Podcast
Questions and Answers
When designing a data ingestion layer, what is a key responsibility of a data engineer?
When designing a data ingestion layer, what is a key responsibility of a data engineer?
- Building a robust ingestion pipeline. (correct)
- Developing marketing strategies.
- Managing company finances.
- Overseeing human resources.
A company needs to ingest sales transaction data from retailers worldwide. The data is sent periodically, analyzed overnight, and reports are needed by morning. Which type of ingestion is most suitable?
A company needs to ingest sales transaction data from retailers worldwide. The data is sent periodically, analyzed overnight, and reports are needed by morning. Which type of ingestion is most suitable?
- Batch ingestion (correct)
- Micro-batch ingestion
- Real-time Streaming ingestion
- Lambda ingestion
A retailer's website generates clickstream data. The data science team needs to analyze this data immediately to provide product recommendations. Which ingestion method is most appropriate?
A retailer's website generates clickstream data. The data science team needs to analyze this data immediately to provide product recommendations. Which ingestion method is most appropriate?
- Real-time stream processing (correct)
- Batch processing
- Scheduled ingestion
- On-demand ingestion
In a traditional ETL process, which approach is typically used for data ingestion?
In a traditional ETL process, which approach is typically used for data ingestion?
In a stream processing architecture, what role do producers play?
In a stream processing architecture, what role do producers play?
What is a key design consideration when building a batch processing pipeline?
What is a key design consideration when building a batch processing pipeline?
What benefit does workflow orchestration provide in batch data ingestion?
What benefit does workflow orchestration provide in batch data ingestion?
What is a key characteristic to consider when evaluating pipeline design for batch ingestion?
What is a key characteristic to consider when evaluating pipeline design for batch ingestion?
A company wants to ingest data from a third-party source but needs to preview the datasets before subscribing. Which AWS service is most suitable for this task?
A company wants to ingest data from a third-party source but needs to preview the datasets before subscribing. Which AWS service is most suitable for this task?
An organization needs to transfer a large number of files from an on-premises file system to Amazon S3. Which AWS service is designed for this purpose?
An organization needs to transfer a large number of files from an on-premises file system to Amazon S3. Which AWS service is designed for this purpose?
Your company wants to ingest customer support tickets that reside in Zendesk into their data lake. Which AWS service is designed for ingesting data from SaaS applications?
Your company wants to ingest customer support tickets that reside in Zendesk into their data lake. Which AWS service is designed for ingesting data from SaaS applications?
A financial firm requires a tool to ingest transaction data from an Oracle database to Amazon S3 continuously. Which AWS service is most appropriate?
A financial firm requires a tool to ingest transaction data from an Oracle database to Amazon S3 continuously. Which AWS service is most appropriate?
An organization is using AWS Glue for batch ingestion and needs to identify the schema of incoming data. What feature of AWS Glue assists with this task?
An organization is using AWS Glue for batch ingestion and needs to identify the schema of incoming data. What feature of AWS Glue assists with this task?
A data engineer wants to visually create and manage ETL jobs in AWS Glue. Which feature should they use?
A data engineer wants to visually create and manage ETL jobs in AWS Glue. Which feature should they use?
What is a primary benefit of using the AWS Glue Spark runtime engine for processing ETL jobs?
What is a primary benefit of using the AWS Glue Spark runtime engine for processing ETL jobs?
How does AWS Glue support the orchestration of ETL tasks?
How does AWS Glue support the orchestration of ETL tasks?
Your organization needs to scale an AWS Glue job horizontally due to a large increase in data volume. What should you do?
Your organization needs to scale an AWS Glue job horizontally due to a large increase in data volume. What should you do?
Your organization's AWS Glue job is memory-intensive due to complex data transformations. Which scaling strategy should you implement?
Your organization's AWS Glue job is memory-intensive due to complex data transformations. Which scaling strategy should you implement?
Which AWS service is purpose-built for analyzing streaming data in real time?
Which AWS service is purpose-built for analyzing streaming data in real time?
In the context of Kinesis Data Streams, what does the Kinesis Producer Library (KPL) simplify?
In the context of Kinesis Data Streams, what does the Kinesis Producer Library (KPL) simplify?
In Amazon Kinesis Data Streams, how is data organized within the stream?
In Amazon Kinesis Data Streams, how is data organized within the stream?
What does a data record in Kinesis Data Streams include?
What does a data record in Kinesis Data Streams include?
Which AWS service can be used to deliver streaming data directly to storage locations such as Amazon S3 and Amazon Redshift?
Which AWS service can be used to deliver streaming data directly to storage locations such as Amazon S3 and Amazon Redshift?
