{"id":98870,"date":"2025-03-17T12:19:32","date_gmt":"2025-03-17T06:49:32","guid":{"rendered":"https:\/\/www.whizlabs.com\/blog\/?p=98870"},"modified":"2025-03-17T12:19:32","modified_gmt":"2025-03-17T06:49:32","slug":"etl-best-practices-for-aws-data-engineers","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/etl-best-practices-for-aws-data-engineers\/","title":{"rendered":"What Are ETL Best Practices for AWS Data Engineers"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">In AWS data engineering, Extract, Transform, and Load (ETL) processes are pivotal, as they allow you to prepare raw data sets for analytical purposes. This blog provides a detailed exploration of data engineering best practices specifically geared toward optimising ETL workflows, enhanced with relevant keywords and concepts for <\/span><strong><a title=\"AWS Certified Data Engineer Associate Certification (DEA-C01)\" href=\"https:\/\/www.whizlabs.com\/aws-certified-data-engineer-certification-exam\/\" target=\"_blank\" rel=\"noopener\">AWS Certified Data Engineer Associate Certification (DEA-C01)<\/a><\/strong><span style=\"font-weight: 400;\">.<\/span><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_76 ez-toc-wrap-left counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #ea7e02;color:#ea7e02\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #ea7e02;color:#ea7e02\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.whizlabs.com\/blog\/etl-best-practices-for-aws-data-engineers\/#The_ETL_Process\" >The ETL Process<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.whizlabs.com\/blog\/etl-best-practices-for-aws-data-engineers\/#ETL_Best_Practices_for_AWS_Data_Engineers\" >ETL Best Practices for AWS Data Engineers\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.whizlabs.com\/blog\/etl-best-practices-for-aws-data-engineers\/#Best_Tools_for_AWS_ETL\" >Best Tools for AWS ETL\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.whizlabs.com\/blog\/etl-best-practices-for-aws-data-engineers\/#ETL_Optimization\" >ETL Optimization\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.whizlabs.com\/blog\/etl-best-practices-for-aws-data-engineers\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"The_ETL_Process\"><\/span><b>The ETL Process<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">ETL is the combination of data from multiple sources into a large central repository called a data warehouse. It uses a set of business rules to clean and organise and prepare raw data for such activities as storage, analytics, and machine learning (ML). It provides a consolidated view of data for in-depth analysis and reporting, leading to more accurate data analysis to meet compliance and regulatory standards. The ETL process in AWS works as shown in the diagram and explained below.<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-98889 size-full\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/the-etl-proces-in-aws.webp\" alt=\"the etl process in aws\" width=\"1536\" height=\"686\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/the-etl-proces-in-aws.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/the-etl-proces-in-aws-300x134.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/the-etl-proces-in-aws-1024x457.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/the-etl-proces-in-aws-768x343.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/the-etl-proces-in-aws-150x67.webp 150w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data extraction:<\/b><span style=\"font-weight: 400;\"> In data extraction, extract, transform, and load tools extract or copy raw data from its multiple source database and store it in a staging area. A staging area, or landing zone, is an intermediate storage area for temporarily holding the extracted data. This phase also opens AWS connections to source systems, such as databases, APIs, or flat files and extracting the required information.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data transformation:<\/b><span style=\"font-weight: 400;\"> ETL tools transform and consolidate the raw data in the staging area in a manner that is suited for analysis. The data transformation phase can involve a variety of types of data changes. The general rule is that before AWS DATA is subjected for analysis, it must be cleaned, transformed, and enriched.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data loading<\/b><span style=\"font-weight: 400;\">: During this stage, ETL tools move the transformed data from the staging area into the target data warehouse. The process of data loading in AWS is usually automated and continuous with the transformed data ingested into an AWS central data repository, such as a data warehouse or data lake.<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2><span class=\"ez-toc-section\" id=\"ETL_Best_Practices_for_AWS_Data_Engineers\"><\/span><b>ETL Best Practices for AWS Data Engineers\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">AWS Certified Data Engineer Associate Certification (DEA-C01) candidates\u00a0 should follow the following best practices for effective ETL processes;\u00a0<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-98876 size-full\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/best-practices-for-effective-etl-processes.webp\" alt=\"etl best practices for aws data engineers \" width=\"1536\" height=\"443\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/best-practices-for-effective-etl-processes.