Transforming Big Data into Insights with AWS CDK / AWS Step Functions and more

In our company, we embarked on an exciting challenge that pushed us to surpass our limits and showcase our commitment to excellence. We want to share how we tackled big data management and successfully transformed a sea of raw information into valuable insights to meet our client's expectations.

It all started with the need to acquire raw data from its sources. Conquering this stage was a real challenge, as we encountered a variety of data formats, structures, and locations that required a solid strategy. However, we knew we were on the right path to delivering exceptional results for our client.

We assembled a dedicated team of data management experts and began crafting a robust strategy. Collaboration and creativity became our primary tools. As we progressed, we identified and overcame hurdles, from data normalization to optimizing data acquisition efficiency.

The journey was long and challenging, but each obstacle we overcame brought us closer to the goal. Technology and innovation played a pivotal role in our ability to process and analyze large volumes of data effectively.

Finally, after months of hard work and dedication, we transformed this raw data into actionable information. The result was a solid and efficient project that empowered our client to make informed, strategic decisions.

This achievement is not only a testament to our ability to tackle challenges but also a reminder of the importance of innovation and commitment to customer satisfaction. We are proud to have overcome this hurdle and look forward to continuing to drive excellence in future projects.

Step 1: Extracting Data from BigQuery to AWS S3

First, our process needed to retrieve data from Google BigQuery using Python and store it in AWS S3 in compressed CSV files. We achieved this using the Google Cloud Python client library and the AWS SDK for Python (Boto3). Here's a Python code snippet to demonstrate the process:

Step 2: Inserting Data into AWS RDS

Once the data was safely stored in AWS S3, the next step was to load it into an AWS RDS database using the LOAD command with a manifest file. Here's a Python example demonstrating this process:

Step 3: Generating Parquet Files from AWS RDS

Finally, we needed to transform the data stored in AWS RDS into Parquet files. We did this using SQL UNLOAD with the following Python example:

By completing these three main steps, our client successfully transformed their data from Google BigQuery into efficient, compressed, and query-friendly Parquet files, facilitating faster and more cost-effective analytics. This case is a testament to the power of cloud services, Python, and a well-designed data pipeline.

AWS CDK and AWS Step Functions to build a robust and scalable pipeline using multiple AWS resources

Unlocking the true potential of AWS Step Functions has never been easier! With AWS CDK, you can effortlessly harness Step Functions's power by leveraging many pre-built classes.

Here are some of the most useful classes that we use in our pipeline to make your life easier:

Choice: We customize execution paths based on specific conditions. Helpful in making decisions based on previous outcomes.

In this example, we create an AWS CDK Stack with two Lambda functions (your_first_lambda_function and your_second_lambda_function). We define a Choice state called "Is First Lambda Successful?" with two branches based on conditions. If the condition $.status is True, it will execute the first Lambda function; otherwise, it will execute the second Lambda function.

Finally, we create a State Machine with the defined choice state and add it to the AWS CDK app.

Condition: We configure conditions to guide the flow of the process. Useful for evaluating and making decisions based on data.

In this example, a choice state is created using sfn.Choice with two possible outcomes based on the input value. The sfn.Condition.number_greater_than and sfn.Condition.number_less_than_equals conditions are used to define the conditions for transitioning to the next states. Depending on the input value provided when executing the state machine, it will follow the appropriate path in the choice state.

Fail: We handle failures and exceptions effectively. Useful for managing errors and unexpected situations.

In this example, we create an AWS CDK stack that includes an AWS Lambda function (lambda_function) and an AWS Step Functions state machine (state_machine). The state machine starts with the Lambda function and immediately transitions to a Fail state (fail_state) with a custom error message "MyCustomError". This demonstrates how to use the aws_cdk.aws_stepfunctions.Fail construct in an AWS CDK application to handle errors in a Step Functions state machine.

Parallel: We execute tasks in parallel for greater efficiency. Useful for running multiple tasks simultaneously.

In this example, a Step Functions state machine is created with a Parallel state that contains two branches:

Branch 1: Invokes a Lambda function (MyLambdaFunction).

Branch 2: A Pass state that simply passes the input along with a fixed result (Branch 2 Result).

Pass: We pass data between states without additional processing. Useful for transmitting data or executing simple tasks.

In this example, we create an AWS Cloud Development Kit (CDK) stack that defines a Step Functions state machine with a Pass state. The Pass state returns the string 'Hello, World!' as a result.

We then create another Pass state called LogPassState, which logs a message to CloudWatch Logs with the result 'Hello from Pass state!' and sets the result path to 'Log'.

Finally, we connect the LogPassState after the initial Pass state in the state machine using the state_machine.next(log_pass_state) statement.

Succeed: We indicate that a task has been completed successfully. Useful for marking the successful completion of a task.

In this example, we define a simple Step Function state machine with two states:

StartState: A Pass state that does nothing and immediately transitions to the next state.

SucceedState: A Succeed state, which immediately succeeds when reached.

You can use the AWS CDK to deploy this Step Function stack to your AWS account. This example creates a basic Step Function workflow that starts with the StartState and then immediately succeeds when it reaches the SucceedState.

Map: We iterate over a list of elements and apply a task to each. Useful for applying a task to multiple elements.

In this example, we create a Step Function that uses a Map state to iterate over items in an array and apply a Pass state as the iterator function. The Map state takes a list of items from the input and applies the Pass state to each item, storing the results in the specified path. This allows you to process multiple items in parallel using AWS Step Functions.

TaskInput: We specify the input for tasks in the pipeline. Useful for defining the input for tasks.

In this example:

1. We create a Lambda function and an IAM role for the Lambda function.
2. We define a Step Function task input using TaskInput.from_text().
3. We create a Step Function task using LambdaInvoke and provide the Lambda function and the task input.
4. We define a Step Function state machine and set the task as its definition.

Within our main pipeline, we also use various tasks:

LambdaInvoke: We invoke Lambda functions. Useful for executing custom code in response to events.

In this example:

1. We create an AWS Lambda function using the aws_lambda.Function class.
2. We define a Step Functions state machine using the aws_stepfunctions.StateMachine class, with a single state that invokes the Lambda function using the aws_stepfunctions_tasks.LambdaInvoke class.
3. We specify the Lambda function to be invoked in the LambdaInvoke task.
4. We set a timeout for the state machine.
5. Finally, we create a CDK app, create an instance of the LambdaStepFunctionStack class, and synthesize the CloudFormation template for deployment.

You'll need to have a Lambda function code available in a directory called "lambda" with a file named "index.py" that contains a function named "handler" for this example to work. You can adjust the code and resources for your specific use case.

StepFunctionsStartExecution: We initiate executions of other StepFunctions. Useful for orchestrating complex workflows.

In the example above, we define a Step Functions State Machine using sfn.StateMachine and a Lambda function using tasks.LambdaInvoke. The Lambda function is responsible for starting an execution of the state machine. We then create a Step Functions State Machine task using sfn.StateMachineStartExecution to start the execution.

ECSRunTask: We run tasks in Amazon Elastic Container Service (ECS). Useful for running containers in container clusters.

In this example:

1. We create an ECS cluster and an ECS Fargate task definition.
2. We add a container to the task definition using the Nginx image.
3. We define an ECS service that uses the task definition and runs it within a VPC.
4. We create an AWS Step Functions state machine that includes an EcsRunTask task, which runs the ECS task in the cluster we defined earlier.
5. The state machine is created, and the CDK application is synthesized.

Say goodbye to resource provisioning headaches and hello to streamlined process management.

Let AWS CDK do the heavy lifting for you!