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This post covers setting up trusted identity propagation between AWS IAM Identity Center, Amazon Redshift, and AWS Lake Formation on separate accounts. It also covers cross-account S3 data lake sharing using Lake Formation to enable Redshift analytics, and using QuickSight for insights. #aws #awscloud #cloud #amazonquicksight #amazonredshift #awsglue #awsiamidentitycenter #awslakeformation

Amazon S3 (Simple Storage Service) is a highly scalable and durable object storage service provided by Amazon Web Services (AWS). Here's a detailed explanation of Amazon S3: Object Storage: Amazon S3 is designed for storing and retrieving data as objects, such as files, documents, images, videos, and backups.Objects are stored in buckets, which act as containers for organizing and managing data within S3. Scalability and Durability: S3 offers virtually unlimited storage capacity, allowing users to store terabytes to exabytes of data.It provides 99.999999999% (11 nines) durability, ensuring data reliability and protection against data loss. Data Access: Objects in S3 are accessed via HTTP or HTTPS protocols, making them accessible from anywhere with internet connectivity.S3 supports fine-grained access control using bucket policies, IAM (Identity and Access Management) policies, and Access Control Lists (ACLs). Storage Classes: S3 offers multiple storage classes, including Standard, Standard-IA (Infrequent Access), One Zone-IA, Intelligent-Tiering, Glacier, and Glacier Deep Archive.Each storage class has different pricing, durability, availability, and retrieval characteristics, allowing users to optimize costs based on data access patterns. Versioning: Amazon S3 supports versioning, allowing users to store multiple versions of an object, track changes over time, and recover previous versions if needed.Versioning helps protect against accidental deletions, data corruption, and unauthorized modifications. Lifecycle Management: S3 lifecycle policies enable automatic data management based on predefined rules, such as transitioning objects between storage classes, deleting expired objects, and archiving data to Glacier for long-term storage.Lifecycle policies help optimize storage costs and comply with data retention policies. Data Encryption: S3 provides encryption options for data at rest and in transit, including SSE-S3 (Server-Side Encryption with S3 Managed Keys), SSE-KMS (Server-Side Encryption with AWS Key Management Service), and client-side encryption.Encryption ensures data confidentiality and security, protecting sensitive information stored in S3. Data Transfer Acceleration: AWS offers Amazon S3 Transfer Acceleration, a feature that uses Amazon CloudFront's globally distributed edge locations to accelerate data transfers to and from S3.Transfer Acceleration improves data upload and download speeds, especially for geographically distributed users. Integration and Use Cases: Amazon S3 integrates with various AWS services, such as AWS Lambda, Amazon EC2, Amazon Redshift, Amazon EMR, and AWS DataSync, enabling data storage, processing, analytics, and backup workflows.Common use cases for S3 include data lakes, backup and recovery, content delivery, static website hosting, media storage, and archiving. #cybersecurity #cybersecurityawareness #aws #cloud

New physical AWS Data Transfer Terminals let you upload to the cloud faster https://ift.tt/LR9vSln Today, we’re announcing the general availability of AWS Data Transfer Terminal, a secure physical location where you can bring your storage devices, connect directly to the AWS network, and upload data faster to the AWS Cloud, bypassing the need for internet-based data transfers. The first Data Transfer Terminals are located in Los Angeles and New York, with plans to add more locations globally. You can reserve a time slot to visit your nearest location and upload data rapidly and securely to any AWS public endpoints, such as Amazon Simple Storage Service (Amazon S3) and Amazon Elastic File System (Amazon EFS), at speeds of up to 400 Gbps. Using AWS Data Transfer Terminal, you can significantly reduce the time of ingesting data with high throughput connectivity in the location near by you. You can upload large datasets from fleets of vehicles operating and collecting data in metro areas for training machine learning (ML) models, digital audio and video files from content creators for media processing workloads, and mapping or imagery data from local government organizations for geographic analysis. After the data is uploaded to AWS, you can use the extensive suite of AWS services to generate value from your data and accelerate innovation. You can also bring your AWS Snowball devices to the location for upload and retain the device for continued use and not rely on traditional shipping methods. Getting started with AWS Data Transfer Terminal You can find the availability of a location in the AWS Management Console and reserve the date and time to visit. Then, you can visit the location, make a connection between your storage device and S3 bucket, initiate the transfer of your data, and validate that your transfer is complete. Go to the AWS Data Transfer Terminal console, then choose Get started. Choose Create Transfer Team and make a team by adding the team’s name and description with agreement of service terms and conditions. You can add your team members for personal or group reservation in the team setting. To reserve your time and location, choose Create Reservation. In the first step, choose your team, a process owner to manage your reservation, and team members to visit the location for the data transferring job. Now, you can choose a location of Data Transfer Terminal facility and set your preferred visiting time. You’ll pay for the space reservation at an hourly rate for your reserved time. To secure your reservation, choose Next and Create after reviewing the reservation details. After your reservation is requested, you can find your upcoming reservations in the team page. You can check the reservation status or cancel your reservation. On your reserved date and time, visit the location and confirm access with the building reception. You’re escorted by building staff to the floor and your reserved room of the Data...