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/best-practices-for-effective-etl-processes-300x87.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/best-practices-for-effective-etl-processes-1024x295.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/best-practices-for-effective-etl-processes-768x222.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/best-practices-for-effective-etl-processes-150x43.webp 150w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/><\/p>\n<ul>\n<li><b>Plan before you build: <\/b><span style=\"font-weight: 400;\">The first step is to develop a clear understanding of its purpose by developing a solid plan. A well-defined plan ensures your workflow meets specific needs. As a candidate for the AWS Certified Data Engineer Associate certification, you need to set clear objectives to help guide design decisions as this will prevent\u00a0 scope creep during the ETL process. You can use AWS data flow diagrams and lineage to visualize how data moves through the ETL process during the planning process.\u00a0<\/span><\/li>\n<li><b>Use scalable data processing: <\/b><span style=\"font-weight: 400;\">When handling big data solutions, it is advisable to use scalable distributed processing frameworks, such as Apache Spark. In addition, use the Concurrency Scaling feature in Amazon Redshift to automatically handle spikes in concurrent read and write query workloads thereby improving performance.\u00a0<\/span><\/li>\n<li><b>Maintain clear documentation and lineage: <\/b><span style=\"font-weight: 400;\">You should also maintain<\/span> <span style=\"font-weight: 400;\">clear documentation and data lineage for transparency and troubleshooting of processed AWS ETL and providing\u00a0 support for audits. Integrate solutions such as OpenMetadata and OpenLineage, to assist in the documentation and visualization of data flows across the AWS ETL pipelines.\u00a0<\/span><\/li>\n<li><b>Use bulk loading techniques: The application of <\/b><span style=\"font-weight: 400;\">\u00a0bulk loading and partitioning techniques during the load phase assist in minimizing ingestion times in AWL ETL processes and maximize target system performance. Bulk loading techniques offer maximum performance benefits during the load phase, ensuring efficient ingestion of transformed and enriched data into central repositories.<\/span><\/li>\n<li><b>Ensure <a title=\"data Security and compliance\" href=\"https:\/\/docs.aws.amazon.com\/whitepapers\/latest\/aws-overview\/security-and-compliance.html\" target=\"_blank\" rel=\"nofollow noopener\">data Security and compliance<\/a>: <\/b><span style=\"font-weight: 400;\">As a data engineer you should implement robust security measures to protect your data throughout the ETL process. Deploy technologies such as encryption, access controls, and other security features provided by AWS to safeguard your data while complying with data protection regulations and standards especially in those environments governed by data engineering best practices.<\/span><\/li>\n<li><b>Implement robust real-time monitoring <\/b><span style=\"font-weight: 400;\">Implement robust monitoring and logging mechanisms are critical for tracking ETL processes&#8217; performance and health, facilitating quick issue resolution. Regular monitoring of AWS ETL pipelines assists in improving data reliability and accuracy.\u00a0<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Best_Tools_for_AWS_ETL\"><\/span><b>Best Tools for AWS ETL\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">It is important to use the right solution functionalities in AWS, as this can lead to efficiency and effectiveness gains. The major capabilities include <\/span><a title=\"AWS Glue\" href=\"https:\/\/www.whizlabs.com\/blog\/what-is-aws-glue\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\"><strong>AWS Glue<\/strong><\/span><\/a><span style=\"font-weight: 400;\"> and Amazon Redshift, as shown in the solution architecture and explained below.<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-98875 size-full\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/the-major-capabilities-include-aws-glue-and-amazon-redshift.webp\" alt=\"best tools for aws etl\u00a0\" width=\"1536\" height=\"1096\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/the-major-capabilities-include-aws-glue-and-amazon-redshift.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/the-major-capabilities-include-aws-glue-and-amazon-redshift-300x214.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/the-major-capabilities-include-aws-glue-and-amazon-redshift-1024x731.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/the-major-capabilities-include-aws-glue-and-amazon-redshift-768x548.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/the-major-capabilities-include-aws-glue-and-amazon-redshift-150x107.webp 150w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>AWS Glue: <\/b><span style=\"font-weight: 400;\">This is serverless data integration service that allows you to easily discover, prepare, move, and integrate data from multiple sources for analytics, machine learning, and application development. It allows you to discover and connect to 80+ diverse data stores that can be managed in a centralized\u00a0data catalog.