#zeroetl

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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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Zero ETL is an innovative approach that minimizes the traditional complexities of ETL processes by eliminating the transformation step. Instead, data is extracted from source systems and loaded directly into the target database without undergoing extensive transformations.

Know more at: https://bit.ly/3JtGqbN

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ChatGPT, ZeroETL, and Other Data Engineering Disruptors

http://i.securitythinkingcap.com/SlpLnG

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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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Amazon Connect analytics data lake simplifies custom centers

Amazon Connect Managed Service

Use AWS’s AI-powered contact centre, Amazon Connect, to revolutionise customer experience (CX) at scale.

With just a few clicks, set up a cloud contact centre and onboard agents to assist clients immediately.

What is Amazon Connect?

Boost agent output with generative AI

Contact centre representatives can provide exceptional client experiences right now thanks to Amazon Connect. Customer issues are automatically detected by Amazon Q, a generative AI-powered assistant available in Amazon Connect. Contextual customer information, suggested replies, and actions are delivered to agents for speedier resolution, all within the single workspace. Furthermore, you can help your agents handle client concerns more quickly and accurately by using step-by-step guidance to automatically recommend appropriate actions to take.

Evaluate, monitor, and enhance performance

Supervisors and managers may proactively identify and resolve problems with their customer experience (CX), agent performance, and contact centre operations by utilising AI-powered analytics and optimisation capabilities. In order to consistently raise customer happiness, managers and supervisors can quickly detect the need for agent coaching, automatically assess agent performance, and obtain real-time insights from customer interactions. To maximise contact centre operations, you may anticipate contact volume, assign the appropriate number of agents, and make the most use of your agents with the help of workforce management skills.

Make the omnichannel experience seamless

With the help of Amazon Connect, you can provide customers with individualised, effective, and proactive experiences via their preferred channels. Customers can save time and effort by receiving natural and intuitive self-service experiences in several languages when you use chatbots driven by artificial intelligence. Additionally, you can proactively engage consumers at scale with Amazon Connect by sending them appointment reminders or pertinent information through their preferred channel.

A contact center’s ability to succeed depends on its analytics. Gaining knowledge of every customer experience touchpoint enables you to monitor performance precisely and adjust to changing business needs. Even if the Amazon Connect console provides typical metrics, there are situations when you need more information and specific requirements for reporting because of the particular demands of your company.

The Amazon Connect analytics data lake is publicly accessible as of right now. This new feature, which was unveiled as a preview last year, makes it unnecessary for you to create and manage intricate data pipelines. The zero-ETL capability of the Amazon Connect data lake eliminates the requirement for extract, transform, and load (ETL).

Enhancing your Amazon Connect customer experience

You may combine several data sources, such as agent activity and customer contact information, into one place by using the Amazon Connect analytics data lake. Your data is now centrally located, which saves money on establishing complex data pipelines and gives you the ability to analyse contact centre performance and obtain insights.

You can access and analyse contact centre data, including contact trace records and Amazon Connect Contact Lens data, with the use of the this analytics data lake. This gives you the freedom to use your preferred business intelligence (BI) tools, such Tableau and Amazon QuickSight, to prepare and analyse data using Amazon Athena.

Utilize the Amazon Connect analytics data lake to get started

Setting up an Amazon Connect instance is a prerequisite for using the Amazon Connect analytics data lake. To establish a new Amazon Connect instance, just follow the instructions on the Create an Amazon Connect instance page. I’ll get right to demonstrating how to use the Amazon Connect analytics data lake.

You can choose instance by first navigating to the Amazon Connect console.

You can then configure analytics data lake by going to Analytics tools and choosing Add data share on the following page.

This opens a pop-up dialogue where you must first specify the AWS account ID that you want to use. You can use this option to set up a centralised account that will receive all data from instances of Amazon Connect running under different accounts. You can then choose the data types you have to share with the target AWS account under Data types. Please check the Associate tables for Analytics data lake to find out more about the sorts of data that you can share within the Amazon Connect analytics data lake.

After it’s finished, you can view a list of every target AWS account ID that you have shared every kind of data with.

You may associate your tables with the analytics data lake using the AWS Command Line Interface (AWS CLI) in addition to the AWS Management Console.

The request must then be approved (or rejected) in the target account’s AWS Resource Access Manager (RAM) console. RAM is an AWS service that facilitates safe resource sharing between accounts. You choose Resource sharing in the Shared with me section after navigating to AWS RAM.

