Overview

• AWS Lake Formation is a fully managed service that makes it easier for you to build, secure, and manage data lakes. 
• Lake Formation simplifies and automates many of the complex manual steps that are usually required to create data lakes.
• These steps include collecting, cleansing, moving, and cataloging data, and securely making that data available for analytics and machine learning.
• Lake Formation provides its own permissions model that augments the AWS Identity and Access Management (IAM) permissions model. This centrally defined permissions model enables fine-grained access to data stored in data lakes through a simple grant/revoke mechanism.
• Lake Formation permissions are enforced at the table and column level across the full portfolio of AWS analytics and machine learning services.

How It Works

• Lake Formation helps you do the following, either directly or through other AWS services:
  • Register the Amazon Simple Storage Service (Amazon S3) buckets and paths where your data lake will reside.
  • Orchestrate data flows that ingest, cleanse, transform, and organize the raw data.
  • Create and manage a Data Catalog containing metadata about data sources and data in the data lake.
  • Define granular data access policies to the metadata and data through a grant/revoke permissions model.

•  Lake Formation manages AWS Glue crawlers, AWS Glue ETL jobs, the Data Catalog, security settings, and access control. After the data is securely stored in the data lake, users can access the data through their choice of analytics services, including Amazon Athena, Amazon Redshift, and Amazon EMR.

Components

• Data Lake
  • The data lake is your persistent data that is stored in Amazon S3 and managed by Lake Formation using a Data Catalog. A data lake typically stores the following:
    • Structured and unstructured data
    • Raw data and transformed data
  • For an Amazon S3 path to be within a data lake, it must be registered with Lake Formation.
• Data Access
  • Lake Formation provides secure and granular access to data through a new grant/revoke permissions model that augments AWS Identity and Access Management (IAM) policies.
• Blueprint
  • A blueprint is a data management template that enables you to easily ingest data into a data lake.
  • Lake Formation provides several blueprints, each for a predefined source type, such as a relational database or AWS CloudTrail logs.
  • From a blueprint, you can create a workflow.
  • Blueprints take the data source, data target, and schedule as input to configure the workflow.
• Workflow
  • A workflow is a container for a set of related AWS Glue jobs, crawlers, and triggers.
  • Workflows consist of AWS Glue crawlers, jobs, and triggers that are generated to orchestrate the loading and update of data.
  • You create the workflow in Lake Formation, and it executes in the AWS Glue service.
  • Lake Formation can track the status of a workflow as a single entity.
  • When you define a workflow, you select the blueprint upon which it is based. You can then run workflows on demand or on a schedule.
• Data Catalog
  • The Data Catalog is your persistent metadata store.
  • It is a managed service that lets you store, annotate, and share metadata in the AWS Cloud
• Underlying Data
  • Underlying data refers to the source data or data within the data lakes that Data Catalog tables point to.
• Principal
  • A principal is an AWS Identity and Access Management (IAM) user or role or an Active Directory user.
• Data Lake Administrator
  • A data lake administrator is a principal who can grant any principal (including self) any permission on any Data Catalog resource or data location.
• The following are the general steps to create and use a data lake:
  • Register an Amazon Simple Storage Service (Amazon S3) path as a data lake.
  • Grant Lake Formation permissions to write to the Data Catalog and to Amazon S3 locations in the data lake.
  • Create a database to organize the metadata tables in the Data Catalog.
  • Use a blueprint to create a workflow. Run the workflow to ingest data from a data source.
  • Set up your Lake Formation permissions to allow others to manage data in the Data Catalog and the data lake.
  • Set up Amazon Athena to query the data that you imported into your Amazon S3 data lake.
  • For some data store types, set up Amazon Redshift Spectrum to query the data that you imported into your Amazon S3 data lake.

Reference

What Is AWS Lake Formation? - AWS Lake Formation

标签：45,AWS,Lake,lake,Amazon,Formation,data
来源： https://blog.csdn.net/meiyubaihe/article/details/121389702