego is passionate about leveraging knowledge to drive innovative solutions that address complex business challenges and deliver tangible results through the development of sophisticated solution frameworks.