You can now use Amazon Connect to access shared resources. You can now use AWS Lake Formation to create connected tables from shared tables. You go to the Tables page in the Lake Formation console and choose Create table.

You have to make a resource link to a database that is shared. Then fill the information and choose the location of the shared table as well as the available database.

The available shared tables that you can access will then be listed when you pick Shared table.

You can visit the Amazon Athena console to do some data searches.

You can access specific Amazon Connect data types with this setup. You can even use Amazon QuickSight to integrate and see the data.

Things to Know

Amazon Connect Pricing

Pricing: You can utilise this analytics data lake for up to two years without incurring any further fees. The services you use to engage with the data are the only ones for which you must pay.

Amazon Connect Availability

Availability: The Amazon Connect analytics data lake is typically accessible in the following AWS Regions: Africa (Cape Town), Asia Pacific (Mumbai, Seoul, Singapore, Sydney, Tokyo), US East (N. Virginia), US West (Oregon), Canada (Central), and Europe (Frankfurt, London).

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PostgreSQL to Amazon Redshift Zero ETL Integration

Understanding ETL Processes

In the realm of data integration, ETL (Extract, Transform, Load) processes play a crucial role. These processes involve extracting data from various sources, transforming it into a usable format, and loading it into a target destination. Understanding the basics of ETL is essential for efficient data management and analytics.

Common Challenges with ETL

Data integration through traditional ETL processes often faces challenges such as data quality issues, complex transformations, and scalability concerns. These challenges can hinder data processing efficiency and impact decision-making processes within organisations.

Introducing ZeroETL

ZeroETL is an innovative approach that aims to streamline data integration processes by minimising complexity and reducing costs. Unlike traditional ETL methods, ZeroETL focuses on minimising data movement and simplifying transformation steps, leading to improved agility and faster data access.

Benefits of ZeroETL

The adoption of ZeroETL brings several benefits to data management practices. These include minimised data movement, simplified transformations, enhanced agility, and improved efficiency in data processing workflows. Organisations can leverage ZeroETL to make faster, data-driven decisions and improve overall data management capabilities.

Live Integration Demo

In our video, we provide a live demonstration of ZeroETL integration between PostgreSQL and Amazon Redshift. Viewers will gain practical insights into setting up ZeroETL integration, transferring data seamlessly, and optimising data workflows for enhanced decision-making.

Limitations and Considerations

While ZeroETL offers significant advantages, it's essential to understand its limitations and considerations. Factors such as data volume, complexity of transformations, and compatibility with specific data sources may impact the effectiveness of ZeroETL integration. Organisations should carefully evaluate these aspects before implementing ZeroETL in their data management strategies.

Why Watch Our Video

Gain valuable insights into efficient data management techniques.

Simplify your data integration process with Zero ETL.

Stay updated with the latest tech trends and strategies.

Enhance your decision-making capabilities with streamlined data workflows.

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Zero ETL-safe BigQuery-Salesforce Data Cloud integration

Zero ETL on Google cloud and Salesforce

The general availability of bidirectional data sharing between BigQuery and Salesforce Data Cloud excites us. Customers will be able to easily enhance their data use cases by safely merging data from several platforms, all without having to pay extra for complicated ETL (Extract, Transform, Load) pipelines and data infrastructure development.

More touchpoints and devices are available to provide instantaneous customer experiences, making prompt customer service more important than ever. However, as more data is generated, collected, and dispersed across SaaS apps and analytics platforms, it’s becoming harder

A partnership between Google Cloud and Salesforce was announced last year. According to the partnership, customers can easily combine data from both Salesforce Data Cloud and BigQuery, and can take advantage of the combined power of BigQuery and Vertex AI solutions to unlock and enrich new analytics and AI/ML scenarios.

They are making these features generally available today, allowing joint Google Cloud and Salesforce customers to safely access their data across various platforms and clouds. Consumers won’t need to set up or maintain infrastructure in order to access their Salesforce Data Cloud in BigQuery. They can also utilize their Google Cloud data to enhance Salesforce Customer 360 and other apps.

Customers of Salesforce and Google Cloud gain from these announcements in the following ways:

A single pane of glass and serverless, cross-platform data access requiring no ETL

Regulated and safe two-way access to their BigQuery and Salesforce Data Cloud data in almost real-time, without requiring the creation of data pipelines and infrastructure.

