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AWS DATA LAKEHOUSE UPGRADE

Defining the governance and transformation layer Data lakes on Amazon S3 also integrate with other AWS ecosystem services (for example, AWS Athena for interactive querying or third-party tools running off Amazon Elastic Compute Cloud (Amazon EC2) instances). The data lake layer provides 99.999999999% data durability and supports various data formats, allowing you to future proof the data lake. AWS Database Migration Service (AWS DMS) connects and migrates data from relational databases, data warehouses, and NoSQL databases.įigure 4.AWS Glue connects to real-time data streams to extract, load, transform, clean, and enrich data.This service is for systems that support batch transfer modes and have no real-time requirements, such as external data entities. AWS Transfer Family for SFTP integrates with source systems to extract data using secure shell (SSH), SFTP, and FTPS/FTP.Data is then placed into a data lake.įigure 3 shows the following services to be included in this layer: Services in this layer work directly with the source systems based on their supported data extraction patterns. This can happen while services in the purpose-built consumption layer address individual business unit requirements. Services can be added, removed, and updated independently when new data sources are identified like data sources to enrich data via AWS Data Exchange. This lake house architecture provides you a de-coupled architecture.

Provide capability to consume and visualize information via purpose-built consumption/value layer.

Build a governance and transformation layer to manipulate data.

Build data ingestion layer using services that support source systems extraction capabilities.

Identify source system extraction capabilities to define an ingestion layer that loads data into a data lake.

These steps summarize building a lake house on AWS: High-level design for an AWS lake house implementation Building a lake house on AWS As such, simply integrating a data lake with a data warehouse is not sufficient.Įach step in Figure 1 needs to be de-coupled to build a lake house.įigure 2. These compromises can include agility associated with change management and impact of different business domain reporting requirements on the data from a central platform. This approach acknowledges that a one-size-fits-all approach to analytics eventually leads to compromises. Lake house architecture uses a ring of purpose-built data consumers and services centered around a data lake. High-level architecture for implementing an AWS lake house Our solution provides you the ability to scale, integrate with multiple sources, improve business agility, and help future proof your analytics investment. In this post, we provide you a reference architecture and show you how an AWS lake house will help you overcome the aforementioned limitations.

These all bind an organization’s growth to the growth of the appliance provider. However, these systems incur operational overhead, are limited by proprietary formats, have limited elasticity, and tie customers into costly and inhibiting licensing agreements.

AWS DATA LAKEHOUSE UPGRADE

To help with this, customers upgrade their traditional on-premises online analytic processing (OLAP) databases to hyper converged infrastructure (HCI) solutions. Organizational analytics systems have shifted from running in the background of IT systems to being critical to an organization’s health.Īnalytics systems help businesses make better decisions, but they tend to be complex and are often not agile enough to scale quickly.