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>AWS Glue Studio:<\/b><span style=\"font-weight: 400;\"> AWS data engineers can also use AWS Glue Studio to create, run, and monitor ETL pipelines that are used to load data into data lakes. They should also understand the AWS data pipeline vs. AWS Glue proposes that the AWS data pipeline focuses on designing data workflows while AWS Glue focuses more on managing ETL tasks.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Amazon Redshift<\/b><span style=\"font-weight: 400;\">: Amazon Redshift is a fast, petabyte-scale AWS data warehouse used to formulate data-driven decisions with relative ease. The functionality also allows data engineers to set up any type of data model and to query data directly from Amazon S3 without loading it into the data warehouse.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Amazon Managed Workflows for Apache Airflow (MWAA)<\/b><span style=\"font-weight: 400;\">: The MWAA functionality provides a graphical user interface (GUI) functionality that is used to schedule and monitor batch AWS ETL workflows. It consists of a variety of features including retry mechanisms and alerting systems, which can allow manual intervention in. Note also that nowadays AI and ML are also increasingly being integrated to automate and optimize data transformations.<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2><span class=\"ez-toc-section\" id=\"ETL_Optimization\"><\/span><b>ETL Optimization\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">There are various ways in which AWS Certified Data Engineer Associate Certification holders can ensure ETL optimization including the following;<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-98877 size-full\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/etl-optimization-in-aws.webp\" alt=\"etl optimization in aws \" width=\"1536\" height=\"813\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/etl-optimization-in-aws.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/etl-optimization-in-aws-300x159.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/etl-optimization-in-aws-1024x542.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/etl-optimization-in-aws-768x407.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/03\/etl-optimization-in-aws-150x79.webp 150w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/><\/p>\n<ul>\n<li aria-level=\"1\"><b>Maximize data quality: <\/b><span style=\"font-weight: 400;\">The old saying \u201cgarbage in, garbage out\u201d also applies to ETL integration. You need to ensure that your feed into your processes is as clean as possible for fast and predictable results. You can deploy automated data quality tools that can help with this task by finding crucial aspects like missing and inconsistent data within your data sets.\u00a0<\/span><\/li>\n<li aria-level=\"1\"><b>Minimize data input:<span style=\"font-weight: 400;\">As a data engineer sitting for the <\/span><\/b><a title=\"AWS Certified Data Engineer Associate Certification (DEA-C01)\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-data-engineer-associate-certification\/\" target=\"_blank\" rel=\"noopener\"><strong>AWS Certified Data Engineer Associate Certification (DEA-C01)<\/strong><\/a><b><span style=\"font-weight: 400;\"> exam, you should understand that the fewer data that you have going into the AWS ETL process, the faster and cleaner your results are likely to be. Therefore, you need to remove any unnecessary data as early in the ETL process as possible. This includes cleaning redundant entries in a database before the ETL process starts and not wasting valuable time transforming unneeded data. <\/span><\/b><\/li>\n<li aria-level=\"1\"><b>Use Incremental Loading:<\/b><span style=\"font-weight: 400;\"> This process involved the extraction of only that data that has undergone changes since the last extraction<\/span><span style=\"font-weight: 400;\">. This reduced the load on the source systems and speeded up the ETL process. Using incremental data updates means that when your data sets are updated, you add only the new data into your ETL pipeline. This also saves resources by updating only new or changed records instead of reprocessing the entire data set, allowing you to avoid replacing all the existing data and starting again from scratch.\u00a0<\/span><\/li>\n<li aria-level=\"1\"><b>Optimize Memory Management:<\/b><span style=\"font-weight: 400;\"> Optimizing memory management is vital when writing AWS Glue ETL jobs. Jobs run on Apache Spark are optimized for in-memory processing. You also need to apply efficient memory utilization processes to ensure smooth operation without unexpected failures. You can also perform data caching, keeping recently used data in memory or on disks where it can be accessed again quickly. This is an easy-to-implement method for speeding AWS ETL processes.\u00a0<\/span><\/li>\n<li aria-level=\"1\"><b>Leverage Parallel Processing:<\/b><span style=\"font-weight: 400;\"> Parallel processing involves running ETL processes in parallel on multiple partitions.. This is very useful for large datasets, which are typical of AWS data engineering environments as it improves efficiency. Modern parallel processing tools can run multiple tasks simultaneously, improving data processing speeds and reducing bottlenecks. It should be noted that no efficient ETL processes should be serial. Instead, time-to-value is minimised by leveraging parallel processing as much as your AWS infrastructure allows.