#zeroETLIntegrations

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Aurora PostgreSQL zero-ETL Integration With Amazon Redshift

The general availability of Amazon Aurora PostgreSQL and Amazon DynamoDB zero-ETL integrations with Amazon Redshift.

The Amazon Aurora PostgreSQL-Compatible Edition zero-ETL integrations with Amazon Redshift are now generally available. By eliminating the need to create and maintain intricate data pipelines that carry out extract, transform, and load (ETL) activities, zero-ETL integration effortlessly makes transactional or operational data available in Amazon Redshift. It updates source data for you to use in Amazon Redshift for analytics and machine learning (ML) skills to extract timely insights and efficiently respond to important, time-sensitive events while automating source data replication to Amazon Redshift.

With these new zero-ETL integrations, you can conduct unified analytics on your data from various applications, eliminating the need to create and maintain separate data pipelines to write data from multiple relational and non-relational data sources into a single data warehouse.

Amazon Redshift is the target and a source is specified in order to construct a zero-ETL integration. The integration keeps an eye on the pipeline’s condition while replicating data from the source to the target data warehouse and making it easily accessible in Amazon Redshift.

Amazon Redshift integration of Aurora PostgreSQL zero-ETL

Near real-time analytics on petabytes of transactional data are made possible by the integration of Amazon Redshift and Amazon Aurora zero-ETL.

Why Aurora zero-ETL integration with Amazon Redshift?

Near real-time analytics and machine learning (ML) on petabytes of transactional data are made possible by the integration of Amazon Redshift with Amazon Aurora zero-ETL. Zero-ETL eliminates the need to create and maintain intricate data pipelines that carry out extract, transform, and load (ETL) activities by effortlessly making transactional data available in Amazon Redshift a few seconds after it was entered into Amazon Aurora.

Advantages

Access to data in almost real time

Run near real-time analytics and machine learning on petabytes of data by accessing transactional data from Aurora in Amazon Redshift in a matter of seconds.

Simple to use

Without having to create and maintain ETL pipelines to transfer transactional data to analytics platforms, you can quickly examine your transactional data in almost real time.

Smooth integration of data

To perform unified analytics across numerous apps and data sources, combine several tables from different Aurora database clusters and replicate your data to a single Amazon Redshift data warehouse.

Absence of infrastructure management

Using both Amazon Redshift Serverless and Amazon Aurora Serverless v2, you can do analytics on transactional data in almost real-time without managing any infrastructure.

Use cases

Operational analytics in near real time

To effectively respond to important, time-sensitive events, use Amazon Redshift analytics and machine learning capabilities to extract insights from transactional and other data in almost real-time. For use cases like fraud detection, data quality monitoring, content targeting, better gaming experiences, and customer behavior research, near real-time analytics can help you obtain more precise and timely insights.

Large-scale analytics

Petabytes of your transactional data pooled from several Aurora database clusters can be analyzed using Amazon Redshift’s capabilities thanks to the Aurora zero-ETL connector. You can benefit from Amazon Redshift’s extensive analytical features, including federated access to numerous data stores and data lakes, materialized views, built-in machine learning, and data sharing. With Amazon Redshift ML’s native integration into Amazon SageMaker, you can use simple SQL queries to generate billions of predictions.

lessen the operational load

It is frequently necessary to create, oversee, and run a sophisticated data pipeline ETL system in order to move data from a transactional database into a central data warehouse. You may easily transfer the schema, current data, and data modifications from your Aurora database to a new or existing Amazon Redshift cluster with a zero-ETL integration. Complex data pipeline management is no longer necessary with zero-ETL integration.

How to begin

You designate an Amazon Redshift data warehouse as the target and an Aurora DB cluster as the data source when creating your zero-ETL interface between Aurora and Redshift. Data from the source database is replicated into the target data warehouse through the integration. Within seconds, the data is accessible in Amazon Redshift, enabling data analysts to start utilizing the analytics and machine learning features of the platform.

Cost

Aurora zero-ETL integration with Amazon Redshift is free of charge via AWS. The change data produced by a zero-ETL integration is created and processed using pre-existing Aurora and Amazon Redshift resources, which you pay for. These resources could consist of:

By turning on change data capture, more I/O and storage are used.

For the first data export to seed your Amazon Redshift databases, the snapshot export costs

Extra Amazon Redshift storage for data replication

Extra Amazon Redshift computation for data replication processing

Cross-AZ data transfer fees for transferring data between sources and destinations.

