Concepts
Data ingestion patterns define how frequently and in what manner data is collected and brought into a data storage or processing system. When preparing for the AWS Certified Data Analytics – Specialty (DAS-C01) or the AWS Certified Data Engineer – Associate (DEA-C01) exams, which are AWS certifications oriented towards professionals who design and implement data engineering solutions, it’s important to understand these patterns as well as some associated AWS services. Here are some common data ingestion patterns:
Batch Data Ingestion
Batch data ingestion involves collecting data in chunks at scheduled intervals, which could range from hourly to daily or even monthly. This pattern is suitable when real-time analysis is not crucial. For instance, daily sales reports or monthly financial closings are typically processed in a batch mode.
AWS Services for Batch Data Ingestion:
- AWS Glue: A managed ETL (Extract, Transform, Load) service that can schedule and automate the ingestion and transformation of large batches of data.
- Amazon S3: As a storage service, S3 can act as a central repository where batch data files are stored before being processed.
Example:
create_job(
Name=’DailySalesImportJob’,
Role=’GlueServiceRole’,
Command={‘Name’: ‘glueetl’, ‘ScriptLocation’: ‘s3://my-scripts/daily_sales_ingest.py’}
)
start_crawler(
Name=’DailySalesCrawler’
)
schedule_job(
JobName=’DailySalesImportJob’,
Schedule=’cron(0 1 * * ? *)’ # Run daily at 1 AM UTC
)
Real-time Data Ingestion
Real-time data ingestion involves processing data almost immediately as it becomes available. This approach supports use cases such as live dashboards, fraud detection, or real-time recommendations.
AWS Services for Real-time Data Ingestion:
- Amazon Kinesis: Provides services for real-time data streaming and analytics.
- AWS Lambda: Can process data in real time as it arrives in services like Amazon S3 or Amazon DynamoDB through event-driven triggers.
Example:
def lambda_handler(event, context):
for record in event[‘Records’]:
# Process each record and perform real-time analytics or storage
process_record(record)
Historical Data Ingestion (Data Backfilling)
There might also be scenarios where historical data needs to be ingested into the system to either backfill data for analytics or to move data to a new storage solution. This is often a one-time operation but may involve a vast amount of data.
AWS Services for Historical Data Ingestion:
- AWS Data Pipeline: Can be used to transfer historical data between AWS services or from on-premises to AWS.
- Amazon S3 Transfer Acceleration: Minimizes the time required to transfer large historical datasets into S3.
Incremental Data Ingestion
When only new or updated data needs to be ingested, the incremental data ingestion pattern is used. It is more efficient as it avoids reprocessing the entirety of the dataset.
AWS Services for Incremental Data Ingestion:
- AWS DMS (Database Migration Service): Supports ongoing replication and can be configured to handle incremental data changes.
- AWS Glue: Tracks data changes via job bookmarks to process incremental loads.
AWS Data Ingestion Patterns Comparison:
| Pattern | Use Case | AWS Services | Frequency |
|---|---|---|---|
| Batch | Daily sales reports | AWS Glue, Amazon S3 | Scheduled intervals |
| Real-time | Live dashboards, fraud detection | Amazon Kinesis, AWS Lambda | Immediate |
| Historical (Backfill) | Data analytics backfill | AWS Data Pipeline | One-time |
| Incremental | New or updated data | AWS DMS, AWS Glue | As changes occur |
Each data ingestion pattern has its place and can be integral in a robust AWS data engineering strategy. Understanding when and how to utilize these patterns aligns with the competencies required for the AWS Certified Data Analytics – Specialty and AWS Certified Data Engineer – Associate certification exams.
Answer the Questions in Comment Section
True or False: Data ingestion refers to the process of transporting data from various sources into a system where it can be stored, analyzed, or processed.
- True
- False
Answer: True
Explanation: Data ingestion is the process of obtaining and importing data for immediate use or storage in a database.
Which AWS service is primarily used for real-time data ingestion?
- AWS RDS
- AWS S3
- AWS Kinesis
- AWS Redshift
Answer: AWS Kinesis
Explanation: AWS Kinesis is designed for real-time data streaming and ingestion.
Batch data ingestion typically involves which of the following characteristics?
