{"id":99272,"date":"2025-04-25T16:38:19","date_gmt":"2025-04-25T11:08:19","guid":{"rendered":"https:\/\/www.whizlabs.com\/blog\/?p=99272"},"modified":"2025-04-25T16:54:28","modified_gmt":"2025-04-25T11:24:28","slug":"how-does-handle-aws-big-data-ml","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/how-does-handle-aws-big-data-ml\/","title":{"rendered":"How Does AWS Handle Big Data for ML?"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">In this blog, we will discover the abilities of AWS to handle big data for ML.\u00a0 Maintaining high-quality data and records comes with a high level of challenges, but the<\/span> <span style=\"font-weight: 400;\">AWS tools for processing and analysing big data come in handy. Let us explore how AWS achieves this!<\/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\/how-does-handle-aws-big-data-ml\/#Major_Challenges_In_Handling_Big_Data_For_Businesses\" >Major Challenges In Handling Big Data For\u00a0 Businesses<\/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\/how-does-handle-aws-big-data-ml\/#AWS_Solving_Big_Data_Challenges_For_ML\" >AWS Solving Big Data Challenges For ML\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\/how-does-handle-aws-big-data-ml\/#AWS_Cloud_Storage_Tools_For_Big_Data\" >AWS Cloud Storage Tools For Big Data<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.whizlabs.com\/blog\/how-does-handle-aws-big-data-ml\/#For_Data_Storage\" >For Data Storage\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.whizlabs.com\/blog\/how-does-handle-aws-big-data-ml\/#Data_Processing_and_Transformation\" >Data Processing and Transformation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.whizlabs.com\/blog\/how-does-handle-aws-big-data-ml\/#Model_Training_and_Deployment\" >Model Training and Deployment<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.whizlabs.com\/blog\/how-does-handle-aws-big-data-ml\/#Data_Integration\" >Data Integration\u00a0<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.whizlabs.com\/blog\/how-does-handle-aws-big-data-ml\/#AWS_Governance_Monitoring_for_Big_Data\" >AWS Governance &amp; Monitoring for Big Data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.whizlabs.com\/blog\/how-does-handle-aws-big-data-ml\/#Best_Practices_to_Manage_Big_Data_with_MLA-C01\" >Best Practices to Manage Big Data with MLA-C01<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.whizlabs.com\/blog\/how-does-handle-aws-big-data-ml\/#To_Sum_Up\" >To Sum Up\u00a0<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Major_Challenges_In_Handling_Big_Data_For_Businesses\"><\/span><b>Major Challenges In Handling Big Data For\u00a0 Businesses<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Today, businesses are moving towards data-driven decision-making. From analysing customer interactions and transactions to IoT sensor data and Social media analysis, data is the starting point to extract meaningful insights. The main challenges faced in handling b<\/span>ig data<span style=\"font-weight: 400;\"> are\u00a0<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Storing and retrieving data without performance issues that affect scalability.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Raw data is formatted in the ML model during transformation.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Training the ML Model involves a significant power source and optimisation.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Protecting sensitive data while complying with regulatory requirements.\u00a0<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">Here is where machine learning makes its entry. Handling well-structured big data is the biggest struggle for any organisation. The absence of the right ML model infrastructure challenges will completely affect the efficiency and innovation of ML adaptation, making it difficult for businesses.\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"AWS_Solving_Big_Data_Challenges_For_ML\"><\/span><b>AWS Solving Big Data Challenges For ML\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Amazon Web Services offers a fully managed, scalable, and cost-efficient cloud ecosystem for Organizations and businesses to securely take care of their storage and operational functions. The whole ecosystem is built to simplify my ML application handling data with its different tools like Amazon S3, AWS Glue, AWS EMR, Amazon SageMaker and others that streamline data storage, processes and training models. This allows businesses to gather and analyse insights effectively rather than managing the infrastructure over and over.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Apart from different and dedicated AWS certifications, opting for<\/span> <span style=\"font-weight: 400;\">the<\/span> <a title=\"AWS Certified Machine Learning Associate certification - MLA-C01\" href=\"https:\/\/www.whizlabs.com\/aws-certified-machine-learning-engineer-associate\/\" target=\"_blank\" rel=\"noopener\"><b>AWS Certified Machine Learning Associate certification &#8211; MLA-C01<\/b> <\/a><span style=\"font-weight: 400;\">\u00a0helps learners master cloud tech and advance their career in cloud computing-based ML models. Eventually, there are definite opportunities to understand how to handle Big Data for Machine Learning<\/span><b>.<\/b><span style=\"font-weight: 400;\"> It validates the skills in implementing and managing machine learning workload, especially on AWS. From data preparation and feature engineering to model training and deployment, professionals excel in it.