Enhance Salesforce Customer 360 and Salesforce Data Cloud with their Google Cloud data. Additionally, the capacity to enhance client data with minimal data movement by combining it with other pertinent public datasets.

utilizing unique Vertex AI and Cloud AI services for churn modeling, predictive analytics, and returning to customer campaigns via the integration of Vertex AI and Einstein Copilot Studio.

BigQuery Omni and Analytics Hub allow customers to view their data holistically across the Salesforce and Google platforms, spanning cloud boundaries. With the help of this integration, data scientists, marketing analysts, and other data and business users can now combine data from the Google and Salesforce platforms to conduct AI/ML pipelines, analyze data, and gain insights in a self-service manner without the assistance of infrastructure or data engineering teams.

Customers can concentrate on analytics and insights because this integration is completely managed and governed, and it spares them from a number of significant business difficulties that often arise when integrating important enterprise systems. These innovations uphold the data governance and access policies that administrators have established. Access to datasets is restricted to those that have been expressly shared, and only those with permission can exchange and examine the information. Relevant data is pre-filtered from Salesforce Data Cloud to BigQuery with minimal copying, lowering egress costs and data engineering overhead when data is spread across multiple clouds and platforms.

Simple and safe access from Google Cloud to Salesforce Data Cloud

Consumers want to be able to access and integrate their loyalty and point-of-sale data from Google analytics platforms with their marketing, commerce, and service data from Salesforce Data Cloud to gain actionable insights about their customer behavior, such as likelihood to buy, cross-sell/upsell recommendations, and the ability to run highly customized promotional campaigns. Additionally, they wish to use unique Google AI services to create machine learning models for training and predictions on top of combined Salesforce and Google Cloud data. This will enable use cases like price elasticity, market-mix modeling, churn modeling, customer funnel analysis, and A/B test experimentation.

Customers can now easily access their Salesforce Data Cloud data with the launch of unique BigQuery cross-cloud and data sharing features. They have access to all the pertinent data required to run powerful ad campaigns and conduct cross-platform analytics securely with other Google products. Administrators of Salesforce Data Cloud can quickly and easily share data with the appropriate BigQuery users or groups. Through the Analytics Hub UI, BigQuery users can effortlessly subscribe to shared datasets.

With this platform integration, information can be shared in multiple ways:

You can use a single cross-cloud join of your Salesforce Data Cloud and Google Cloud datasets for smaller datasets and ad hoc access, such as to identify the store with the highest sales last year, with little data movement or duplication.

You can use cross-cloud materialized views to access larger data sets that power your executive update, weekly business review, or marketing campaign dashboards. These views are automatically and incrementally updated, bringing in new data only on a periodic basis.

Add Google Cloud-stored data to Salesforce Customer 360

They also hear from customers, particularly retailers, who want to use the rich features of Salesforce Data Cloud to deliver personalized messaging, create a more comprehensive customer 360-degree profile, and access and combine behavioral data from their websites and mobile apps that was collected by Google Analytics with their own data. Breaking down data silos and providing customers with seamless real-time access to Google Analytics data within Salesforce Data Cloud to create more detailed customer profiles and personalized experiences is now easier than ever.

Customers of Salesforce Data Cloud can connect to their Google Cloud account with ease using point-and-click navigation, choose pertinent BigQuery datasets, and make them available as External Data Lake Objects, enabling real-time data access. After becoming Data Lake Objects, they function as native Data Cloud objects, enhancing Customer 360 data models and providing insights to support Analytics and Personalization for Customer 360 models. The operational overhead and latency associated with the conventional ETL copy approach are eliminated by this integration, which also removes the need to create and maintain ETL pipelines for data integration.Imagec redit to Google cloud

Tearing down the barriers separating Google and Salesforce data

With the help of this platform integration between Google Cloud and Salesforce Data Cloud, businesses can now break down data silos, obtain actionable insights, and provide outstanding customer service. Through the power of Google AI, unified access, and seamless data sharing, this partnership is revolutionizing how businesses use their data to achieve success.

Customers can directly access data stored in Salesforce Data Cloud and combine it with data in Google Cloud to further enrich it for business insights and activation through the unique cross-cloud functionality of BigQuery Omni and the data sharing capabilities of Analytics Hub. Customers no longer need to build custom ETL or move data in order to perform unmatched cross-cloud analytics or view their data across clouds.

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