- Immediate processing of data
- Continuous import of data
- Scheduled import of large volumes of data
- Low-latency data availability
Answer: Scheduled import of large volumes of data
Explanation: Batch data ingestion involves the scheduled import of large volumes of data, which is processed at specific intervals.
True or False: In the context of AWS, AWS Glue can be used to schedule and orchestrate the batch ingestion of data.
- True
- False
Answer: True
Explanation: AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it simple to prepare and load your data for analytics.
Which of the following options are suitable for ingestion of streaming data? (Select two)
- AWS Snowball
- Amazon Kinesis Data Firehose
- Amazon RDS
- Amazon Kinesis Data Streams
- Amazon S3
Answer: Amazon Kinesis Data Firehose, Amazon Kinesis Data Streams
Explanation: Both Amazon Kinesis Data Firehose and Amazon Kinesis Data Streams are designed for the ingestion of streaming data in real-time.
True or False: The frequency of data ingestion can impact data storage costs.
- True
- False
Answer: True
Explanation: The frequency of data ingestion can lead to more data being stored, which can, in turn, affect storage costs.
In AWS, which service is often used for periodic, scheduled data ingestion jobs?
- Amazon EC2
- AWS Glue
- AWS Lambda
- Amazon Kinesis Data Streams
Answer: AWS Glue
Explanation: AWS Glue provides capabilities to schedule periodic ETL jobs, making it suitable for batch data ingestions.
When designing a data lake in AWS, which of the following approaches can be used for ingesting historical data? (Select two)
- AWS DataSync
- Amazon Kinesis Data Streams
- AWS Direct Connect
- Amazon QuickSight
Answer: AWS DataSync, AWS Direct Connect
Explanation: Both AWS DataSync and AWS Direct Connect can facilitate large-scale data transfer that is often required when ingesting historical data into a data lake.
True or False: AWS DMS (Database Migration Service) can only be used for one-time migration and not for continuous data replication.
- True
- False
Answer: False
Explanation: AWS DMS supports both one-time migrations and continuous data replication, making it suitable for data ingestion use cases that require ongoing data transfer.
What is a common challenge associated with the ingestion of high-velocity data?
- Ensuring data quality
- Obtaining data sources
- Visualizing data
- Archiving data
Answer: Ensuring data quality
Explanation: When dealing with high-velocity data ingestion, ensuring data quality and consistency can be challenging due to the rapid inflow of data.
Which AWS service offers a managed Kafka service, which can be used for streaming data ingestion?
- Amazon Athena
- Amazon MSK (Managed Streaming for Kafka)
- AWS Data Pipeline
- Amazon Redshift
Answer: Amazon MSK (Managed Streaming for Kafka)
Explanation: Amazon MSK provides a fully managed service that runs Apache Kafka, which is useful for ingesting streaming data.
True or False: The AWS Snow family of services is designed for both online and offline data ingestion, including bulk data transfer and edge computing.
- True
- False
Answer: True
Explanation: The AWS Snow family (e.g., AWS Snowball, AWS Snowmobile) is designed to handle both online and offline data transfers, suitable for bulk data ingestion and edge computing cases.
Great blog post! The explanation on data ingestion frequency was very clear.
Thank you for the insights on data history. It’s an area I’ve been struggling with.
The differences between batch and real-time ingestion patterns are very well explained. Appreciate the details!
Awesome content! Does anyone know how data retention policies impact ingestion strategies?
Retention policies are critical. They can dictate the volume of data ingested and how frequently it’s archived.
Agree. Retention policies also affect compliance, so it’s essential to consider legal requirements.
How do you handle schema changes in a real-time ingestion pipeline?
Schema evolution tools can help, but they add complexity. Test thoroughly before deploying any changes.
We’ve used tools like Apache Avro with good success. It’s a challenge but manageable.
Is there a preferred AWS service for managing large-scale batch data ingestion?
AWS Glue is quite powerful for ETL jobs and integrates well with a variety of data sources.
I’d recommend AWS Data Pipeline for more complex workflows. It offers great flexibility.
This article is a goldmine! Any thoughts on data deduplication when ingesting real-time data?
Real-time deduplication can be tricky. We use Kafka Streams for this, but it does add latency.
Another option is AWS Lambda for processing deduplication logic as events are ingested.
Thanks for the detailed explanation on data ingestion patterns!