\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"AWS_Cloud_Storage_Tools_For_Big_Data\"><\/span><b>AWS Cloud Storage Tools For Big Data<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">AWS Cloud Storage for Big Data offers an array of tools and services specifically designed to address storage, processing, and transformation, and it also contributes to machine learning workflows. Here is a detailed categorisation of AWS storage for Big data and its functionalities.\u00a0<\/span><b><\/b><\/p>\n<h3><span class=\"ez-toc-section\" id=\"For_Data_Storage\"><\/span><b>For Data Storage\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-99281\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-storage.webp\" alt=\"aws cloud storage for big data storage\" width=\"1536\" height=\"417\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-storage.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-storage-300x81.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-storage-1024x278.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-storage-768x209.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-storage-150x41.webp 150w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/><\/p>\n<p><b>\u00a0 \u00a0* Amazon S3 (Simple Storage Service)<br \/>\n<\/b><span style=\"font-weight: 400;\">The <\/span><a style=\"font-size: 16px; background-color: #ffffff;\" title=\"Amazon S3\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-s3-data-security\/\" target=\"_blank\" rel=\"noopener\"><b>Amazon S3<\/b> <\/a><span style=\"font-weight: 400;\">is the backbone for data lakes, which offers virtually unlimited storage with scalability and high durability (99.99999999%). It can store structured, semi-structured, and unstructured data in its very own native format. The tool is an ideal choice for building centralized data lakes, and it stores raw and processed big data. <\/span><span style=\"font-size: 16px; font-weight: 400;\">It can accept multiple storage classes, contributing towards cost optimisation based on Access frequency. Lifecycle management policies transition data between storage tiers. And it can innately integrate with analytical tools like Amazon Athena and Redshift Spectrum for querying data from S3.<\/span><\/p>\n<p><b>\u00a0 * Amazon Redshift<br \/>\n<\/b><b><\/b><b><span style=\"font-size: 16px; font-weight: 400;\">It&#8217;s a fully managed, scalable cloud data warehouse that is designed for running complex SQL queries on larger datasets. This is best suited for analytical workloads that require high performance and scalability.\u00a0<\/span><\/b><span style=\"font-weight: 400;\">Redshift uses massively parallel processing to enable fast query execution. Its Columnar storage is efficient in compressing and retrieving. It is a perfect tool to integrate with Amazon SageMaker, building and training ML models directly from the warehouse.<\/span><\/p>\n<p><b>\u00a0* AWS Lake Formation<br \/>\n<\/b><span style=\"font-weight: 400;\">AWS Lake Formation is an easy-to-set-up service to secure data lakes as it collects and catalogues data from different sources into Amazon S3. It automates schema discovery and metadata management and centralises security policies to control access. This simplifies the creation of secure and scalable data lakes.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Data_Processing_and_Transformation\"><\/span><b>Data Processing and Transformation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-99282\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-processing-and-transformation.webp\" alt=\"aws cloud storage for big data processing and transformation\" width=\"1536\" height=\"417\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-processing-and-transformation.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-processing-and-transformation-300x81.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-processing-and-transformation-1024x278.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-processing-and-transformation-768x209.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-processing-and-transformation-150x41.webp 150w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/><\/p>\n<p><b>* AWS Glue<br \/>\n<\/b><a title=\"AWS Glue\" href=\"https:\/\/www.whizlabs.com\/blog\/what-is-aws-glue\/\" target=\"_blank\" rel=\"noopener\"><b>AWS Glue<\/b><\/a><span style=\"font-weight: 400;\"> is a serverless ETL (Extract, Transform, Load) service that prepares and transforms data for analytics and machine learning. It can automate ETL workflow and prepare big data for analytics and ML pipelines. The Built-in Data catalogue manages metadata and its integration with Apache Spark distributes data processing. It also supports both batch and streaming ETL jobs.\u00a0<\/span><b><\/b><\/p>\n<p><b>* Amazon EMR (Elastic MapReduce)<br \/>\n<\/b><span style=\"font-weight: 400;\">This is a managed Hadoop Framework that allows large dataset processing in a distributed format using open-source tools like Spark, Hive and Presto. Has Dynamic scaling that cluster-based workload needs and is very seamless to integrate with S3 for data access. This is an ideal tool to run large-scale distributed computations.\u00a0<\/span><\/p>\n<p><b>* AWS Data Pipeline<br \/>\n<\/b><span style=\"font-weight: 400;\">This is a web service that automates the movement and transformation of data between AWS services and on-premises systems. The customizable workflow makes the retry mechanism easy, and it&#8217;s easy to integrate with Redshift and Dynamodb-like services. This can automate recurring data workflows like backups and transformations.