Streaming data ingestion refers to the process of importing data records directly and in real-time from sources such as transactions, event logs, and social media feed into a database or data warehouse. Such data is valuable because when analyzed, it can provide real-time insights into business operations.
In the context of the AWS Certified Data Engineer – Associate (DEA-C01) exam, it is crucial to understand how to design and implement AWS services for effective streaming data ingestion. AWS provides a number of tools that can facilitate this process, such as Amazon Kinesis, AWS Glue, Amazon Redshift, and Amazon S3.
Amazon Kinesis
Amazon Kinesis is a cloud-based service designed for real-time or near-real-time ingestion of large streams of data. Kinesis provides three applications to handle different types of streaming data:
- Amazon Kinesis Data Streams (KDS): KDS allows developers to build custom applications that process or analyze streaming data for specialized needs.
- Amazon Kinesis Data Firehose: It is the easiest way to reliably load streaming data into data lakes, data stores and analytics tools. It can capture, transform, and load streaming data into Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and more.
- Amazon Kinesis Data Analytics: This application enables to process and analyze streaming data using standard SQL.
AWS Glue
AWS Glue is another significant AWS service that aids in data ingestion. It is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load your data for analytics. AWS Glue can catalog your data, clean it, enrich it, and move it reliably between various data stores.
AWS Glue provides both visual and code-based interfaces to make data preparation and movement easy. Moreover, AWS Glue is serverless, so there’s no infrastructure to manage and you pay only for the compute resources you consume.
Example of Streaming Data Ingestion
One typical example of streaming data ingestion involves ingesting social media feed data into an Amazon Redshift data warehouse for real-time analysis:
- Amazon Kinesis Data Firehose collects the social media feed data and transmits it directly into an AWS Glue catalog.
- AWS Glue then cleans and transforms the data, preparing it for ingestion into Amazon Redshift’s columnar storage.
- Once the data is inside Amazon Redshift, analysts can use any SQL client to query the data and gain insights.
Here is an example of a high-level architecture that incorporates these components:
Conclusion
In conclusion, AWS provides a range of scalable, fully-managed streaming data ingestion services that can meet the needs of businesses of all sizes. Whether your data comes in the form of web clickstreams, logs from AWS services, or social media feeds, AWS has tools that can prepare and load it for real-time analytics. Understanding these tools and how to implement them is a key aspect of the AWS Certified Data Engineer – Associate (DEA-C01) exam.
Practice Test
True or False: Amazon Kinesis is a service used by AWS for handling real-time streaming data.
Answer: True
Explanation: Amazon Kinesis is indeed a platform within AWS used for handling real-time streaming data, making it effective for ingestion and processing.
What does stream processing do in Streaming data ingestion?
- a. It divides the data into batches before processing.
- b. It transforms data in real-time.
- c. It takes a significant amount of time to process data.
Answer: b. It transforms data in real-time.
Explanation: Stream processing in data ingestion works on a real-time basis, allowing immediate analysis and transformations on the streaming data.
Multiple Select: What AWS services are used for streaming data ingestion?
- a. Amazon Redshift
- b. Amazon Kinesis
- c. AWS Lambda
- d. AWS DynamoDB
Answer: b. Amazon Kinesis, c. AWS Lambda
Explanation: Amazon Kinesis is used for handling real-time streaming data, while AWS Lambda processes the ingested data. The other options specialize in different areas of AWS.
True or False: Streaming Data ingestion helps to collect, store and process real-time data.
Answer: True
Explanation: One of the main reasons to use Streaming Data ingestion is for its ability to handle real-time data, providing instant collection, storage, and processing.
In AWS, which Kinesis service is best used for analyzing video streams?
- a. Kinesis Data Streams
- b. Kinesis Video Streams
- c. Kinesis Data Analytics
- d. Kinesis Firehose
Answer: b. Kinesis Video Streams
Explanation: While all are Kinesis services, Kinesis Video Streams specifically offers capabilities for capturing, processing, and storing video streams.
What is the primary tool for real-time analytics in AWS?
- a. Amazon Redshift
- b. Amazon Aurora
- c. Amazon Quicksight
- d. Kinesis Data Analytics
Answer: d. Kinesis Data Analytics
Explanation: Kinesis Data Analytics is specifically designed to perform real-time analytics on streaming data.
True or False: AWS data streams cannot process continuous data capture (CDC).
