Tutorial / Cram Notes

Data ingestion is the process of obtaining and importing data for immediate use or storage in a database. AWS offers a robust set of services to help ML practitioners orchestrate both batch-based and streaming-based data ingestion pipelines, which are critical for training machine learning models effectively.

Batch-Based ML Workloads

For batch processing, where data is collected over a period of time and processed in large chunks, services like AWS Glue and Amazon EMR are commonly employed.

AWS Glue:

AWS Glue is a managed extract, transform, and load (ETL) service that makes it easy to prepare and load your data for analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. AWS Glue is serverless, so there’s no infrastructure to set up or manage.

Amazon EMR:

Amazon EMR (Elastic MapReduce) provides a managed Hadoop framework that enables you to easily add or remove capacity from your clusters, run petabyte-scale analysis at less than half of the cost of traditional on-premises solutions, and support a variety of distributed frameworks such as Apache Hadoop, Apache Spark, and more.

For example, you might use AWS Glue to catalog your data and prepare it for analysis, then use Amazon EMR to run complex data processing jobs. EMR supports various ML-related tasks, such as data classification, clustering, and regression.

Streaming-Based ML Workloads

For real-time processing, where data needs to be processed almost as soon as it is recorded, you would look at Amazon Kinesis, Amazon Managed Service for Apache Flink, and Amazon Kinesis Data Firehose.

Amazon Kinesis:

Amazon Kinesis makes it easy to collect, process, and analyze video and data streams in real time. Using Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning.

Amazon Managed Service for Apache Flink:

This service is a fully managed service that allows you to use Apache Flink to process streaming data. Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams.

Amazon Kinesis Data Firehose:

Kinesis Data Firehose is the easiest way to reliably load streaming data into data lakes, data stores, and analytics tools. It can capture, transform, and deliver streaming data into Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk, enabling near real-time analytics with existing business intelligence tools and dashboards.

Here is a basic example of setting up a Kinesis Data Firehose delivery stream that points to an Amazon S3 bucket where incoming streaming data can be stored:

  1. Create an Amazon S3 bucket to store your streaming data.
  2. Navigate to Amazon Kinesis Data Firehose in the AWS Console and create a new delivery stream.
  3. Select a source for your streaming data. You can choose to put data directly or to use Amazon Kinesis Stream as your source.
  4. Configure your destination by selecting Amazon S3 and setting the target bucket.
  5. Set up transformations and conversions as needed.
  6. Review and create the delivery stream.

Comparison Table

The following table compares the AWS services for data ingestion purposes:

Feature / Service AWS Glue Amazon EMR Amazon Kinesis Amazon Kinesis Data Firehose Amazon Managed Service for Apache Flink
Data Processing Type Batch Batch Streaming Streaming Streaming
Managed Service Yes Yes Yes Yes Yes
Scalability Automatic Manual/Auto Automatic Automatic Automatic
Real-time Processing No No Yes Yes Yes
Integration with Analytics Tools Yes Yes Yes Yes Yes
Serverless Yes No No Yes No

Each service mentioned fulfills specific roles in data ingestion and processing for machine learning workloads. As you prepare for the AWS Certified Machine Learning – Specialty (MLS-C01) exam, understanding the nuances of these services, their use cases, and how they complement each other is essential.

To orchestrate a comprehensive data ingestion pipeline on AWS, it is important to assess your workload requirements and choose the appropriate service, whether for periodic batch processing or continuous real-time data streaming. By leveraging these powerful AWS components, machine-learning practitioners are well-equipped to build scalable, efficient, and robust data ingestion pipelines.

Practice Test with Explanation

True or False: Amazon Kinesis enables you to collect, process, and analyze real-time, streaming data.

  • (A) True
  • (B) False

Answer: A) True

Explanation: Amazon Kinesis provides the ability to work with real-time streaming data, enabling the collection, processing, and analysis of this data as it arrives.

Which AWS service is best suited for batch processing workloads?

  • (A) Amazon Kinesis Data Firehose
  • (B) AWS Glue
  • (C) Amazon EMR
  • (D) Amazon Managed Service for Apache Flink

Answer: C) Amazon EMR

Explanation: Amazon EMR is a cloud big data platform for processing vast amounts of data using open-source tools such as Apache Spark and Hadoop, making it well-suited for batch processing workloads.

True or False: AWS Glue is a fully managed ETL (extract, transform, and load) service that can be used for both stream and batch processing.

  • (A) True
  • (B) False

Answer: A) True

Explanation: AWS Glue is a fully managed ETL service that can prepare and load data for analytics both in batch and streaming workflows.

Which AWS service can automatically scale to accommodate data throughput?

  • (A) Amazon Kinesis Data Streams
  • (B) Amazon S3
  • (C) AWS Direct Connect
  • (D) Amazon EC2

Answer: A) Amazon Kinesis Data Streams

Explanation: Amazon Kinesis Data Streams can scale elastically to accommodate data throughput, allowing it to handle large volumes of streaming data.

