Serverless computing services like AWS Lambda let you run code in response to events while having the underlying compute resources managed for you automatically. Data warehousing solutions may be created utilizing it, along with a range of other applications and services.
To collect data from multiple sources, convert it, and clean it before importing it into a data warehouse like Amazon Redshift or Snowflake, one method to utilize AWS Lambda for data warehousing is to set up a Lambda function that runs on a regular basis (for example, every hour or every day). An Amazon CloudWatch Event, which enables you to establish a timetable for the function’s execution, can start a Lambda function.
For instance, you might configure a Lambda function to get data from a database or API, change the data using a Python script, and then load the modified data into a Redshift table using the Redshift COPY command. To make sure the data in the data warehouse is up to date, the function might be programmed to run every hour.
Another method to utilize AWS Lambda for data warehousing is to configure a Lambda function to execute in response to certain events, such as data being uploaded to an Amazon S3 bucket or an Amazon Kinesis stream. The new data may then be processed by the function and loaded into the data warehouse. With the help of this, you can create real-time data pipelines that continually ingest and analyze data as it becomes available.
AWS Lambda may, in general, be a helpful tool for developing data warehousing solutions because it makes it simple to create and deploy code that can extract, convert, and load data into a data warehouse without the need to maintain servers or infrastructure.
Why AWS Lambda over Apache Airflow/Snowflake?
AWS Lambda is a serverless computing service that lets you run code in response to events and handles the underlying compute resources automatically on your behalf. Data warehousing solutions are only one of the many applications and services that can be created with it.
Apache Airflow, on the other hand, is an open-source platform for planning and controlling data pipelines. Data pipelines can be defined, carried out, and observed as directed acyclic graphs (DAGs) of activities. Data warehousing workflows and other complicated workflows may be created and automated using Airflow.
With Snowflake, a cloud-based data warehouse service, you can store, query, and utilize SQL to analyze enormous volumes of data. It provides support for data loading and transformation, data sharing and security, and performance optimization, among other features for data warehousing.
Each of these technologies has certain advantages and may serve a variety of functions in a data warehousing system. Building real-time data pipelines and executing brief, event-driven activities are two uses for AWS Lambda that stand out. For organizing and scheduling jobs in a production setting, as well as for creating and automating complicated processes, such as data warehousing workflows, Airflow is an excellent option. A robust data warehouse service called Snowflake is designed to store, query, and analyze enormous volumes of data.
The precise needs of your data warehousing solution and the trade-offs you are prepared to make between features, complexity, and cost will largely determine whether you choose to use AWS Lambda, Airflow, or Snowflake.