#amazonredshift

Modern businesses need fast, scalable, and reliable analytics platforms to turn massive amounts of data into actionable insights — and Amazon Redshift is built for that mission.

Amazon Redshift is a high-performance cloud data warehouse designed to deliver scalable analytics using columnar storage, massively parallel processing (MPP), and optimized query execution.

From enterprise reporting and business intelligence to real-time analytics and data-driven decision-making, Redshift helps organizations process and analyze large-scale datasets with speed and efficiency.

With seamless cloud integration, advanced performance optimization, and scalable architecture, Amazon Redshift empowers businesses to unlock the full value of their data.

In the era of big data, performance and scalability define the future of analytics.

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Amazon Redshift offers the finest price-performance cloud data warehouse to support data-driven decision-making.

What is Amazon Redshift?

Amazon Redshift leverages machine learning and technology created by AWS to provide the greatest pricing performance at any scale, utilizing SQL to analyze structured and semi-structured data across data lakes, operational databases, and data warehouses.

With only a few clicks and no data movement or transformation, you can break through data silos and obtain real-time and predictive insights on all of your data.

With performance innovation out of the box, you may achieve up to three times higher pricing performance than any other cloud data warehouse without paying extra.

Use a safe and dependable analytics solution to turn data into insights in a matter of seconds without bothering about infrastructure administration.

Why Amazon Redshift?

Every day, tens of thousands of customers utilize Amazon Redshift to deliver insights for their organizations and modernize their data analytics workloads. Amazon Redshift’s fully managed, AI-powered massively parallel processing (MPP) architecture facilitates swift and economical corporate decision-making. With AWS’s zero-ETL strategy, all of your data is combined for AI/ML applications, near real-time use cases, and robust analytics. With the help of cutting-edge security features and fine-grained governance, data can be shared and collaborated on safely and quickly both inside and between businesses, AWS regions, and even third-party data providers.

Advantages

At whatever size, get the optimal price-performance ratio

With a fully managed, AI-powered, massively parallel processing (MPP) data warehouse designed for speed, scale, and availability, you can outperform competing cloud data warehouses by up to six times.

Use zero-ETL to unify all of your data

Use a low-code, zero-ETL strategy for integrated analytics to quickly access or ingest data from your databases, data lakes, data warehouses, and streaming data.

Utilize thorough analytics and machine learning to optimize value

Utilize your preferred analytics engines and languages to run SQL queries, open source analytics, power dashboards and visualizations, and activate near real-time analytics and AI/ML applications.

Use safe data cooperation to innovate more quickly

With fine-grained governance, security, and compliance, you can effortlessly share and collaborate on data both inside and between your businesses, AWS regions, and even third-party data sets without having to move or copy data by hand.

How it works

In order to provide the best pricing performance at any scale, Amazon Redshift leverages machine learning and technology created by AWS to analyze structured and semi-structured data from data lakes, operational databases, and data warehouses using SQL.

Use cases

Boost demand and financial projections

Allows you to create low latency analytics apps for fraud detection, live leaderboards, and the Internet of Things by consuming hundreds of megabytes of data per second.

Make the most of your business intelligence

Using BI tools like Microsoft PowerBI, Tableau, Amazon QuickSight, and Amazon Redshift, create insightful reports and dashboards.

Quicken SQL machine learning

To support advanced analytics on vast amounts of data, SQL can be used to create, train, and implement machine learning models for a variety of use cases, such as regression, classification, and predictive analytics.

Make money out of your data

Create apps using all of your data from databases, data lakes, and data warehouses. To increase consumer value, monetize your data as a service, and open up new revenue sources, share and work together in a seamless and safe manner.

Easily merge your data with data sets from outside parties

Subscribe to and merge third-party data in AWS Data Exchange with your data in Amazon Redshift, whether it’s market data, social media analytics, weather data, or more, without having to deal with licensing, onboarding, or transferring the data to the warehouse.

Amazon Redshift concepts

Amazon Redshift Serverless helps you examine data without provisioning a data warehouse. Automatic resource provisioning and intelligent data warehouse capacity scaling ensure quick performance for even the most demanding and unpredictable applications. The data warehouse is free when idle, so you only pay for what you use. The Amazon Redshift query editor v2 or your favorite BI tool lets you load data and query immediately. Take advantage of the greatest pricing performance and familiar SQL capabilities in a zero-administration environment.

If your company is eligible and your cluster is being formed in an AWS Region without Amazon Redshift Serverless, you may be eligible for the free trial. Choose Production or Free trial to answer. For what will you use this cluster? Free trial creates a dc2.large node configuration. AWS Regions with Amazon Redshift Serverless are included in the Amazon Web Services General Reference’s Redshift Serverless API endpoints.