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Model_Training_and_Deployment\"><\/span><b>Model Training and Deployment<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-99283\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-model-training-and-deployment.webp\" alt=\"aws cloud storage for big data model training and deployment\" width=\"1536\" height=\"417\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-model-training-and-deployment.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-model-training-and-deployment-300x81.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-model-training-and-deployment-1024x278.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-model-training-and-deployment-768x209.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-model-training-and-deployment-150x41.webp 150w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/><\/p>\n<p><b>* Amazon SageMaker<br \/>\n<\/b><span style=\"font-weight: 400;\">Complete service manager simplifying building, training, tuning and deploying machine learning models at scale. The <\/span><a title=\"SageMaker\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-sagemaker\/\" target=\"_blank\" rel=\"noopener\"><b>Amazon SageMaker<\/b><\/a><span style=\"font-weight: 400;\"> has a built-in algorithm to optimize big data training; it can manage Jupyter notebooks for experimentation and integrates S3, Glue and Redshift like Amazon services seamlessly. All of it makes sense to go to end-to-end ML workflow management.<\/span><b><\/b><\/p>\n<p><strong>* <\/strong><b>AWS Lambda<br \/>\n<\/b><span style=\"font-weight: 400;\">This is a serverless computer service provider that runs code in response to events without providing servers. AWS Lambda has an event-driven trigger for ML inferences with the potential to handle millions of requests per second. It supports real-time inference and processing in ML pipelines.\u00a0<\/span><b><\/b><\/p>\n<p><strong>*<\/strong> <b>Amazon EC2 (Elastic Compute Cloud)<br \/>\n<\/b><span style=\"font-weight: 400;\">The Amazon EC2 provides resizable compute capacity in the cloud to run custom ML models. It can train a wide range of instance types to be optimised for ML training, and it is flexible to install custom frameworks and libraries. It is mainly used in high-performing training jobs that require specialised hardware like GPU and others.<br \/>\n<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Data_Integration\"><\/span><b>Data Integration\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-99284\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-integration.webp\" alt=\"aws cloud storage for big data integration\" width=\"1536\" height=\"417\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-integration.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-integration-300x81.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-integration-1024x278.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-integration-768x209.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/04\/aws-cloud-storage-for-big-data-integration-150x41.webp 150w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/><\/p>\n<p><strong>* <\/strong><b>Amazon Kinesis<br \/>\n<\/b><span style=\"font-weight: 400;\">With the ability to stream services for high-velocity data, <\/span><a title=\"Amazon Kinesis\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-kinesis-use-cases\/\" target=\"_blank\" rel=\"noopener\"><b>Amazon Kinesis<\/b><\/a><span style=\"font-weight: 400;\"> handles use cases like log analysis, IoT telemetry, event tracking and more. It can also scale automatically with the ability to accommodate varying workloads.\u00a0<\/span><\/p>\n<p><strong>*<\/strong><b> AWS Data Migration Service<br \/>\n<\/b><span style=\"font-weight: 400;\">AWS data migration services simplify database migration to AWS with minimal downtime. It also supports heterogeneous migration between different database engines conveniently.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Vitalising these tools effectively, AWS tools form a complete, comprehensive ecosystem to handle big data, challenges, access storage, processes, transforms and machine learning. <\/span><\/p>\n<p>&nbsp;<\/p>\n<h2><span class=\"ez-toc-section\" id=\"AWS_Governance_Monitoring_for_Big_Data\"><\/span><b>AWS Governance &amp; Monitoring for Big Data<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">AWS projects have shown considerable scaling and complexity, where governance and monitoring become highly essential to ensure optimised performance, cost control, and data security. Here are a few tools that help organisations monitor their infrastructure, manage resource usage, and maintain compliance conveniently.\u00a0<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h4><b>Amazon CloudWatch \u2013 For Metrics &amp; Monitoring<\/b><b><br \/>\n<\/b><span style=\"font-size: 16px; font-weight: 400;\">It provides real-time monitoring for AWS resources, applications, and services. Collecting and tracking metrics such as CPU usage, memory utilisation, and disk I\/O from services like EC2, S3, SageMaker, and Lambda.<\/span><\/h4>\n<\/li>\n<\/ul>\n<ul>\n<li aria-level=\"1\">\n<h4><b>AWS CloudTrail \u2013 For Governance &amp; Auditing<br \/>\n<\/b><span style=\"font-size: 16px; font-weight: 400;\">AWS CloudTrail provides visibility into all API calls made within the AWS account. It is a central audit trail for security and operations, reviewing each move.<\/span><\/h4>\n<\/li>\n<\/ul>\n<ul>\n<li aria-level=\"1\">\n<h4><b>AWS Cost Explorer \u2013 For Cost &amp; Usage Analysis<br \/>\n<\/b><span style=\"font-size: 16px; font-weight: 400;\">AWS Cost Explorer is a budgeting and cost visualisation tool that makes businesses understand their AWS spend and optimise it effectively.