Answer: False
Explanation: With Kinesis Data Streams, you can store and process continuous data capture (CDC).
Single Select: In which process does AWS Lambda fit during streaming data ingestion?
- a. Data Storing
- b. Data Collection
- c. Data Processing
- d. Data Archiving
Answer: c. Data Processing
Explanation: AWS Lambda comes in the data processing stage during streaming data ingestion as it’s capable of executing your code in response to events.
True or False: AWS Fargate is recommended for applying transformations to streaming data.
Answer: False
Explanation: AWS Fargate is a compute engine and not mainly used for transforming streaming data. Services such as AWS Lambda and Kinesis Data Analytics are recommended for this purpose.
Single Select: When would you use Kinesis Firehose over Kinesis Data Streams?
- a. When you need to process data before it’s delivered.
- b. When you need to store video streams.
- c. When you’re sending data to a destination without processing.
- d. When you’re creating a machine learning model.
Answer: c. When you’re sending data to a destination without processing.
Explanation: Kinesis Firehose is ideal when you need to deliver data to a destination without needing to process it first. If processing is needed, Kinesis Data Streams would be more appropriate.
Interview Questions
What is Streaming Data ingestion in AWS?
Streaming data ingestion is the process of importing real-time data into AWS as it is generated by different sources. Services like Amazon Kinesis and AWS Glue are used to ingest, process, and analyze streaming data.
What are the core components of Amazon Kinesis for streaming data ingestion?
The core components of Amazon Kinesis include Kinesis Video Streams, Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics.
What role does Amazon Kinesis Data Streams play in streaming data ingestion?
Amazon Kinesis Data Streams is a service designed to handle real-time streaming of big data. It can continuously capture gigabytes of data per second from hundreds of thousands of sources such as website clickstreams, database event streams, financial transactions, social media feeds, IT logs, and location-tracking events.
What is the use of AWS Glue in streaming data ingestion?
AWS Glue can catalog streaming data, making it available for queries in real-time. It can discover both the schema and the schema changes in the incoming data, storing the captured schemas in the AWS Glue Data Catalog.
How does Amazon Kinesis Data Firehose manage data ingestion?
Amazon 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 load streaming data into Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk, enabling near real-time analytics with existing business intelligence tools.
What is the difference between Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose?
Amazon Kinesis Data Streams involves manual intervention for data processing and requires setting up applications to process or analyze the streaming data. However, Amazon Kinesis Data Firehose is fully automated—no need to write applications or manage resources.
What role does AWS Lambda play in streaming data ingestion?
AWS Lambda functions can be used to process data from Kinesis data streams and Kinesis Data Firehose. With Lambda, you only need to manage your code, not the underlying infrastructure.
How does streaming data ingestion contribute to real-time analytics in AWS?
Streaming data ingestion enables real-time analytics by capturing and processing live data for immediate insights, trends, and patterns. It feeds real-time applications that are able to update and display live information without any delay.
What is the role of Apache Flink in AWS for streaming data ingestion?
Apache Flink is integrated with Amazon Kinesis Data Analytics. It lets you process and analyze streaming data with a more sophisticated algorithm, complex event processing, and machine learning.
How does Amazon Kinesis Data Analytics process streaming data?
Amazon Kinesis Data Analytics processes streaming data with standard SQL. It allows you to query streaming data, join streams together, and perform windowed aggregations on your streaming data.
Can you directly ingest data from Amazon Kinesis into AWS Glue?
No, AWS Glue does not natively support Amazon Kinesis as a data source. However, Kinesis Data Firehose can load data into Amazon S3, which can be cataloged and transformed by AWS Glue.
What is the importance of buffering in streaming data ingestion?
Buffering is used to handle load during times of high data activity or burst input in terms of incoming data rate. It ensures the smooth functioning of the stream and that no data is lost during peak times.
What are shards in Amazon Kinesis Data Streams?
In Amazon Kinesis Data Streams, a shard is a base throughput unit. A stream is composed of one or more shards, and each shard can support five transactions per second for reads, up to a maximum total data read rate of 2 MB per second.
How does AWS manage the durability of streaming data?
AWS ensures durability of streaming data by synchronously replicating it across three separate facilities within an AWS region.
What type of data does Amazon Kinesis effectively process?
Amazon Kinesis is capable of effectively processing structured and semi-structured data such as log data, event data, and IoT telemetry data.
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