Key Amazon Redshift Serverless ideas are below

Namespace: Database objects and users are in a namespace. Amazon Redshift Serverless namespaces contain schemas, tables, users, datashares, and snapshots.

Workgroup: A collection of computer resources. Amazon Redshift Serverless computes in workgroups. Redshift Processing Units, security groups, and use limits are examples. Configure workgroup network and security settings using the Amazon Redshift Serverless GUI, AWS Command Line Interface, or APIs.

Important Amazon Redshift supplied cluster concepts:

Cluster: A cluster is an essential part of an Amazon Redshift data warehouse’s infrastructure.

A cluster has compute nodes. Compiled code runs on compute nodes.

An additional leader node controls two or more computing nodes in a cluster. Business intelligence tools and query editors communicate with the leader node. Your client application only talks to the leader. External apps can see computing nodes.

Database: A cluster contains one or more databases.

One or more computing node databases store user data. SQL clients communicate with the leader node, which organizes compute node queries. Read about compute and leader nodes in data warehouse system design. User data is grouped into database schemas.

Amazon Redshift is compatible with other RDBMSs. It supports OLTP functions including inserting and removing data like a standard RDBMS. Amazon Redshift excels at batch analysis and reporting.

Amazon Redshift’s typical data processing pipeline and its components are described below.

A example Amazon Redshift data processing path is shown below.Image credit to AWS

An enterprise-class relational database query and management system is Amazon Redshift. Business intelligence (BI), reporting, data, and analytics solutions can connect to Amazon Redshift. Analytic queries retrieve, compare, and evaluate vast volumes of data in various stages to obtain a result.

Multiple data sources upload structured, semistructured, and unstructured data to the data storage layer at the data ingestion layer. This data staging section holds data in various consumption readiness phases. Storage may be an Amazon S3 bucket.

The optional data processing layer preprocesses, validates, and transforms source data using ETL or ELT pipelines. ETL procedures enhance these raw datasets. ETL engines include AWS Glue.

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.

Amazon launched three serverless database and analytics innovations to help clients build their data infrastructure faster and easier for their most demanding use cases. By automatically exceeding write constraints, Amazon Aurora Limitless Database simplifies application scaling and saves developers months over custom solutions.

Amazon ElastiCache Serverless makes clients’ most demanding applications highly available, scalable vertically and horizontally in under a minute without infrastructure administration. Amazon Redshift Serverless AI predicts workloads, scales, and price-optimizes resources. AWS’s advanced serverless solutions simplify operations and handle data at any scale to focus on end-user innovation. Visit aws.amazon.com/data to learn how AWS values data.

Dr. Swami Sivasubramanian, AWS Data and AI vice president, thinks data boosts digital potential. Serverless database and analytics services from AWS handle clients’ most demanding workloads due to dynamic data. Serverless solutions allow organizations to process millions of transactions per second, rapidly expand capacity, and optimize workloads for speed and cost.”

Companies store petabytes of multi-source data. These organizations need an end-to-end strategy to evaluate and manage data at any scale to maximize value. The Aurora relational database, ElastiCache in-memory cache, and Redshift data warehousing power many AWS customers’ important applications and data-driven decisions. Service simplifies database and analytics management for UX. Lambda and serverless S3 simplify client work. The most serverless data analytics from AWS simplifies autonomous provisioning, on-demand scalability, and pay-for-use pricing. These results show AWS’s serverless database and analytics capabilities, cutting costs and data value.

Amazon Aurora Limitless Database lets petabyte programs write millions/second

Amazon Aurora offers a 10% discount on fully maintained MySQL- and PostgreSQL-compatible relational databases to hundreds of thousands of customers. Hundreds of thousands of transactions per second power these apps using Amazon Aurora Serverless v2. With fine-grained capacity shift, the application scales with the right database resources.

Gaming and finance require hundreds of millions of people, transactions, and petabytes! To scale horizontally, these organizations must split data into smaller subsets and distribute them across multiple database instances through “sharding,” which requires months or years of developer effort to build custom software that routes requests to the correct instance or makes changes across multiple instances. Database capacity management requires time and resources. Create a table column, back up all compute instances, upgrade and patch, and adjust and balance instance demand. Maintenance must be coordinated by organizations. Companies need automatic scaling beyond one database without scaling technology.