<\/span><\/h4>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Best_Practices_to_Manage_Big_Data_with_MLA-C01\"><\/span><b>Best Practices to Manage Big Data with MLA-C01<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">It&#8217;s truly a critical skill to manage big data in any sector and industry, which is validated by AWS Certified Machine Learning \u2013 Speciality (MLA-C01) certification. These best practices are to be understood and followed to optimise performance and cost, also ensure data security and scalability across ML workflows.\u00a0<\/span><b><\/b><\/p>\n<ul>\n<li><span style=\"font-weight: 400;\">Leverage on S3 storage class, which is standard, Intelligent-Tiering, Glacier, etc. &#8211; that optimises cost based on the patterns that the data is accessed. You can then configure the lifecycle policy, which is automated to transition older data into cost-effective tiers.\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\">You can also secure the data with IAM and Encryption by implementing IAM roles that have the least privileged access and use S3 bucket policies to fine-grain control. And for key management, enable server-side encryption and you can also consider <a title=\"AWS KMS\" href=\"https:\/\/docs.aws.amazon.com\/AmazonRDS\/latest\/UserGuide\/Overview.Encryption.Keys.html\" target=\"_blank\" rel=\"nofollow noopener\"><strong>AWS KMS<\/strong><\/a>.\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\">By storing data in columnar format like parquet or ORC, it&#8217;s convenient to reduce the storage size and improve the read performance. You also optimize the partitioning strategies for faster query performance in analytics and ML pipelines.\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\">With Amazon CloudWatch for storage metrics and CloudTrail tracks API usage and identity anomalies that access data and configuration, you can monitor and audit.\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\">The S3 versioning is used for backup and recovery and design workflow by assuming eventual consistency. By combining AWS Lambda or Step Function with S3 automate scalable data processing tasks.\u00a0<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The core of the MLA-C01 certification aligns with these practices and by contributing to preparing data, implementing ML solutions and maintaining operational excellence in machine learning projects.\u00a0<\/span><\/p>\n<p>&nbsp;<\/p>\n<h2><span class=\"ez-toc-section\" id=\"To_Sum_Up\"><\/span><b>To Sum Up\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">In this blog, we saw the abilities of AWS to handle big data in real time for data processing, transforming, managing, storing, and analysing. This highly contributes towards big data machine learning model training. With its built-in exclusive ecosystem that enables businesses to handle big data.\u00a0 All the Business and cloud enthusiasts who are willing to explore big data with minimal ML training can start with the AWS Certified Machine Learning Associate Certification. We have dedicated practice tests for MLA-C01 and for further hands-on learning, check out our <\/span><a title=\"sandboxes\" href=\"https:\/\/www.whizlabs.com\/cloud-sandbox\/\" target=\"_blank\" rel=\"noopener\"><b>sandboxes<\/b><\/a><span style=\"font-weight: 400;\"> and <\/span><a title=\"hand-on labs\" href=\"https:\/\/www.whizlabs.com\/hands-on-labs\/\" target=\"_blank\" rel=\"noopener\"><b>hand-on labs<\/b><\/a><span style=\"font-weight: 400;\"> that are available. Get started with our practice test and level up your A game in training big data for ML in organisations.\u00a0<\/span><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this blog, we will discover the abilities of AWS to handle big data for ML.\u00a0 Maintaining high-quality data and records comes with a high level of challenges, but the AWS tools for processing and analysing big data come in handy. Let us explore how AWS achieves this! Major Challenges In Handling Big Data For\u00a0 Businesses Today, businesses are moving towards data-driven decision-making. From analysing customer interactions and transactions to IoT sensor data and Social media analysis, data is the starting point to extract meaningful insights. The main challenges faced in handling big data are\u00a0 Storing and retrieving data without 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Sivakumar","author_link":"https:\/\/www.whizlabs.com\/blog\/author\/mythili\/"},"uagb_comment_info":23,"uagb_excerpt":"In this blog, we will discover the abilities of AWS to handle big data for ML.\u00a0 Maintaining high-quality data and records comes with a high level of challenges, but the AWS tools for processing and analysing big data come in handy. Let us explore how AWS achieves this! Major Challenges In Handling Big Data For\u00a0&hellip;","_links":{"self":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/99272","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/users\/444"}],"replies":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/comments?post=99272"}],"version-history":[{"count":7,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/99272\/revisions"}],"predecessor-version":[{"id":99285,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/99272\/revisions\/99285"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media\/99274"}],"wp:attachment":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media?parent=99272"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/categories?post=99272"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/tags?post=99272"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}