You will often find yourself dealing with large quantities of data that need to be moved into your cloud environment for processing and analysis. While real-time data ingestion is often a necessity, one cannot overlook the importance of batch data ingestion in the data engineering space.

Batch data ingestion refers to the process of capturing, loading, and processing data in micro-batches or batches as opposed to ingesting data in real-time. It can be either scheduled or event-driven.

  1. Scheduled Batch Data Ingestion:

As the name suggests, scheduled ingestion occurs at predefined set times. For example, it could be set to encapsulate and migrate data every night, or every week, or even every month depending on the usage frequency and sensitivity of the data.

Table of Contents

AWS Guide For Scheduled Ingestion

In terms of AWS, services like AWS Lambda and Amazon CloudWatch Events can be used to schedule data ingestion tasks. Here is a simplified process flow:

  1. Create a Lambda function that resonates with your data extraction and loading semantics.
  2. Create a new rule in CloudWatch Events and define the desired schedule expression.
  3. Add the Lambda function as the target for the newly created rule.
  4. The Lambda function will now run at the defined schedule, performing data ingestion as coded.
  1. Event-Driven Batch Data Ingestion:

Event-driven ingestion, on the other hand, is triggered by the occurrence of a specific event or a set of events. These could be any changes or updates in the sourced data, database triggers, message queues, IoT sensor data, logs, files in a storage bucket, and more.

AWS Tools For Event-Driven Ingestion

One of the commonly used Amazon services for event-driven data ingestion is AWS Glue. AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load your data for analytics. It can automatically discover and catalog metadata about your data stores in a centralized data catalog.

  • Amazon S3 Events can be monitored to trigger AWS Lambda, which can then initiate a Glue job to extract, transform, and load the data.
  • Amazon Kinesis Data Streams can collect and process large streams of data records. AWS Lambda can then read records from Kinesis and process them.
  • Amazon DynamoDB Streams can capture table activity, and their stream records can initiate AWS Lambda to load an Amazon Redshift database.
Scheduled Ingestion Event-Driven Ingestion
Trigger Time-based Event-based
AWS Services Used AWS Lambda, Amazon CloudWatch Amazon Glue, AWS Lambda, S3 events, Kinesis
Pros Easier scheduling, bulk processing Real-time response, finer granularity control.
Cons Inflexible, data latency Needs defined event

In conclusion, both scheduled and event-driven data ingestion play significant roles when dealing with big data. Your choice between scheduled or event-driven batch data ingestion would depend largely on your use case, the volume of data, and the requirements of the business process in question. As a best practice, it’s a good idea to understand and closely monitor your data operations to employ an optimum mix of both methodologies. Leveraging both the techniques gives you the flexibility to handle data bottlenecks as well as the capability to manage real-time business intelligence and analytics effectively.

Note: The process of preparing for Data Engineer – Associate (DEA-C01) or any AWS certification exam requires a deep understanding of each service along with hands-on experience. Always refer to the official AWS documentation for the most recent and accurate information.

Practice Test

True or False: Batch data ingestion is a process of importing data sets on a real-time basis.


Answer: False.

Explanation: Batch data ingestion is a method of importing data sets at scheduled intervals or after specific events, not in real-time.

Which of the following AWS services is not typically used for batch data ingestion?

  • a) AWS Glue
  • b) AWS Data Pipeline
  • c) AWS Kinesis
  • d) AWS Redshift

Answer: c) AWS Kinesis

Explanation: AWS Kinesis is mostly used for real-time data ingestion and streaming, whereas AWS Glue, AWS Data Pipeline and AWS Redshift are commonly used for batch data ingestion.

True or False: Event-driven ingestion occurs when data is ingested in response to a specific event.

Answer: True

Explanation: True, in event-driven ingestion, data is ingested into the system when a particular event triggers the ingestion process.

What does scheduled ingestion mean in the context of Batch data ingestion?

  • a) Importing data in real-time
  • b) Importing data every time an event occurs
  • c) Importing data at regular scheduled times
  • d) Importing data manually

Answer: c) Importing data at regular scheduled times

Explanation: Scheduled ingestion means importing data at pre-determined intervals or times, thus it doesn’t need manual intervention or specific events to begin.

What type of data is typically ingested using batch data ingestion protocols?