Amazon Kinesis Data Firehose primarily serves what purpose?

  • (A) Real-time data streaming
  • (B) ETL jobs
  • (C) Data ingestion and loading to other AWS services
  • (D) Batch processing

Answer: C) Data ingestion and loading to other AWS services

Explanation: Amazon Kinesis Data Firehose is used for data ingestion, capturing, transforming, and loading streaming data into Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and other AWS services.

True or False: Amazon Managed Service for Apache Flink is specifically designed for long-running, highly available applications and supports both batch and streaming workloads.

  • (A) True
  • (B) False

Answer: A) True

Explanation: Amazon Managed Service for Apache Flink is designed for highly available, long-running applications. It supports both batch processing and stream processing, offering capabilities for stateful computations over data streams.

Which of the following is NOT a typical use case for AWS Glue?

  • (A) Data discovery
  • (B) Data cataloging
  • (C) Real-time data processing
  • (D) ETL processing

Answer: C) Real-time data processing

Explanation: AWS Glue is not designed for real-time data processing. It is used for data discovery, cataloging, and ETL processing, which are typically batch-oriented tasks.

In the context of Amazon Kinesis, what does a “shard” represent?

  • (A) A unit of data storage
  • (B) A unit of data throughput
  • (C) A managed ETL job
  • (D) An individual record within a data stream

Answer: B) A unit of data throughput

Explanation: In Amazon Kinesis, a shard represents a unit of data throughput consisting of a sequence of data records in a stream.

Amazon Kinesis Data Firehose can directly load data into which of the following data stores?

  • (A) Amazon S3
  • (B) Amazon Redshift
  • (C) Amazon DynamoDB
  • (D) Both (A) and (B) are correct

Answer: D) Both (A) and (B) are correct

Explanation: Amazon Kinesis Data Firehose can load data directly into Amazon S3 and Amazon Redshift, among other AWS services.

True or False: AWS Glue can only access data stored in Amazon S

  • (A) True
  • (B) False

Answer: B) False

Explanation: AWS Glue can connect to various data sources, not only Amazon S3, including Amazon RDS, Amazon Redshift, and databases on Amazon EC

Multiple select: Which of the following are capabilities of Amazon EMR?

  • (A) Stream processing
  • (B) Machine learning
  • (C) Interactive analysis
  • (D) All of the above

Answer: D) All of the above

Explanation: Amazon EMR supports a variety of big data use cases, including stream processing, machine learning, and interactive analysis, by using open-source frameworks such as Apache Spark and Apache Hadoop.

True or False: Amazon Managed Service for Apache Flink is suitable for complex event processing (CEP).

  • (A) True
  • (B) False

Answer: A) True

Explanation: Amazon Managed Service for Apache Flink is designed to handle complex event processing, allowing for the analysis of streaming data in real time.

Interview Questions

Can you describe how Amazon Kinesis and Amazon Kinesis Data Firehose differ when setting up real-time data ingestion for machine learning workloads?

Amazon Kinesis Data Streams is designed for building custom real-time data processing applications, whereas Amazon Kinesis Data Firehose is used for reliably loading streaming data into data lakes, data stores, and analytics services. Kinesis Data Streams requires manual provisioning of throughput, and you usually consume the data using a custom application written with a Kinesis client library. Kinesis Data Firehose provides automatic scaling and requires no ongoing administration; it enables you to create a fully managed delivery stream that automatically sends data to a specified destination such as Amazon S3 or Redshift.

How would you use AWS Glue in the context of a batch-based machine learning workload?

AWS Glue can be used for various components of batch-based ML workloads such as data cataloging, data preparation, and ETL (extract, transform, load) processes. AWS Glue can discover and catalog metadata about the data stores, facilitating data exploration, and ETL scripts can be generated, which can be scheduled to prepare the data for batch machine learning training jobs.

What role does Amazon EMR play in machine learning data pipelines, and how would you integrate it with other AWS services for ML workloads?

Amazon EMR is a cloud-native big data platform that can process vast amounts of data quickly and cost-effectively using open-source tools like Apache Spark and Hadoop. In the context of ML, you can use EMR to run Spark MLlib for machine learning pipelines. EMR can be seamlessly integrated with Amazon S3 for data storage, AWS Glue for a metadata repository, and Amazon Redshift for analytics, providing a comprehensive and scalable analytics platform.

How can one manage stateful transformations in a streaming data pipeline using Amazon Managed Service for Apache Flink?

With Amazon Managed Service for Apache Flink, stateful transformations can be managed by defining a state object in the Flink application. Flink provides rich state primitives that can be checkpointed and restored, ensuring fault tolerance and allowing for complex event-driven processing. The managed service takes care of scaling, maintaining, and monitoring the Flink applications, which simplifies the operational aspect of managing stateful streaming applications.

What benefits does AWS Glue provide in building a data ingestion pipeline when compared to manually configuring an ETL process on Amazon EMR?

