Concepts
This method involves ingesting data at predefined intervals, which could range from minutes to hours, or even on a daily or weekly schedule. AWS offers various solutions to achieve this.
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it reliably between various data stores. You can set up a schedule for AWS Glue jobs to run batch ingestion tasks at regular intervals.
Amazon CloudWatch Events allows you to schedule automated actions that self-trigger at certain times using cron or rate expressions. For example, you could trigger an AWS Lambda function to perform data ingestion tasks.
Example: To schedule a daily batch data ingestion task using AWS Glue, you can configure a Glue Crawler to run every 24 hours to discover new data, and a Glue Job can be set to process and move the data into the target data store.
Event-Driven Batch Data Ingestion
Event-driven ingestion is about ingesting data in response to certain events or triggers. This can be more efficient than scheduled ingestion as it processes data as soon as it becomes available.
AWS Lambda can be used to ingest data in response to events. For example, a new file uploaded to an S3 bucket can trigger a Lambda function to ingest the file into a database.
Amazon S3 Event Notifications can trigger AWS Lambda functions, Amazon Simple Notification Service (SNS) topics, Amazon Simple Queue Service (SQS) queues, or even directly invoke AWS Glue ETL jobs when new objects are uploaded to S3.
Example: To set up an event-driven data ingestion pipeline, configure an Amazon S3 bucket to send event notifications to an AWS Lambda function upon the arrival of new files. The Lambda function then processes the files and inserts the data into the target data storage.
Comparison
| Aspect | Scheduled Ingestion | Event-Driven Ingestion |
|---|---|---|
| Timing | At regular intervals (cron schedule) | Triggered by specific events |
| Control | High level of control over when it runs | Reactive to data availability |
| Complexity | Can be complex to manage different schedules | Simpler, as it’s managed by AWS services |
| Resources | Resources are utilized irrespective of data | Resources are used only when data arrives |
| Latency | Higher latency compared to event-driven | Low latency, as data is processed instantly |
| Best for | Regular, predictable workloads | Real-time or unpredictable workloads |
When preparing for the AWS Certified Data Engineer – Associate (DEA-C01) exam, understanding the trade-offs between these two methods is crucial. You should be well-versed in the AWS services that facilitate batch data ingestion and be able to apply them to various scenarios.
Lastly, when implementing either of these methods, pay special attention to error handling, data validation, and monitoring to ensure that data flows are reliable and accurate. Using AWS CloudWatch for monitoring and AWS CloudTrail for auditing is recommended to keep track of the ingestion pipelines and their performance.
Answer the Questions in Comment Section
(True/False) AWS Glue can be used to schedule and perform batch data ingestion jobs.
- True
Correct Answer: True
Explanation: AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it simple to prepare and load data for analytics. You can create, schedule, and run ETL jobs with a point-and-click interface.
(Single Select) Which AWS service is commonly used for event-driven batch data ingestion?
- A) AWS Lambda
- B) AWS Batch
- C) Amazon S3
- D) Amazon Kinesis
Correct Answer: A) AWS Lambda
Explanation: AWS Lambda can be used for event-driven data ingestion by triggering functions in response to events such as file uploads to Amazon S
(True/False) Amazon Kinesis is best suited for real-time data ingestion rather than batch data ingestion.
- True
Correct Answer: True
Explanation: Amazon Kinesis provides services to facilitate real-time data streaming and analytics, although it can also be used for micro-batch processing with Kinesis Data Firehose.
(Multiple Select) Which AWS services provide mechanisms for scheduling batch data ingestion jobs? (Select two)
- A) AWS Step Functions
- B) Amazon Redshift
- C) AWS Data Pipeline
- D) Amazon S3
Correct Answer: A) AWS Step Functions, C) AWS Data Pipeline
Explanation: AWS Step Functions allows you to coordinate multiple AWS services into serverless workflows, and AWS Data Pipeline is a web service to automate the movement and transformation of data.
(True/False) AWS Direct Connect is a service that enhances batch data ingestion by providing a dedicated network connection to AWS.
- True
Correct Answer: True
Explanation: AWS Direct Connect provides a dedicated network connection to AWS, which can increase bandwidth throughput and provide a more consistent network experience than internet-based connections, enhancing data transfer tasks like batch data ingestion.