  • a) Time-series data
  • b) Real-time streaming data
  • c) IoT sensor data
  • d) Social media feeds in real-time

Answer: a) Time-series data

Explanation: Time-series data that doesn’t require real-time processing and analysis is typically suitable for batch ingestion methods.

True or False: Batch data ingestion processes are more resource-intensive and expensive than real-time data ingestion.

Answer: False

Explanation: Batch data ingestion can be more efficient and cost-effective than real-time data ingestion as they allow for the processing of large volumes of data at once, rather than continuously streaming and processing.

Which AWS service offers a way to automate the movement and transformation of data between different AWS services and on-premise data sources?

  • a) AWS Lambda
  • b) AWS Glue
  • c) AWS Batch
  • d) AWS DMS

Answer: b) AWS Glue

Explanation: AWS Glue is a managed Extract, Transform, Load (ETL) service that moves and reformats data between different storage and compute services.

True or False: In batch ingestion, data latency is higher compared to real-time ingestion.

Answer: True

Explanation: As batch ingestion processes data at regular intervals, it does have a higher latency compared to real-time ingestion which processes data as it arrives.

What is the major downside of batch data ingestion?

  • a) High latency
  • b) More compute resource utilization
  • c) High cost
  • d) All of above

Answer: a) High latency

Explanation: While batch data ingestion has its benefits, one major downside is its higher latency, as it handles data in batches rather than in real-time.

True or False: AWS Data Pipeline can be used for both real-time and batch data ingestion.

Answer: False

Explanation: AWS Data Pipeline is primarily used for moving, transforming and updating data between different AWS services and on-premise data sources on a scheduled or event-driven basis, leaning towards batch data ingestion. For real-time ingestion, AWS Kinesis is used.

Interview Questions

What is the role of a data engineer in batch data ingestion?

A data engineer is responsible for setting up, managing, and troubleshooting the systems that ingest, process, and store batch data.

What is the primary use of AWS Glue in terms of batch data ingestion?

AWS Glue provides a fully managed ETL (extract, transform, and load) service that makes it easy to move data between your data stores.

What is the proper AWS service for staging the data before ingestion?

Amazon S3 is typically used for staging the data before ingestion because it’s scalable, secure, and can handle large amounts of data.

What kind of data would you use batch data ingestion for?

Batch data ingestion is typically used for large amounts of structured and semi-structured data that does not need to be processed in real-time.

What is scheduled data ingestion?

Scheduled data ingestion is a method where data is ingested at predefined or scheduled times, such as once every 24 hours.

How is event-driven ingestion different from scheduled ingestion?

Event-driven ingestion ingests data as soon as a specific event occurs, while scheduled ingestion ingests data at predefined times.

What AWS service would you use for event-driven ingestion?

AWS Lambda is typically used for event-driven ingestion because it can trigger functions upon events such as the arrival of new data.

What kind of data storage works best with batch data ingestion in AWS?

Amazon Redshift, a fully managed, petabyte-scale data warehouse service, typically works best with batch data ingestion in AWS.

What is the role of AWS Kinesis in real-time and batch data ingestion?

AWS Kinesis allows real-time ingestion of data and then provides the option to batch that data for processing, making it a versatile choice for both real-time and batch data ingestion.

What does an ETL job in AWS Glue do?

An ETL job in AWS Glue prepares (extracts, transforms, and loads) the data for analytics by cleaning, normalizing, and moving the data from various sources into an analytics-friendly repository.

How does AWS Batch help in batch data ingestion?

AWS Batch helps in batch data ingestion by efficiently queuing, scheduling, and executing batch computing workloads across the full range of AWS compute services and features.

How do AWS IAM roles help in data ingestion?

AWS IAM roles help in data ingestion by managing permissions, ensuring only authorized services are able to access the data.

How does data partitioning in Amazon S3 support batch data ingestion?

Data partitioning in Amazon S3 supports batch data ingestion by organizing data in separate partitions, improving query performance and reducing costs by scanning relevant partitions only.

What part does AWS Step Functions play in batch data ingestion?

AWS Step Functions coordinates the components of batch data ingestion by orchestrating multiple Lambda functions into a defined workflow.

What is the role of Amazon Data Pipeline in batch data ingestion?

Amazon Data Pipeline facilitates the process of batch data ingestion by moving and transforming data across different AWS services and on-premise data sources.

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