<\/span><\/li>\n<li aria-level=\"1\"><b>Use Partitioning to Improve Query Performance: <\/b><span style=\"font-weight: 400;\">Partitioning divides a large dataset into smaller partitions based on specific columns or keys. AWS Glue can perform selective scans on subsets of data, enhancing query performance. You can use the leverage prefix-based partitioning to apply the Amazon Redshift Spectrum\u2019s partition pruning capabilities<\/span><span style=\"font-weight: 400;\">. This process helps in optimizing performance by partitioning your data while skipping unneeded partitions.<\/span><\/li>\n<li aria-level=\"1\"><b>Use Workload Management to Improve ETL Runtimes: <\/b><span style=\"font-weight: 400;\">You therefore need to enable automatic WLM maximizes throughput and resource utilization, helping manage distinct workloads. Use automatic WLM to dynamically adjust query concurrency and memory allocation based on the resource requirements of the current AWS thus enhancing the overall performance of the AWS ETL processes.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Use Automatic Table Optimization (ATO):<\/b><span style=\"font-weight: 400;\"> ATO is a self-tuning functionality in Amazon Redshift that\u00a0 automatically optimizes table designs. It achieves this through various methods including by applying sort, multidimensional data layout sorting, and distribution keys. It continuously observes your queries and uses AI-powered methods to choose the optimal keys that maximize performance for your cluster\u2019s specific workload. ATO helps maintain table performance by automatically optimizing them based on usage patterns, reducing manual intervention required.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Maximize the benefits of materialized views:\u00a0 <\/b><span style=\"font-weight: 400;\">Materialized views in Amazon Redshift precompute and store complex query results, significantly improving ETL processes&#8217; performance by reducing recomputation needs. This has the effect of boosting\u00a0 performance for complex or frequently accessed analytical queries such as diligence (BI) dashboards and ELT workloads resulting in low latency for analytical queries.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Perform multiple steps in a single transaction: <\/b><span style=\"font-weight: 400;\">Performing multiple steps in a single transaction maintains data consistency and integrity, ensuring all steps are completed successfully before committing changes. As the transformation logic often spans multiple steps minimizing the number of commits in a process is necessary to ensure that each single commitment is performed only after all the transformation logic in the ETL processes has successfully executed.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Use UNLOAD to extract large result sets:<\/b><span style=\"font-weight: 400;\"> When dealing with large result sets, the UNLOAD command efficiently extracts data being, managing large volumes effectively and ensuring fast and reliable extraction. Because fetching many rows in AWS data extraction is expensive and time-consuming, the UNLOAD command helps in reducing THE elapsed time during the extraction processes therefore enhancing performance.\u00a0<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><b>Conclusion<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">This blog has revealed the best practices that should be observed by holders of the AWS Certified Data Engineer Associate Certification (DEA-C01) in optimizing the ETL processes. By adhering to best practices and proven principles, you are better placed as an AWS data engineer to ensure that all ETL processes are optimized for performance. This leads to the delivery of accurate results as well as scalable and high-performing data pipelines. The blog is also crucial in assisting your AWS data engineering certification preparation with <\/span><a title=\"Hands-on labs\" href=\"https:\/\/www.whizlabs.com\/hands-on-labs\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\"><strong>Hands-on labs<\/strong><\/span><\/a><span style=\"font-weight: 400;\"> and <\/span><a title=\"sandboxes\" href=\"https:\/\/www.whizlabs.com\/cloud-sandbox\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\"><strong>sandboxes<\/strong><\/span><\/a><span style=\"font-weight: 400;\">. Talk to our experts in case of queries!<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In AWS data engineering, Extract, Transform, and Load (ETL) processes are pivotal, as they allow you to prepare raw data sets for analytical purposes. This blog provides a detailed exploration of data engineering best practices specifically geared toward optimising ETL workflows, enhanced with relevant keywords and concepts for AWS Certified Data Engineer Associate Certification (DEA-C01). The ETL Process ETL is the combination of data from multiple sources into a large central repository called a data warehouse. It uses a set of business rules to clean and organise and prepare raw data for such activities as storage, analytics, and machine learning 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