1 / 8

Amazon S3 Data Lake — Concept and Features

Get Enterprise-level Data Integration for Snowflake, S3, SQL Server, Redshift, Azure Synapse, and more. Lightning-fast data replication in real-time 24x7 in your data lake. For large enterprises, fast data ingestion in bulk with partitioning and multi-thread loading. Create real-time data lakes without any coding. <br>

troyevans275
Download Presentation

Amazon S3 Data Lake — Concept and Features

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Amazon S3 Data Lake — Concept and Features Amazon S3 (Simple Storage Service) is a data storage service that helps build data lakes for storing unstructured, semi-structured, and structured data in their native formats. S3 ensures that data can be scaled seamlessly in a safe and secure environment with data durability of 11 9s (99.999999999). Key Concepts of Amazon S3To understand the functioning of the S3 data lake, it is necessary to know about the key concepts of Amazon S3.

  2. Data is stored in buckets in the Amazon S3 where a file comprises an object and metadata. A file or metadata can be stored in a bucket by loading an object in Amazon S3. After completing this step access permissions can be set on the related metadata or an object. The permissions may be limited to a select few only who can access logs and objects and decide where the buckets and their contents will be stored on Amazon S3.

  3. When an S3 data lake is built, several competencies can be accessed. These include big data analytics, media data processing applications, machine learning (ML), high-performance computing (HPC), and artificial intelligence (AI). All these help organizations get vital insights into unstructured datasets.From the S3 data lake, file systems can be initiated for ML and HPC applications and large volumes of media workloads processed with Amazon FSx Luster. There is also the flexibility to use HPC, ML, and AI applications through the S3 data lake from the Amazon Partner Network (APN).

  4. Features of the Amazon S3 Data LakeThere are several cutting-edge features of the Amazon S3 data lake. • In traditional systems, storage and computing facilities of data lakes were closely interlinked, making it very difficult to maintain data and optimize costs. The S3 data lake, on the other hand, has separate storage and computing capabilities, thereby increasing performance at reduced costs. Data can also be stored in their native formats. Amazon EC2 improves the performance of the S3 data lake by maximizing ratios of memory and bandwidth.

  5. When an S3 data lake is built, several competencies can be accessed. These include big data analytics, media data processing applications, machine learning (ML), high-performance computing (HPC), and artificial intelligence (AI). All these help organizations get vital insights into unstructured datasets.From the S3 data lake, file systems can be initiated for ML and HPC applications and large volumes of media workloads processed with Amazon FSx Luster. There is also the flexibility to use HPC, ML, and AI applications through the S3 data lake from the Amazon Partner Network (APN).

  6. Features of the Amazon S3 Data LakeThere are several cutting-edge features of the Amazon S3 data lake. • In traditional systems, storage and computing facilities of data lakes were closely interlinked, making it very difficult to maintain data and optimize costs. The S3 data lake, on the other hand, has separate storage and computing capabilities, thereby increasing performance at reduced costs. Data can also be stored in their native formats. Amazon EC2 improves the performance of the S3 data lake by maximizing ratios of memory and bandwidth.

  7. • The S3 data lake eases implementation on server less and non-cluster AWS platforms as it can process data with Amazon Redshift Spectrum, Amazon Athena, AWS Glue, and Amazon Rekognition. Server less computing is enabled on S3 and codes can be run without provisioning and managing servers. Payment is for resources used only without any flat fees or one-time charges. • The centralized environment of S3 allows building of S3 data lake in a multi-tenant ecosystem by bringing popular data analytics tools to a common data set. Hence, data governance quality is improved along with lower costs in comparison to earlier systems where several data copies had to be circulated across multiple data platforms.

  8. • APIs of the S3 data lake are uniform and consistent and are supported by numerous third-party vendors of software like Apache Hadoop and others. For all these features,S3 data lake is the preferred option for businesses for data lake requirements.

More Related