Amazon Aurora Limitless Database handles petabytes and millions/second writes. A customer’s data model controls how Amazon Aurora Limitless Database distributes data and queries among serverless machines without software. Amazon Aurora Limitless Database scales resources vertically within serverless instances and horizontally across instances to improve workload performance and save clients months or years of software development. Maintenance and adjustments in one database can be automatically dispersed across instances, eliminating the need to manually manage common procedures across dozens or hundreds of databases.

Amazon ElastiCache Serverless deploys and grows cache faster and easier to meet application demand without provisioning, planning, or monitoring capacity

Developers cache frequently used data to speed up answers and lower database costs. These clients use open-source, in-memory Redis and Memcached for performance and scalability. For hundreds of thousands of clients demanding real-time, cost-optimized performance, AWS ElastiCache simplifies cache building and running. Amazon ElastiCache lets companies run mission-critical applications across multiple Availability Zones with hundreds of terabytes of data and hundreds of millions of operations per second at microsecond response times.

Amazon ElastiCache’s fine-grained customization options are popular, but some companies developing new applications or moving workloads prefer to start without constructing and supplying cache infrastructure, which requires experience and application traffic pattern knowledge. Businesses must monitor and scale capacity or overprovision peak capacity, raising costs, to thrive. They need infrastructure management to install and run cache faster.

Monitoring a cache’s compute, memory, and network use and scaling vertically and horizontally to meet demand without downtime or performance degradation, Amazon ElastiCache Serverless avoids capacity planning. Customers can construct a highly available cache in under a minute without infrastructure or configuration.

Next-generation Amazon Redshift Serverless AI-driven scaling and optimizations improve variable workload price-performance

Daily, Amazon Redshift handles exabytes of data for tens of thousands of clients. Multiple client requests increase Amazon Redshift Serverless data warehouse capacity. Customer prefer executing analytical workloads of various sizes on Amazon Redshift Serverless without managing data warehouse architecture, but they would gain more from adapting to job parameters such data volume and query complexity to maximize speed and cost. Dashboarding companies with predictable workloads may need more data and better searches for regulatory reporting.

An professional database administrator may spend hours shifting the new burden to a separate data warehouse or making multiple, complex manual adjustments to handle workload swings across all dimensions without disrupting existing workloads. This requires temporarily increasing data input and new query workload resources, pre-computing results for fast access, organizing data for efficient retrieval, and timing data warehouse management tasks. Every company’s performance and cost needs necessitate regular modifications to data volume, query complexity, and concurrent queries.

AI-optimized Price-effective Serverless Amazon Redshift scales job-dimensional resources. AI optimizes Amazon Redshift Serverless capacity to price-performance requirements based on query difficulty, data quantity, and frequency. In Amazon Redshift Serverless, client workload is forecast and resources adjusted. AWS Redshift Serverless with AI-driven scalability and optimizations dynamically reduces dashboard workload capacity over the day and boosts it for complex queries. For huge data volumes, Amazon Redshift Serverless scales overnight. Cost-benefit analysis and resource auto-tuning optimize Amazon Redshift Serverless apps. The AWS Console lets consumers choose between cost and performance.

ElastiCache enables Genesys’ all-in-one cloud platform, connecting millions of customers everyday. Amazon ElastiCache Serverless should speed up.

Serverless Amazon ElastiCache creates caches in under a minute without infrastructure, configuration, or capacity planning.

Peloton memberships and equipment help people worldwide exercise. “They collect and process data, from hardware sales to instructor trends and user workout data, to create and refine business decisions for better customer experiences,” said Data Engineering director Jerry Wang. As they will see, advanced analytical workloads require his database managers to manually tune capacity and databases. Amazon Redshift Serverless optimizations reduce data warehouse admin expenses and enhance productivity.

Digital engineering firm Quantiphi wants change. Sanchit Jain, the Q&A CEO, “Quantiphi provides tailored data analytics and machine learning solutions for his customers, and Amazon Redshift is his data warehouse foundation.” They want an affordable, effective solution. Past query results suggest Amazon Redshift Serverless’ AI-driven scalability and optimizations would make his solution adaptive, intelligent, autonomous, and cost-effective. AWS makes data volume growth affordable.”

IoT devices enhance product value and simplify partner and customer lives on Tuya Smart’s cloud platform. “Tuya’s IoT Developer Platform has over 846,000 registered developers from over 200 countries, serving more than 7,600 enterprises with Tuya IoT solutions,” stated Chong Chen, Tuya Smart’s Data Infrastructure chief After five years using Amazon Aurora and other AWS databases, massive write demands prompted them to design their own sharding and proxy solution. They like that Amazon Aurora Limitless Database can increase write throughput to accommodate his growing customer base and give a consistent, smooth, and efficient response without a self-managed solution.

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