- ACCESSING REDSHIFT CLUSTER IN VPC VIA TABLEAU SERVER DRIVERS
- ACCESSING REDSHIFT CLUSTER IN VPC VIA TABLEAU SERVER MANUAL
The Data API is asynchronous, so you can retrieve your results later. The Data API takes care of managing database connections and buffering data. Instead, you can run SQL commands to an Amazon Redshift cluster by simply calling a secured API endpoint provided by the Data API.
ACCESSING REDSHIFT CLUSTER IN VPC VIA TABLEAU SERVER DRIVERS
The Data API eliminates the need for configuring drivers and managing database connections. The Amazon Redshift Data API simplifies data access, ingest, and egress from programming languages and platforms supported by the AWS SDK, such as Python, Go, Java, Node.js, PHP, Ruby, and C++. Simple API to interact with Amazon Redshift: Amazon Redshift lets you painlessly access data with all types of traditional, cloud-native, and containerized, serverless web services-based applications and event-driven applications. Query using your own tools: Amazon Redshift gives you the flexibility to run queries within the console or connect SQL client tools, libraries, or data science tools including Amazon Quicksight, Tableau, PowerBI, QueryBook and Jupyter Notebook.
ACCESSING REDSHIFT CLUSTER IN VPC VIA TABLEAU SERVER MANUAL
The additional features Automatic Vacuum Delete, Automatic Table Sort, and Automatic Analyze eliminate the need for manual maintenance and tuning of Redshift clusters to get the best performance for new clusters and production workloads. If Amazon Redshift determines that applying a key will improve cluster performance, tables will be automatically altered without requiring administrator intervention. Automatic Table Optimization selects the best sort and distribution keys to optimize performance for the cluster’s workload. It also provides an intuitive editor for authoring and sharing SQL queries, analyses, visualizations, and annotations, and securely sharing them with your team.Īutomated Table Design: Amazon Redshift monitors user workloads and uses sophisticated algorithms to find ways to improve the physical layout of data to optimize query speeds. Query Editor v2 lets you visualize query results in a single click, create schemas and tables, load data visually, and browse database objects.
Query Editor v2: Use SQL to make your Amazon Redshift data and data lake more accessible to data analysts, data engineers, and other SQL users with a web-based analyst workbench for data exploration and analysis.
With Redshift Serverless, any user-including data analysts, developers, business professionals, and data scientists-can get insights from data by simply loading and querying data in the data warehouse. Get simplified data access, ingest, and egress from numerous programming languages and platforms without configuring drivers and managing database connections.įocus on getting from data to insights in seconds and delivering on your business outcomes, without worrying about managing your data warehouse.Īmazon Redshift Serverless (preview): Amazon Redshift Serverless (preview) is a serverless option of Amazon Redshift that makes it easy to run and scale analytics in seconds without the need to set up and manage data warehouse infrastructure.
Optimize your business intelligence - Build insight-driven reports and dashboards using Amazon QuickSight, Tableau, Microsoft PowerBI, or other business intelligence tools. Improve financial and demand forecasts - Automatically create, train, and deploy machine learning models for predictive insights.Ĭollaborate and share data - Securely share data among accounts, organizations, and partners while building applications on top of third-party data. Run and scale analytics in seconds on all your data without having to manage your data warehouse infrastructure. Tens of thousands of customers today rely on Amazon Redshift to analyze exabytes of data and run complex analytical queries, making it the most widely used cloud data warehouse. Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and machine learning to deliver the best price performance at any scale.
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