AWS Certified Associate Changed Data-Engineer-Associate Questions

The company is facing performance issues loading data into Amazon Redshift because it is issuing separate COPY commands for each data file location. The most efficient way to increase the speed of data ingestion into Redshift without increasing the cost is to use amanifest file.

Option D: Create a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.A manifest file provides a list of all the data files, allowing the COPY command to load all files in parallel from different locations in Amazon S3. This significantly improves the loading speed without adding costs, as it optimizes the data loading process in a single COPY operation.

Other options (A, B, C) involve additional steps that would either increase the cost (provisioning clusters, using Glue, etc.) or do not address the core issue of needing a unified and efficient COPY process.

References:

Amazon Redshift COPY Command

Redshift Manifest File Documentation

The ecommerce company wants to migrate its data pipelines into the AWS Cloud without managing servers, and the solution must orchestrate Python and Bash scripts without refactoring code.Amazon Managed Workflows for Apache Airflow (Amazon MWAA)is the most suitable solution for this scenario.

Option B: Amazon Managed Workflows for Apache Airflow (Amazon MWAA)MWAA is a managed orchestration service that supportsPython and Bash scriptsvia Directed Acyclic Graphs (DAGs) for workflows. It is a serverless, managed version of Apache Airflow, which is commonly used for orchestrating complex data workflows, making it an ideal choice for migrating existing pipelines without refactoring. It supports Python, Bash, and other scripting languages, and the company would not need to manage the underlying infrastructure.

Other options:

AWS Lambda (Option A)is more suited for event-driven workflows but would require breaking down the pipeline into individual Lambda functions, which may require refactoring.

AWS Step Functions (Option C)is good for orchestration but lacks native support for Python and Bash without using Lambda functions, and it may require code changes.

AWS Glue (Option D)is an ETL service primarily for data transformation and not suitable for orchestrating general scripts without modification.

References:

Amazon Managed Workflows for Apache Airflow (MWAA) Documentation

Problem Analysis:

The company usesKinesis Data Streamsfor both inventory management and reordering.

TheKinesis Producer Library (KPL)publishes data, and theKinesis Client Library (KCL)consumes data.

Duplicate records were observed in the inventory reordering system.

Key Considerations:

Kinesis streams are designed for durability but may produce duplicates under certain conditions.

Factors such asnetwork timeouts,shard splits, or changes inrecord processorscan cause duplication.

Solution Analysis:

Option A: Network-Related Timeouts

If the producer (KPL) experiences network timeouts, it retries data submission, potentially causing duplicates.

Option B: High IteratorAgeMilliseconds

High iterator age suggests delays in processing but does not directly cause duplication.

Option C: Changes in Shards or Processors

Changes in the number of shards or record processors can lead to re-processing of records, causing duplication.

Option D: AggregationEnabled Set to True

AggregationEnabled controls the aggregation of multiple records into one, but it does not cause duplication.

Option E: High max_records Value

A high max_records value increases batch size but does not lead to duplication.

Final Recommendation:

Network-related timeoutsandchanges in shards or processorsare the most likely causes of duplicate data in this scenario.

References:

Amazon Kinesis Data Streams Best Practices

Kinesis Producer Library (KPL) Overview

Kinesis Client Library (KCL) Overview

The performance issue in Amazon Redshift Spectrum queries arises due to the nature of CSV files, which are row-based storage formats. Spectrum is more optimized for columnar formats, which significantly improve performance by reducing the amount of data scanned. Also, partitioning data based on relevant columns like order date can further reduce the amount of data scanned, as queries can focus only on the necessary partitions.

A. Configure the third-party application to create the files in a columnar format:

Columnar formats(like Parquet or ORC) store data in a way that is optimized for analytical queries because they allow queries to scan only the columns required, rather than scanning all columns in a row-based format like CSV.

Amazon Redshift Spectrum works much more efficiently with columnar formats, reducing the amount of data that needs to be scanned, which improves query performance.