When migrating a large, on-premises database to the cloud, the sheer size of the data can present significant obstacles. This blog post, co-authored with Pragnesh S and Chibuzor Onwudinjor, aims to provide guidance and insights to make this migration process more seamless. The goal is to equip you with the knowledge and strategies needed to overcome the database size barrier and successfully migrate your on-premises database to the AWS cloud. Pragnesh S Chibuzor Onwudinjor #aws #blogs #migration #modernization #data

🎨 Learn how Amazon DataZone uses popular AWS services you may already have in your environment, including Amazon Redshift, Amazon Athena, AWS Glue & AWS Lake Formation, as well as on-premises & third-party sources. 📄 https://go.aws/3US1tuU

🚀 Data Migration: Moving from On-Premise to Google Cloud BigQuery 🚀 Facing challenges with data storage and processing on your On-Premise servers? Don't worry, the solution is within reach! In this article, I discuss how migrating data to the Cloud can overcome the limitations of On-Premise infrastructure. By utilizing Google Cloud BigQuery, you can enjoy seamless data management without worrying about storage capacity and processing speed. 💡 What You'll Learn: 1. Technical Data Migration Strategy: Step-by-step process from PostgreSQL On-Premise to BigQuery. 2. Data Export Process: Easily move data from PostgreSQL to Google Cloud Storage. 3. ETL (Extract, Transform, Load): Efficiently transfer data from GCS to BigQuery. Read the full article here: https://lnkd.in/ggWC-whg #DataMigration #BigData #GoogleCloud #BigQuery #DataEngineering #CloudComputing #TechInnovation

Navigating the AWS Aurora Adventure: Unveiling the Limitations That Keep You Grounded in the Cloud! ⚠️ Hey cloud ☁️ enthusiasts, As we journey through the vast expanse of AWS Aurora, it's essential to shine a light on the limitations. 1. Scaling Sensitive: - While AWS Aurora boasts impressive scalability, scaling beyond certain thresholds can be challenging. As your DB grows to massive proportions, you may encounter bottlenecks and performance limitations that require careful planning and optimization strategies. Whether it's adjusting instance sizes, partitioning data, or optimizing queries, scaling sensitive workloads in Aurora requires a thoughtful approach. 2. Locking Heads with Locking: - DB locking mechanisms can sometimes put a damper on concurrency and performance in AWS Aurora. With its optimistic concurrency control model, Aurora aims to minimize locking contention and maximize throughput. However, in highly concurrent environments or scenarios with complex transactional logic, you may encounter situations where locking becomes a bottleneck. It's essential to understand Aurora's locking behavior and design your applications accordingly to avoid performance pitfalls. 3. Regionally Challenged: - While AWS Aurora offers multi-region replication for disaster recovery and data locality, cross-region performance can be a sticking point. Data transfer costs, network latency, and eventual consistency considerations may impact the performance and reliability of cross-region Aurora deployments. It's crucial to weigh the trade-offs and carefully plan your multi-region architecture to ensure optimal performance and resilience across regions. 4. Data Durability Dilemmas: - AWS Aurora prioritizes performance and availability, but data durability can sometimes take a back seat. In certain failure scenarios, such as simultaneous failures of multiple storage nodes, Aurora may experience data loss or inconsistency. While Aurora's built-in replication and backup features provide safeguards against data loss, it's essential to have a robust backup and recovery strategy in place to mitigate the risk of data durability dilemmas. 5. Feature FOMO: - Despite its rich feature set, AWS Aurora may leave you experiencing a case of "feature FOMO" (Fear Of Missing Out). Compared to traditional relational databases or specialized solutions, Aurora may lack certain advanced features or extensions that are critical for specific use cases. Whether it's advanced analytics, specialized indexing, or native support for specific data types, it's essential to evaluate Aurora's feature set against your requirements and consider alternative solutions if necessary. As we navigate the AWS Aurora adventure, let's embrace these limitations as opportunities for growth and innovation. By understanding Aurora's constraints and leveraging its strengths, we can chart a course to success in the cloud! ✨ #AWS #AuroraAdventures #CloudChallenges #DatabaseJourney

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and integrate data from multiple sources. AWS Glue customers often have strict security requirements like locking down network connectivity or running inside a VPC. #aws #awscloud #cloud #advanced300 #awsglue #bestpractices #technicalhowto

Immuta's recent achievement of the AWS Data and Analytics Competency status highlights its commitment to helping clients optimise data management and security on AWS. This recognition underscores Immuta's technical expertise and positions the company as a valuable partner for organisations looking to enhance their data strategies. With robust solutions for services like Amazon S3 and Amazon Redshift, Immuta is set to support enterprises in securely leveraging their data. This is a crucial step as businesses increasingly rely on data-driven decisions. What are your thoughts on the growing importance of data security in the cloud? How is your organisation addressing these challenges? Source: https://buff.ly/3Lquqsz #DataSecurity #AWS #DataAnalytics #Immuta #CloudComputing #DataGovernance