A company needs to perform real-time analytics on data as it passes through a stream. Which AWS service should they use?
A company needs to perform real-time analytics on data as it passes through a stream. Which AWS service should they use?
What is the purpose of setting the retention period in Kinesis Data Streams?
What is the purpose of setting the retention period in Kinesis Data Streams?
When scaling Kinesis Data Streams, which factor determines the maximum write capacity?
When scaling Kinesis Data Streams, which factor determines the maximum write capacity?
Which service provides metrics to monitor how your Kinesis data stream handles the data being written to and read from it?
Which service provides metrics to monitor how your Kinesis data stream handles the data being written to and read from it?
What is a key component of the AWS IoT universe that connects devices to the physical world?
What is a key component of the AWS IoT universe that connects devices to the physical world?
Which communication model is commonly used with AWS IoT services to facilitate communication with IoT devices?
Which communication model is commonly used with AWS IoT services to facilitate communication with IoT devices?
A company wants to filter and transform data coming from IoT devices before routing it to other AWS services. Which AWS service should they use?
A company wants to filter and transform data coming from IoT devices before routing it to other AWS services. Which AWS service should they use?
What is the role of the rules engine in AWS IoT Core?
What is the role of the rules engine in AWS IoT Core?
A data engineer is creating a stream processing pipeline that needs to reformat incoming data from .csv to .json before delivering it to an S3 bucket, while minimizing the amount of coding required. Which service is most suitable?
A data engineer is creating a stream processing pipeline that needs to reformat incoming data from .csv to .json before delivering it to an S3 bucket, while minimizing the amount of coding required. Which service is most suitable?
If a company requires real-time processing and analysis of streaming data with capabilities for enriching and augmenting data across time windows, which service should they use?
If a company requires real-time processing and analysis of streaming data with capabilities for enriching and augmenting data across time windows, which service should they use?
What is a key advantage of using Amazon AppFlow for data ingestion?
What is a key advantage of using Amazon AppFlow for data ingestion?
When setting up a Kinesis data stream, which factor is crucial for influencing how producers distribute data records across shards?
When setting up a Kinesis data stream, which factor is crucial for influencing how producers distribute data records across shards?
What capability does AWS IoT Core provide to manage and protect information exchanged with IoT devices?
What capability does AWS IoT Core provide to manage and protect information exchanged with IoT devices?
In a real-time stream processing pipeline, what is the role of consumers?
In a real-time stream processing pipeline, what is the role of consumers?
What is the main advantage of loose coupling in stream ingestion?
What is the main advantage of loose coupling in stream ingestion?
When designing for stream ingestion and processing, what benefit do parallel consumers offer?
When designing for stream ingestion and processing, what benefit do parallel consumers offer?
Why is checkpointing and replay an important feature for stream ingestion and processing?
Why is checkpointing and replay an important feature for stream ingestion and processing?
What is a key activity performed by batch jobs in data ingestion?
What is a key activity performed by batch jobs in data ingestion?
Which of the following is a primary characteristic of stream processing?
Which of the following is a primary characteristic of stream processing?
A company requires near real-time analysis of user activity data as it is generated. Which ingestion method is most suitable?
A company requires near real-time analysis of user activity data as it is generated. Which ingestion method is most suitable?
What is the initial step in building a batch processing pipeline?
What is the initial step in building a batch processing pipeline?
Which characteristic is most important when handling large data volumes in batch processing?
Which characteristic is most important when handling large data volumes in batch processing?
What is the role of orchestration in batch processing pipelines?
What is the role of orchestration in batch processing pipelines?
Which AWS service is designed to ingest data from SaaS applications?
Which AWS service is designed to ingest data from SaaS applications?
To ingest data from relational databases which AWS service should be used?
To ingest data from relational databases which AWS service should be used?
Which feature of AWS Glue is primarily used for understanding the structure of data sources?
Which feature of AWS Glue is primarily used for understanding the structure of data sources?
Which component of AWS Glue is used to visually create, manage, and monitor ETL jobs?
Which component of AWS Glue is used to visually create, manage, and monitor ETL jobs?
When large files are processed in AWS Glue, the Spark runtime engine?
When large files are processed in AWS Glue, the Spark runtime engine?
Why is it important that stream ingestion pipelines are able to scale?
Why is it important that stream ingestion pipelines are able to scale?
What does Kinesis Data Streams use to uniquely organize data within the stream?
What does Kinesis Data Streams use to uniquely organize data within the stream?
For scaling Kinesis Data Streams, one needs to increase the number of shards. What impact would this have?
For scaling Kinesis Data Streams, one needs to increase the number of shards. What impact would this have?
