Data Engineering Powerhouse: The AWS Trio Solving Data Challenges

Overview

Analyzing customer needs and creating software that focuses on storing, transferring, converting, and organizing data for Analytics and Reporting purposes is known as data engineering.

An AWS Engineer examines the customer’s requirements, their data’s quantity and quality, and the outcomes of their activities. They also choose the greatest services and tools so that users may get the best results.

AWS Trio for Data Engineering solutions

• Amazon Redshift
• AWS Glue DataBrew
• Amazon SageMaker

Architecture flow for a proposed solution

Amazon Redshift for Data Warehousing Solution

AWS provides Redshift as a fully managed service. This suggests we don’t have to worry about cluster management, query processing over several nodes, or other low level Redshift chores. We can easily set up a cluster and start using data on the data warehouse. Data in  Amazon Redshift can be imported to AWS Glue DataBrew for data profiling. This can be done with the help of a JDBC connection.

Processing structured, semi-structured, and/or unstructured data is sometimes necessary to derive insights. Traditional business intelligence solutions cannot manage multiple data structures from various sources. In certain usage cases, Amazon Redshift is a powerful tool.

AWS Glue DataBrew for Profiling data

A new visual data preparation tool called AWS Glue AWS Glue DataBrew makes it simple for data scientists and analysts to clean and normalize data to prepare it for analytics and machine learning. You may automate data preparation chores without writing code by selecting from more than 100 pre-built transforms. You may automate operations like filtering anomalies, converting data to common formats, fixing incorrect values, and more. When your data is prepared, you can use it immediately for analytics and machine learning tasks. There is no upfront commitment; you only pay for what you use.

AWS Glue DataBrew can be used for profiling, transforming, and feature engineering. With the help of a connection from AWS Redshift, data can be bought in an AWS Glue DataBrew project. This data can then be manipulated as a mathematical model for data science solutions.

Amazon SageMaker for making Data Models

It is simple to iterate through data preparation workflows with AWS Glue DataBrew. The resulting jobs and recipes can be duplicated and applied to huge, distinct datasets. You may effortlessly prepare your data in context within your Jupyter notebook with the AWS Glue DataBrew Jupyter plugin.

The set of feature engineering steps that a data scientist has identified and performed on historical data over a given period will be applied to all new data after that period. The models trained from the historical feature have to predict the features obtained from the new data. Instead of manually performing these feature transformations on new data as new data arrives, data scientists can create a data preprocessing pipeline to perform a set of feature engineering steps. Expect to run whenever new raw data is available automatically.

Separating data engineering from data science in this way can be an effective time-saver when done properly.

Data engineering teams commonly use workflow orchestration tools like AWS Step Functions or Apache Airflow to create these extract, transform, and load (ETL) data pipelines. While these tools provide comprehensive and extensible options to support a wide range of data transformation workloads, data scientists may prefer to use a block-specific set of tools for ML workloads. Amazon SageMaker supports the end-to-end lifecycle of ML projects, including simplifying feature preparation with SageMaker Data Wrangler and feature storage and distribution with the SageMaker page Feature Store.

Conclusion

Date engineering tasks can be tiresome, and most of the time is spent creating a data set to overcome this problem. The solution architecture provided helps save time and effort in the process. The most important thing is due to the serverless architecture. They’re highly scalable and reliable you pay for what you use. Nevertheless, a cost is associated with each service which should be kept in mind while performing data engineering processes.

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