In today’s fast-paced world, businesses need to make quick decisions based on real-time data. This is where serverless data analytics comes into play. Serverless data analytics is a cloud-based approach to data processing that allows businesses to analyze data in real-time without the need for a dedicated server. In this article, we will discuss how to perform real-time serverless data analytics by combining streaming data source and CDC data with AWS Glue, AWS DMS, and Amazon DynamoDB on Amazon Web Services.
What is Serverless Data Analytics?
Serverless data analytics is a cloud-based approach to data processing that allows businesses to analyze data in real-time without the need for a dedicated server. This approach is becoming increasingly popular because it allows businesses to scale their data processing needs without having to worry about managing servers or infrastructure.
AWS Glue
AWS Glue is a fully managed ETL (Extract, Transform, Load) service that makes it easy to move data between different data stores. It allows businesses to create and run ETL jobs that extract data from various sources, transform the data, and load it into a target data store.
AWS DMS
AWS DMS (Database Migration Service) is a fully managed service that makes it easy to migrate databases to AWS. It allows businesses to migrate their databases to AWS with minimal downtime and no data loss.
Amazon DynamoDB
Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. It allows businesses to store and retrieve any amount of data, at any time, from anywhere in the world.
Combining Streaming Data Source and CDC Data
To perform real-time serverless data analytics, businesses need to combine streaming data source and CDC (Change Data Capture) data. Streaming data source refers to real-time data that is generated continuously, such as sensor data or log files. CDC data refers to changes made to a database, such as inserts, updates, and deletes.
To combine streaming data source and CDC data, businesses can use AWS Glue and AWS DMS. AWS Glue can be used to extract data from streaming data sources and transform it into a format that can be loaded into Amazon DynamoDB. AWS DMS can be used to capture changes made to a database and replicate them to Amazon DynamoDB.
Performing Real-Time Serverless Data Analytics
To perform real-time serverless data analytics, businesses need to follow these steps:
1. Set up a streaming data source: Businesses need to set up a streaming data source that generates real-time data continuously.
2. Set up CDC: Businesses need to set up CDC on their database to capture changes made to the database.
3. Extract and transform data: Businesses need to use AWS Glue to extract data from the streaming data source and transform it into a format that can be loaded into Amazon DynamoDB.
4. Replicate changes: Businesses need to use AWS DMS to replicate changes made to the database to Amazon DynamoDB.
5. Analyze data: Once the data is loaded into Amazon DynamoDB, businesses can use various analytics tools to analyze the data in real-time.
Conclusion
Real-time serverless data analytics is becoming increasingly popular because it allows businesses to analyze data in real-time without the need for a dedicated server. By combining streaming data source and CDC data with AWS Glue, AWS DMS, and Amazon DynamoDB on Amazon Web Services, businesses can perform real-time serverless data analytics with ease. This approach allows businesses to make quick decisions based on real-time data, which can give them a competitive advantage in today’s fast-paced world.
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- Source: Plato Data Intelligence.