What is a key benefit of using AWS IoT Core for data ingestion?
What is a key benefit of using AWS IoT Core for data ingestion?
Flashcards
Batch Ingestion
Batch Ingestion
Ingest and process records as a dataset on demand, schedule, or event.
Streaming Ingestion
Streaming Ingestion
Ingest and process sets of records as they arrive on the stream continuously.
Purpose-built Tools
Purpose-built Tools
Tools that matches of data to be ingested and simplifies the tasks involved in ingestion.
AWS Glue
AWS Glue
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AWS Glue Crawlers
AWS Glue Crawlers
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AWS Glue Studio
AWS Glue Studio
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AWS Glue Spark Runtime Engine
AWS Glue Spark Runtime Engine
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AWS Glue Workflows
AWS Glue Workflows
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Kinesis Producer Library
Kinesis Producer Library
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AWS IoT Core
AWS IoT Core
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AWS IoT Core Rules Engine
AWS IoT Core Rules Engine
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Amazon AppFlow
Amazon AppFlow
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AWS DMS
AWS DMS
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AWS DataSync
AWS DataSync
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AWS purpose-built tools benefits
AWS purpose-built tools benefits
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Ingestion tool
Ingestion tool
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tool used in streaming data
tool used in streaming data
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Amazon AppFlow
Amazon AppFlow
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AWS Data Exchange
AWS Data Exchange
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Amazon Data Firehose
Amazon Data Firehose
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Study Notes
- This module provides an overview of ingesting by batch or by stream
Module Objectives
- Identifies key tasks for data engineers building ingestion layers
- Describes how AWS services support ingestion tasks
- Illustrates automating batch ingestion with AWS Glue features
- Explains AWS streaming services
- Identifies configuration options in AWS Glue and Amazon Kinesis Data Streams
- Describes ingesting Internet of Things (IoT) data with AWS IoT Core
Batch and Stream Ingestion
- Batch ingestion processes records as a dataset on-demand, on a schedule, or based on an event
- Streaming ingestion processes records continually as they arrive
Data Volume and Velocity
- Data volume and velocity are primary drivers that determine which ingestion method to use
- Batch ingestion is suitable for sales transaction data from retailers, sent periodically with overnight analysis
- Streaming ingestion is suitable for clickstream data from a retailer's website that needs immediate analysis
Key Takeaways: Batch vs Stream
- Batch jobs query the source, transform the data, and load it into the pipeline
- Traditional ETL uses batch processing
- Stream processing involves producers putting records on a stream for consumers to process
- Streams deal with high-velocity data and real-time processing
Batch Processing Pipeline Tasks
- Extract: Connect to sources and select data
- Transform/Load: Identify source and target schemas, transfer and store data securely, and transform the dataset
- Load/Transform: Load the dataset to durable storage, orchestrating workflows with scripts and jobs
Key Characteristics of Batch Processing Design
- Batch processing should be flexible, offer low-code/no-code and serverless options
- It should handle large data volumes, support various sources, targets, and data formats
- It needs workflow creation, dependency management, bookmarking, job failure alerts, and logging
- It benefits from automatic scaling and pay-as-you-go options
Batch Ingestion Takeaways
- Batch ingestion uses scripts and jobs for ETL or ELT processes
- Workflow orchestration manages interdependencies and failures
- Pipeline design considers ease of use, data volume/variety, orchestration/monitoring, scaling, and cost management
AWS Purpose-Built Tools
- AWS provides purpose-built tools for different data sources
- Tools include SaaS apps, relational databases, file shares, and third-party datasets
- Features include secure connections, data store integration, automated updates, CloudWatch monitoring, selection, and transformation
Amazon AppFlow
- Use Amazon AppFlow to ingest data from a software as a service (SaaS) application
- Creates connectors with filters, maps fields, performs transformations, validates data, and securely transfers to Amazon S3 or Amazon Redshift
- Example: Ingest customer support ticket data from Zendesk
AWS Database Migration Service (DMS)
- Use AWS DMS to ingest data from relational databases
- Connects to source data, formats it for target, uses source filters, table mappings, validates data, writes to AWS data stores, and creates replication tasks
- Example: Ingest line of business transactions from an Oracle database
AWS DataSync
- Use DataSync to ingest data from file systems
- Applies filters to transfer files, uses various file systems, and transfers data between storage systems