AWS Glue automates much of the undifferentiated heavy lifting that comes with ETL processes. It provides a managed environment that simplifies the discovery, preparation, and combination of data for analytics, machine learning, and application development. Unlike manual ETL configurations on EMR, you do not need to manage the server resources, worry about scaling, monitor jobs, or manually write ETL scripts, as AWS Glue generates code for you.

In machine learning workloads that require a lambda architecture (combining batch and real-time processing), how might you incorporate AWS services for data ingestion?

For batch processing, you could use AWS Glue or Amazon EMR to handle the heavy lifting of ETL jobs. For real-time processing, Amazon Kinesis Data Streams or Amazon Managed Service for Apache Flink would be appropriate. The batch and real-time outputs could converge into a storage layer such as Amazon S3, which serves as an immutable data source for ML training and inference with services like Amazon SageMaker.

When should you choose Amazon Managed Streaming for Apache Kafka (Amazon MSK) over Amazon Kinesis for streaming machine learning workloads?

Choose Amazon MSK if you’re already running Apache Kafka on-premises and wish to migrate to the cloud with minimal changes to your existing applications, or if you require specific Apache Kafka features and integrations. If you prioritize ease of use and integration with AWS services, then Amazon Kinesis might be more suitable for AWS-native workloads.

Can you describe how AWS Glue DataBrew helps in preprocessing data for machine learning?

AWS Glue DataBrew is a visual data preparation tool that allows data scientists and data analysts to clean and normalize data without writing code. With DataBrew, users can interactively discover, combine, and transform data to prepare it for machine learning. It provides over 250 built-in transformations to automate data preparation tasks, such as handling missing values and normalization, which are essential for ML model training and feature engineering.

Explain how you would handle incremental data loading into a machine learning model using AWS services.

To handle incremental data loading, you might set up a batch process using AWS Glue or Amazon EMR to periodically process new data and integrate it into your existing datasets in Amazon S Alternatively, you could use a combination of Amazon Kinesis Data Streams for real-time data ingestion and Amazon Kinesis Data Firehose to batch, compress, and load the streaming data into S3 at specified intervals, which your machine learning model can then consume.

What mechanisms are available in Amazon Kinesis Data Streams to ensure data is processed in the correct order for machine learning predictions?

Amazon Kinesis Data Streams ensures ordered data processing at the shard level. When you use Kinesis Data Streams, data records are partitioned based on a partition key that you specify, and records with the same partition key are delivered in order to the same shard. To maintain order, ensure that the same partition key is used for all records that require ordering. Consumers such as Kinesis Data Analytics or custom applications built using the Kinesis Client Library (KCL) can then process records in the order they arrived.

Describe a situation where you would use AWS Lambda in coordination with Amazon Kinesis for machine learning purposes.

AWS Lambda can process streaming data directly from Amazon Kinesis, allowing you to write custom logic to perform lightweight data transformation, filtering, or aggregation before it is sent to the downstream ML model for inference. It’s particularly useful for serverless applications where you need to react to data in real time, and have no need for the more heavy-weight processing capabilities or management overhead of an Apache Flink or Apache Spark framework.

What’s the advantage of using Amazon Kinesis Data Firehose for ML workloads when integrating with Amazon Redshift or Amazon S3?

Amazon Kinesis Data Firehose offers a fully managed, automatic scaling service for data ingestion into Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk. When dealing with ML workloads that require data analysis, Amazon Kinesis Data Firehose can simplify the data pipeline by loading the streaming data directly into Amazon Redshift for SQL-based analytics or into S3 for getting insights using other AWS analytics and machine learning services. It provides a no-code solution for streaming ETL tasks, making it highly efficient for machine learning data pipelines that need to quickly transform and relay data to analytics databases or data lakes.

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Dominggus Van de Veerdonk

Great blog post! I’m currently preparing for the AWS Certified Machine Learning – Specialty exam and found the section on Amazon Kinesis very helpful.

Olga Kloc
6 months ago

I have some experience with AWS Glue, but I’m still confused about when to prefer AWS Glue over Amazon EMR for data transformation tasks. Can anyone clarify?

Youri Steur
7 months ago

Thanks for the detailed write-up. The comparison between batch-based and streaming-based ML workloads was particularly enlightening.

Joe Gardner
5 months ago

Can someone explain how Amazon Kinesis Data Firehose is different from Amazon Kinesis Data Streams?

Akshay Kavser
7 months ago

The practice questions for Amazon Kinesis and AWS Glue were spot on! Thanks for the helpful content.

Marilda Monteiro
6 months ago

I appreciate the inclusion of Amazon Managed Service for Apache Flink. It’s often overlooked but very powerful for streaming applications.

Conchita Moya
6 months ago

Has anyone here used both AWS Glue and Amazon EMR for batch processing? Which one do you find more efficient?

Chloe Clarke
6 months ago

I didn’t find the section on Amazon Managed Service for Apache Flink as detailed as I hoped.

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