Continuous data change processing via zero-ETL integration is provided at no extra cost. Please visit the Aurora price page for additional details.

Availability

The AWS regions for the US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Hong Kong), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm) now offer Aurora PostgreSQL zero-ETL integration with Amazon Redshift.

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Redshift Amazon With RDS For MySQL zero-ETL Integrations

With the now broadly available Amazon RDS for MySQL zero-ETL interface with Amazon Redshift, near real-time analytics are possible.

For comprehensive insights and the dismantling of data silos, zero-ETL integrations assist in integrating your data across applications and data sources. Petabytes of transactional data may be made accessible in Redshift Amazon in only a few seconds after being written into Amazon Relational Database Service (Amazon RDS) for MySQL thanks to their completely managed, no-code, almost real-time solution.

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As a result, you may simplify data input, cut down on operational overhead, and perhaps even decrease your total data processing expenses by doing away with the requirement to develop your own ETL tasks. They revealed last year that Amazon DynamoDB, RDS for MySQL, and Aurora PostgreSQL-Compatible Edition were all available in preview as well as the general availability of zero-ETL connectivity with Redshift Amazon for Amazon Aurora MySQL-Compatible Edition.

With great pleasure, AWS announces the general availability of Amazon RDS for MySQL zero-ETL with Redshift Amazon. Additional new features in this edition include the option to setup zero-ETL integrations in your AWS Cloud Formation template, support for multiple integrations, and data filtering.

Data filtration

The majority of businesses, regardless of size, may gain from include filtering in their ETL tasks. Reducing data processing and storage expenses by choosing just the portion of data required for replication from production databases is a common use case. Eliminating personally identifiable information (PII) from the dataset of a report is an additional step. For instance, when duplicating data to create aggregate reports on recent patient instances, a healthcare firm may choose to exclude sensitive patient details.

In a similar vein, an online retailer would choose to provide its marketing division access to consumer buying trends while keeping all personally identifiable information private. On the other hand, there are other situations in which you would not want to employ filtering, as when providing data to fraud detection teams who need all of the data in almost real time in order to draw conclusions. These are just a few instances; We urge you to explore and find more use cases that might be relevant to your company.

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Zero-ETL Integration

You may add filtering to your zero-ETL integrations in two different ways: either when you construct the integration from scratch, or when you alter an already-existing integration. In any case, this option may be found on the zero-ETL creation wizard’s “Source” stage.

Entering filter expressions in the format database.table allows you to apply filters that include or exclude databases or tables from the dataset. Multiple expressions may be added, and they will be evaluated left to right in sequence.

If you’re changing an existing integration, Redshift Amazon will remove tables that are no longer included in the filter and the new filtering rules will take effect once you confirm your modifications.

Since the procedures and ideas are fairly similar, we suggest reading this blog article if you want to dig further. It goes into great detail on how to set up data filters for Amazon Aurora zero-ETL integrations.

Amazon Redshift Data Warehouse

From a single database, create several zero-ETL integrations

Additionally, you can now set up connectors to up to five Redshift Amazon data warehouses from a single RDS for MySQL database. The only restriction is that you can’t add other integrations until the first one has successfully completed its setup.

This enables you to give other teams autonomy over their own data warehouses for their particular use cases while sharing transactional data with them. For instance, you may use this in combination with data filtering to distribute distinct data sets from the same Amazon RDS production database to development, staging, and production Redshift Amazon clusters.

One further intriguing use case for this would be the consolidation of Redshift Amazon clusters via zero-ETL replication to several warehouses. Additionally, you may exchange data, train tasks in Amazon SageMaker, examine your data, and power your dashboards using Amazon Redshift materialized views.

In summary

You may duplicate data for near real-time analytics with RDS for MySQL zero-ETL connectors with Redshift Amazon, eliminating the need to create and maintain intricate data pipelines. With the ability to implement filter expressions to include or exclude databases and tables from the duplicated data sets, it is already widely accessible. Additionally, you may now construct connections from many sources to combine data into a single data warehouse, or set up numerous connectors from the same source RDS for MySQL database to distinct Amazon Redshift warehouses.

In supported AWS Regions, this zero-ETL integration is available for Redshift Amazon Serverless, Redshift Amazon RA3 instance types, and RDS for MySQL versions 8.0.32 and later.

Not only can you set up a zero-ETL connection using the AWS Management Console, but you can also do it with the AWS Command Line Interface (AWS CLI) and an official AWS SDK for Python called boto3.

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