- Example: Ingest on-premises genome sequencing data to Amazon S3
AWS Data Exchange
- Use AWS Data Exchange to integrate third-party datasets
- Finds and subscribes to sources, previews data, copies datasets to Amazon S3, and receives notifications
- Example: Ingest de-identified clinical data from a third party
Purpose-Built Ingestion Takeaways
- Choose the right tools to match data types and simplify ingestion tasks
- Amazon AppFlow, AWS DMS, and DataSync simplify certain data ingestion
- AWS Data Exchange simplifies finding and subscribing to third-party datasets
AWS Glue for Batch Processing
- AWS Glue simplifies batch ingestion tasks
- Integrates with data sources and storage solutions like Amazon Redshift and Amazon S3
- Features schema identification, data cataloging, job authoring/monitoring, serverless ETL, and orchestration
Schema Identification and Data Cataloging
- AWS Glue crawlers derive schemas and populate AWS Glue Data Catalog with metadata for ETL script generation
Job Authoring
- AWS Glue offers low-code job creation and management with a graphical interface, transformations, and monitoring
Serverless Job Processing
- AWS Glue uses Apache Spark, is fully managed and serverless, and optimizes queries across datasets
ETL Orchestration
- AWS Glue supports complex, multi-job ETL processing, tracks entities, and runs on a schedule
Monitoring and Troubleshooting AWS Glue Jobs
- Integration with CloudTrail and CloudWatch helps to monitor and troubleshoot AWS Glue jobs
AWS Glue Takeaways
- AWS Glue simplifies ETL tasks with centralized Data Catalog via crawlers
- AWS Glue Studio has visual authoring and job management
- AWS Glue Spark runtime engine is serverless
- AWS Glue offers ETL orchestration, integrated monitoring, and logging through CloudWatch
Scaling Considerations
- Horizontal scaling increases worker count
- Vertical scaling chooses larger worker types
- Focus on performance goals when scaling
- Splittable files allow parallel jobs with less overhead
Scaling Takeaways
- Performance goals drive batch scaling
- Scale AWS Glue horizontally by adding workers
- Scale AWS Glue vertically by choosing a larger worker
- Splittable files are run in parallel with less overhead
Stream Processing Tasks
- Extract: Input records to stream (Producers)
- Transform/Load: Secure durable storage, get records (Consumers), transform records
- Load/Transform: Analyze or store processed data
Characteristics of Stream Ingestion
- Plan for resilient, scalable streams that adapt to changing velocity and volume
- Build independent components
- Allow multiple consumers
- Maintain record order and support replay and failure marking
Purpose-Built Streaming Services
- Streaming data can be ingested and processed with the listed services.
- Amazon Data Firehose can transform and load data for future analysis while Amazon Managed Service for Apache Flink processes and analyzes data in real-time.
Kinesis Data Streams
- Data records are units of data with a sequence number, partition key, and data blob
- Partitions key determines which shard to use
Amazon Data Firehose
- Amazon Data Firehose can perform no-code and low-code streaming ETL
- Ingests from services, applies transformations, and delivers to data stores
Amazon Managed Service for Apache Flink
- Can query and analyze streaming data and build applications in Flink.
- Can ingest from other services and augment data across time windows.
Stream Processing Takeaways
- A stream is a buffer between producers and consumers
- The KPL simplifies writing producers
- Data is written to shards as a sequence of records containing a sequence number, partition key, and data blob
- Amazon Data Firehose delivers to storage
- Amazon Managed Service for Apache Flink is for real-time analytics
Kinesis Data Stream Scaling Configurations
- Streams can be setup with the configurations listed.
Monitoring Kinesis Data Streams
- API Actions can be tracked with cloud trail
- Track record age, throttling and write and read failures with Amazon Cloudwatch
Scaling Considerations for Stream Processing
- Kinesis Data Streams provides scaling to manage throughput
- Scale the write and storage capacity of the stream, throughput of each consumer
- CloudWatch monitors data written and read
IoT Ingestion
- IoT ecosystem consists of devices, interfaces, cloud services, and apps
- Devices are composed of Hardware, interfaces and communications
- Interfaces connect devices to the physical world
- Cloud services offer storage and processing
AWS IoT Core
- AWS IoT Core connects, processes, and acts on IoT data
- Filters and transforms data and routes to AWS services
AWS IoT Core Components
- Publishers: Send messages to AWS IoT Core
- Subscribers: IoT Core
- Rule Actions: Amazon Data Firehose, Amazon S3, Lambda, and DynamoDB
Rules Engine
- The rules engine routes and transforms data and allows different AWS services to ingest, process, and analyse the information.
IoT Ingestion Takeaways
- AWS IoT services use MQTT and a pub/sub model for communication
- Use AWS IoT Core to securely connect, process, and act on device data
- The AWS IoT Core rules engine transforms and routes messages to AWS services
Sample Exam Question
- Key words; Stream processing, reformat incoming data, delivering it to an S3 bucket, least amount of coding.
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