Amazon RedShift

Amazon RedShift – Purpose, Features, Use cases, Redshift Cluster

Amazon Redshift is a fully managed data warehouse service in the cloud. Its datasets range from 100s of gigabytes to a petabyte. The initial process to create a data warehouse is to launch a set of compute resources called nodes, which are organized into groups called cluster.

Amazon Redshift is a managed data warehouse that allows you to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. Redshift uses query optimization, columnar storage, parallel execution, and high-performance disks to query petabytes of data in seconds. Redshift is valuable for companies that use SQL or existing BI tools and want to analyze large amounts of data with their existing tools.

Redshift’s column-oriented database is designed to connect to SQL-based clients and business intelligence tools, making data available to users in real time. Based on PostgreSQL 8, Redshift delivers fast performance and efficient querying that help teams make sound business analyses and decisions.

What is the purpose of Amazon RedShift?

Redshift is a columnar storage database, which is optimized for huge and repetitive type of data. Using columnar storage, reduces the I/O operations on disk drastically, improving performance as a result. Redshift gives you an option to define column-based encoding for data compression.

What is a AWS Redshift Cluster?

Each Amazon Redshift data warehouse contains a collection of computing resources (nodes) organized in a cluster. Each Redshift cluster runs its own Redshift engine and contains at least one database.

Is Amazon Redshift a Relational Database?

Redshift is Amazon’s analytics database and is designed to crunch large amounts of data as a data warehouse. Those interested in Redshift should know that it consists of clusters of databases with dense storage nodes, and allows you to even run traditional relational databases in the cloud.

Is AWS Redshift fully managed?

Redshift is a fully managed cloud data warehouse. It has the capacity to scale to petabytes, but lets you start with just a few gigabytes of data. Leveraging Redshift, you can use your data to acquire new business insights.

Features of Amazon Redshift

Following are the features of Amazon Redshift −

  1. Supports VPC − The users can launch Redshift within VPC and control access to the cluster through the virtual networking environment.
  2. Encryption − Data stored in Redshift can be encrypted and configured while creating tables in Redshift.
  3. SSL − SSL encryption is used to encrypt connections between clients and Redshift.
  4. Scalable − With a few simple clicks, the number of nodes can be easily scaled in your Redshift data warehouse as per requirement. It also allows to scale over storage capacity without any loss in performance.
  5. Cost-effective − Amazon Redshift is a cost-effective alternative to traditional data warehousing practices. There are no up-front costs, no long-term commitments and on-demand pricing structure.

5 Reasons Why Amazon Redshift Rocks as a Data Warehouse

When deciding how to choose a data warehouse, we consider Amazon Redshift the optimal choice for a wide range of businesses for a variety of reasons. In this post, we’ll cover five core areas about Amazon Redshift that should mark high on your checklist.

    1. Ease of Use It’s hard to overstate the ease of use with Redshift. Due to the similarities between Amazon Redshift’s relational database structure and the household SQL-based commands that can be operated upon it, administrators will find ramp-up and adoption are quickly facilitated though familiar best practices in SQL. Queries are distributed and parallelized to easily scale an Amazon Redshift data warehouse cluster up or down with a few clicks in the Amazon AWS Management Console or with a single API call. Amazon Redshift automates the common administrative tasks to help manage, monitor, and scale your data warehouse with push-button simplicity. This eliminates the undifferentiated heavy lifting commonly faced in managing a data warehouse and effectively liberates one to focus on the analytics and core business needs.
    2. Cost-efficiency We wrote an entire guide to Redshift’s pricing model, which we highly recommend you check out, but suffice to say that the cost of running Amazon Redshift has some clear advantages in the market. Compared to more traditional, legacy data warehouses, Amazon Redshift provides a blend of both entry-level affordability and massive cost-efficiency at scale. You can have an unlimited number of users doing unlimited analytics on all your data for just $1,000 per terabyte per year. Amazon Redshift’s columnar-based architecture for query optimization inherently reduces I/O load to return results in seconds and improve costs. With flexible pricing to run your Amazon Redshift cluster either as an on-demand instance or on reserved instances paid up-front with added discounts, organizations get the benefit of planning their spend in a holistic context with their overall analytics and business needs.
    3. Ease of Configuration and Management Overcoming price hurdles is one thing, but when it comes to setup, Amazon Redshift provides significant efficiency and performance gains to the daily DevOps workflow. Whether you’re new or a veteran on Amazon AWS, you’ll find that provisioning is remarkably simple as Amazon Redshift automatically handles many of the time-consuming aspects of managing your own data warehouse. Once schema and definitions are set, Amazon Redshift manages provisioning, configuration, and patching. Data durability and availability are assured as well via automatic replication and backup through Amazon S3. Scaling is simplified by simply adding or removing nodes with just a single API call or through the Amazon AWS management console.
    4. Synergy – Benefits of The Amazon AWS Ecosystem If you’re already using Amazon AWS, then there are considerable synergies with running Amazon Redshift adjacent to other services, ranging from speed and cost of deployment to scalability and innovation. For example, Amazon Redshift’s Spectrum application can be leveraged against services like S3 to run queries against exabytes of data and store highly structured, frequently accessed data on Amazon Redshift local disks, keep vast amounts of unstructured data in an Amazon S3 “data lake”, and query seamlessly across both.
    5. The Journey through Redshift Redshift custom integrations with JDBC and ODBC drivers make connections with SQL clients easy to apply critical services such as database replication for your BI needs. One such tool is FlyData. At FlyData we provide an intuitive, user-friendly means for near real-time data replication of data RDBMS endpoints to Amazon Redshift, from Amazon RDS, Amazon Aurora, PostgreSQL, MySQL and more. With proactive reporting and clear administration, FlyData empowers intelligent data migration plans for expansion and business development on Amazon Redshift, streamlining the definitions and schemas with which you’re accustomed in your RDBMS directly to Reshift. Additionally, FlyData’s partnership with Amazon AWS can serve as a logical extension of your overall data pipeline management and analytics strategy, helping to synchronize and replicate data to Amazon Redshift on the fly.

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