![]() The result is a large reduction in the amount of time Redshift needs to complete a single, massive job. The processors complete their computations simultaneously rather than sequentially. A large processing job is organized into smaller jobs which are then distributed among a cluster of processors (compute nodes). MPP is a distributed design approach in which several processors apply a "divide and conquer" strategy to large data jobs. In this scenario, being a column-oriented database allows Redshift to complete massive data processing jobs quickly. For example, in an online analytical processing-or OLAP-environment such as Redshift, users generally apply a smaller number of queries to much larger datasets. In contrast, column-oriented databases allow for increased speed when it comes to accessing large amounts of data. This is known as online transaction processing, or OLTP, and is used by most operational databases. That's because row-oriented systems are designed to quickly process a large number of small operations. ![]() The most common system of organizing data is by row. What determines the type of method is the nature of the workload. Column-oriented databasesĭata can be organized either into rows or columns. Here are the six features of that architecture that help Redshift stand out from other data warehouses. Redshift is known for its emphasis on continuous innovation, but it's the platform's architecture that has made it one of the most powerful cloud data warehouse solutions. Sign up for free → Contact Sales → 6 essential features of Redshift Second, Redshift services can be scaled to meet demand, so companies only pay for the capacity they need at a given point in time.Īs a result, Redshift provides a degree of agility and efficiency not possible with other types of data warehouses or infrastructures. First, they avoid the expense of building and maintaining a local infrastructure. Companies then rent the data processing and compute resources they need from Amazon.īusinesses capture two primary benefits from this model. In this scenario, Redshift provides a complete data warehouse infrastructure. Redshift helps companies overcome this obstacle by providing a cloud-based suite of data management, processing, and analytics tools. On-premises servers must be built, managed, and maintained, and many companies find this scenario to be inefficient, wasteful, and expensive. In turn, the business insights gleaned from the data in our warehouses help us optimize our operations, grow revenue, and improve our marketing strategies.ĭespite the benefits they promise to businesses, traditional data warehouses require massive investments of time, money, and expertise. traditional data warehousesĭata warehouses provide the storage and analytics capacity needed to drive business intelligence. In this article, we'll take a look at six features that set Redshift apart from other cloud data warehouses, and how these features can help you make the most of your Redshift data.īefore we get to that, let's consider what makes Redshift and conventional data warehouses such different animals. What you might not know is that Redshift differs from traditional data warehouses in several critical areas. Redshift also provides access to a variety of data analytics tools, compliance features, and even artificial intelligence and machine learning applications. Redshift Spectrum automatically scales query compute capacity based on the data being retrieved, so queries against Amazon S3 run fast, regardless of data set size.6 Redshift features that change the data warehouse gameĪmazon Redshift delivers lightning-fast performance and scalable data processing solutions without a massive investment in infrastructure. No loading or transformation is required, and you can use open data formats, including Avro, CSV, Grok, ORC, Parquet, RCFile, RegexSerDe, SequenceFile, TextFile, and TSV. With Amazon Redshift, you can start small with no commitments and scale out to petabytes of data, less than a tenth the cost of traditional solutions.Īmazon Redshift also includes Redshift Spectrum, allowing you to directly run SQL queries against exabytes of unstructured data in Amazon S3. It allows you to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution. Accelerate your time to insights with fast, easy, and secure cloud data warehousing at scale!Īmazon Redshift is a fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools.
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