(Single Select) Which AWS service is specifically designed for batch processing workloads?
- A) AWS Batch
- B) Amazon EMR
- C) Amazon RDS
- D) Amazon EC2
Correct Answer: A) AWS Batch
Explanation: AWS Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS.
(Single Select) Which of the following services can be used for orchestrating batch data ingestion workflows?
- A) Amazon EC2
- B) Amazon S3
- C) Amazon SQS
- D) Amazon SWF
Correct Answer: D) Amazon SWF
Explanation: Amazon Simple Workflow Service (SWF) is a web service that helps to coordinate work across distributed components for workflows, including batch data ingestion and processing.
(True/False) Amazon SQS is primarily used for message queuing and is not suitable for batch data ingestion.
- True
Correct Answer: True
Explanation: Amazon Simple Queue Service (SQS) is used for message queuing and is not designed to be a data ingestion or storage service, although it may orchestrate components in a data ingestion pipeline.
(Single Select) Why might you choose S3 Transfer Acceleration over standard S3 data transfer methods for batch data ingestion?
- A) To reduce costs
- B) To comply with data privacy laws
- C) To increase transfer speed
- D) To preserve data lineage
Correct Answer: C) To increase transfer speed
Explanation: S3 Transfer Acceleration is a feature that enables faster, more consistent data transfers to Amazon S3 from globally distributed client locations via AWS edge locations.
(True/False) You can trigger AWS Lambda functions using Amazon CloudWatch Events for scheduled batch data ingestion.
- True
Correct Answer: True
Explanation: Amazon CloudWatch Events (now part of Amazon EventBridge) can be used to trigger AWS Lambda functions on a scheduled basis or in response to system events.
(True/False) AWS DataSync cannot be used to transfer data over the Internet for batch data ingestion purposes.
- False
Correct Answer: False
Explanation: AWS DataSync is a data transfer service that simplifies, automates, and accelerates moving data between on-premises storage systems and AWS services over the Internet or AWS Direct Connect.
(Multiple Select) Which of the following are benefits of using AWS for batch data ingestion? (Select two)
- A) Automated scaling
- B) Reduced latency
- C) Free data transfer
- D) Unlimited storage
Correct Answer: A) Automated scaling, B) Reduced latency
Explanation: AWS provides services with automated scaling capabilities to handle varying workloads and features like AWS Direct Connect and S3 Transfer Acceleration to reduce transfer latency. Data transfer is not always free and is subject to AWS pricing, and while AWS provides scalable storage, it is not unlimited and is also subject to costs based on usage.
This blog post on batch data ingestion is very insightful! I particularly found the section on scheduled ingestion to be very comprehensive.
Thanks for the detailed write-up on event-driven ingestion. It cleared up a lot of my doubts.
Can someone explain how event-driven ingestion differs from scheduled ingestion in terms of scalability?
Event-driven ingestion scales more dynamically because it can handle data as it comes without waiting for a scheduled time. It is more responsive to high data volumes in real-time compared to scheduled ingestion.
Precisely, plus event-driven ingestion can better handle spikes in data volume by processing data immediately as events occur, which can be more efficient for applications needing real-time analytics.
How does AWS Kinesis fit into the whole event-driven ingestion paradigm?
AWS Kinesis is perfect for event-driven ingestion as it allows you to continuously capture gigabytes of data per second from hundreds of thousands of sources. It can then be processed in real-time which is ideal for event-driven requirements.
Great post! It really helped me understand the concepts better. Appreciate it!
I’m having trouble deciding when to use batch ingestion vs. event-driven ingestion. Any advice?
Batch ingestion is better when you don’t need real-time data processing and can accumulate data to process at a later time. Event-driven ingestion is ideal for real-time data processing needs, such as monitoring or immediate analytics.
To add on, scheduled ingestion is more cost-effective for non-critical data, while event-driven might be more expensive due to its real-time nature but is crucial for applications needing up-to-the-second data.
Very informative blog post, thanks!
Does anyone have experience using AWS Glue for scheduled batch data ingestion?
Yes, I’ve been using AWS Glue for scheduled batch ingestion. It’s very efficient for ETL processes and integrates well with various data sources and targets within the AWS ecosystem.