AWS Redshift: 9 Things You HAVE To Know 🔧 скачать в хорошем качестве

AWS Redshift: 9 Things You HAVE To Know 🔧

Amazon Redshift is a fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. Hi Guys, this is Abi from GokceDB and in this video, you are going to learn 9 things about Amazon Redshift in AWS. Let's get into it. 1. Amazon Redshift uses SQL to analyze structured and semi-structured data across operational databases, data lakes, and data warehouses. This enables you to get integrated insights with real-time and predictive analytics on complex datasets. 2. Amazon Redshift is optimized for datasets ranging from a few hundred gigabytes to a petabyte or more. As of this recording, the service costs less than $1,000 per terabyte per year which is one-tenth the cost of most traditional data warehousing solutions. 3. You can subscribe to Redshift cloud data warehouse products in AWS Data Exchange which allows you to query datasets from your own Redshift cluster without extracting, transforming, and loading the data. 4. With Redshift ML, you can use SQL statements to create and train Amazon SageMaker models on your data in Amazon Redshift. Later, you can use these models for predictions such as churn detection, financial forecasting, and risk scoring directly in your queries and reports. 5. Redshift's Integration for Apache Spark makes it easy to build and run Apache Spark applications on Amazon Redshift data. This enables you to open up your data warehouse for a broader set of analytics and machine learning solutions. 6. You get no-code integration between Amazon Aurora and Redshift. This allows Amazon Aurora users to use Amazon Redshift for near real-time analytics and machine learning on petabytes of transactional data. 7. Redshift makes it easy to both query and write data back to your data lake in open file formats such as Parquet, ORC, JSON, Avro, CSV, and ANSI SQL. Exporting data back to your data lake from Redshift lets you analyze the data further with AWS services such as Amazon Athena, Amazon EMR, and Amazon SageMaker. 8. Redshift supports auto-copy to automate data loading from Amazon S3. This reduces time and effort to build custom solutions or manage 3rd party services. As new data arrives in specified S3 folders, the ingestion process is triggered automatically based on user-defined configurations. 9. The service allows you to scale with minimal cost impact, as each cluster earns up to one hour of free Concurrency Scaling credits per day. In summary, Amazon Redshift enables you to break through data silos and gain real-time and predictive insights on all your data in a few clicks with no data movement or transformation. There you have it.